Creating a new Ontology: a Modular Approach

Creating a new Ontology: a Modular ApproachComment: in Adrian Paschke, Albert Burger, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 201

Construction of correlation networks with explicit time-slices using time-lagged, variable interval standard and partial correlation coefficients

The construction of gene regulatory models from microarray time-series data has received much attention. Here we propose a method that extends standard correlation networks to incorporate explicit time-slices. The method is applied to a time-series dataset of a study on gene expression in the developmental phase of zebrafish. Results show that the method is able to distinguish real relations between genes from the data. These relations are explicitly placed in time, allowing for a better understanding of gene regulation. The method and data normalisation procedure have been implemented using the R statistical language and are available from http://zebrafish.liacs.nl/supplements.html

Specificity Enhancement in microRNA Target Prediction through Knowledge Discovery

Computer Systems, Imagery and Medi

Frequent Episode Mining to support Pattern Analysis in Developmental Biology

Computer Systems, Imagery and Medi

Segmentation of NKX2.5 Signal in Human Pluripotent Stem Cell-Derived Cardiomyocytes

Human pluripotent stem cell-derived Cardiomyocytes (hPSC-CMs) become increasingly popular in recent years for disease modeling and drug screening. NKX2.5 gene is a key transcription factor that regulates cardiomyocyte differentiation. A human embryonic stem cell (hESC) reporter line with NKX2.5 in GFP signal allows us to monitor the specificity and efficiency of human cardiac differentiation. We intend to develop an automatic analysis pipeline for the NKX2.5 signal. However, the NKX2.5 signal captured from fluorescence microscopy is highly heterogeneous. It is not possible to be properly segmented using traditional thresholding methods. Therefore, in this paper, one machine learning method: enhanced Fuzzy C-Means clustering (EnFCM) and two deep learning models: U-Net and DeepLabV3+, are evaluated on the segmentation performance. Parameters have been tuned for each method so as to reach to the optimal segmentation performance. The results show that EnFCM reaches the performance of 0.85. U-Net and DeepLabV3+ have a superior performance. Their performances are 0.86 and 0.89 respectively.</p

Advances in Kidney Biopsy Lesion Assessment through Dense Instance Segmentation

Renal biopsies are the gold standard for diagnosis of kidney diseases. Lesion scores made by renal pathologists are semi-quantitative and exhibit high inter-observer variability. Automating lesion classification within segmented anatomical structures can provide decision support in quantification analysis and reduce the inter-observer variability. Nevertheless, classifying lesions in regions-of-interest (ROIs) is clinically challenging due to (a) a large amount of densely packed anatomical objects (up to 1000), (b) class imbalance across different compartments (at least 3), (c) significant variation in object scales (i.e. sizes and shapes), and (d) the presence of multi-label lesions per anatomical structure. Existing models lack the capacity to address these complexities efficiently and generically. This paper presents \textbf{a generalized technical solution} for large-scale, multi-source datasets with diverse lesions. Our approach utilizes two sub-networks: dense instance segmentation and lesion classification. We introduce \textbf{DiffRegFormer}, an end-to-end dense instance segmentation model designed for multi-class, multi-scale objects within ROIs. Combining diffusion models, transformers, and RCNNs, DiffRegFormer efficiently recognizes over 500 objects across three anatomical classes (glomeruli, tubuli, arteries) within ROIs on a single NVIDIA GeForce RTX 3090 GPU. On a dataset of 303 ROIs (from 148 Jones' silver-stained renal WSIs), it outperforms state of art models, achieving AP of 52.1\% (detection) and 46.8\% (segmentation). Our lesion classification sub-network achieves 89.2\% precision and 64.6\% recall on 21889 object patches (from the 303 ROIs). Importantly, the model demonstrates direct domain transfer to PAS-stained WSIs without fine-tuning.Comment: 16 pages, 15 figures, 6 tables, Journa

Lentivirus-mediated transgene delivery to the hippocampus reveals sub-field specific differences in expression

<p>Abstract</p> <p>Background</p> <p>In the adult hippocampus, the granule cell layer of the dentate gyrus is a heterogeneous structure formed by neurons of different ages, morphologies and electrophysiological properties. Retroviral vectors have been extensively used to transduce cells of the granule cell layer and study their inherent properties in an intact brain environment. In addition, lentivirus-based vectors have been used to deliver transgenes to replicative and non-replicative cells as well, such as post mitotic neurons of the CNS. However, only few studies have been dedicated to address the applicability of these widespread used vectors to hippocampal cells in vivo. Therefore, the aim of this study was to extensively characterize the cell types that are effectively transduced in vivo by VSVg-pseudotyped lentivirus-based vectors in the hippocampus dentate gyrus.</p> <p>Results</p> <p>In the present study we used Vesicular Stomatitis Virus G glycoprotein-pseudotyped lentivirual vectors to express EGFP from three different promoters in the mouse hippocampus. In contrast to lentiviral transduction of pyramidal cells in CA1, we identified sub-region specific differences in transgene expression in the granule cell layer of the dentate gyrus. Furthermore, we characterized the cell types transduced by these lentiviral vectors, showing that they target primarily neuronal progenitor cells and immature neurons present in the sub-granular zone and more immature layers of the granule cell layer.</p> <p>Conclusion</p> <p>Our observations suggest the existence of intrinsic differences in the permissiveness to lentiviral transduction among various hippocampal cell types. In particular, we show for the first time that mature neurons of the granule cell layer do not express lentivirus-delivered transgenes, despite successful expression in other hippocampal cell types. Therefore, amongst hippocampal granule cells, only adult-generated neurons are target for lentivirus-mediated transgene delivery. These properties make lentiviral vectors excellent systems for overexpression or knockdown of genes in neuronal progenitor cells, immature neurons and adult-generated neurons of the mouse hippocampus in vivo.</p

Fungal metabarcoding data integration framework for the MycoDiversity DataBase (MDDB)

Fungi have crucial roles in ecosystems, and are important associates for many organisms. They are adapted to a wide variety of habitats, however their global distribution and diversity remains poorly documented. The exponential growth of DNA barcode information retrieved from the environment is assisting considerably the traditional ways for unraveling fungal diversity and detection. The raw DNA data in association to environmental descriptors of metabarcoding studies are made available in public sequence read archives. While this is potentially a valuable source of information for the investigation of Fungi across diverse environmental conditions, the annotation used to describe environment is heterogenous. Moreover, a uniform processing pipeline still needs to be applied to the available raw DNA data. Hence, a comprehensive framework to analyses these data in a large context is still lacking. We introduce the MycoDiversity DataBase, a database which includes public fungal metabarcoding data of environmental samples for the study of biodiversity patterns of Fungi. The framework we propose will contribute to our understanding of fungal biodiversity and aims to become a valuable source for large-scale analyses of patterns in space and time, in addition to assisting evolutionary and ecological research on Fungi