Automated Open-Hardware Multiwell Imaging Station for Microorganisms Observation.

Bright field microscopes are particularly useful tools for biologists for cell and tissue observation, phenotyping, cell counting, and so on. Direct cell observation provides a wealth of information on cells' nature and physiological condition. Microscopic analyses are, however, time-consuming and usually not easy to parallelize.

Open-hardware wireless controller and 3D-printed pumps for efficient liquid manipulation.

Many routines in biological experiments require the precise handling of liquid volumes in the range of microliters up to liters. In this paper, we describe a new wireless controller that is adapted to liquid manipulation tasks, in particular when combined with the proposed 3D-printed pumps. It can be built from widely available electronic components and managed with open-source software.

Distinction of Lymphoma from Sarcoidosis on F-FDG PET/CT: Evaluation of Radiomics-Feature-Guided Machine Learning Versus Human Reader Performance.

Sarcoidosis and lymphoma often share common features on F-FDG PET/CT, such as intense hypermetabolic lesions in lymph nodes and multiple organs. We aimed at developing and validating radiomics signatures to differentiate sarcoidosis from Hodgkin lymphoma (HL) and diffuse large B-cell lymphoma (DLBCL). We retrospectively collected 420 patients (169 sarcoidosis, 140 HL, and 111 DLBCL) who underwent pretreatment F-FDG PET/CT at the University Hospital of Liege.

[F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation.

Purpose: To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[F] fluoro-2-deoxy-D-glucose ([F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC).

Methods: One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm.

Unsupervised GRN Ensemble.

Inferring gene regulatory networks from expression data is a very challenging problem that has raised the interest of the scientific community. Different algorithms have been proposed to try to solve this issue, but it has been shown that different methods have some particular biases and strengths, and none of them is the best across all types of data and datasets. As a result, the idea of aggregating various network inferences through a consensus mechanism naturally arises.

Combining semi-automated image analysis techniques with machine learning algorithms to accelerate large-scale genetic studies.

Genetic analyses of plant root systems require large datasets of extracted architectural traits. To quantify such traits from images of root systems, researchers often have to choose between automated tools (that are prone to error and extract only a limited number of architectural traits) or semi-automated ones (that are highly time consuming). We trained a Random Forest algorithm to infer architectural traits from automatically extracted image descriptors.

Study of Meta-analysis strategies for network inference using information-theoretic approaches.

Background: Reverse engineering of gene regulatory networks (GRNs) from gene expression data is a classical challenge in systems biology. Thanks to high-throughput technologies, a massive amount of gene-expression data has been accumulated in the public repositories. Modelling GRNs from multiple experiments (also called integrative analysis) has; therefore, naturally become a standard procedure in modern computational biology.

Using a Structural Root System Model to Evaluate and Improve the Accuracy of Root Image Analysis Pipelines.

Root system analysis is a complex task, often performed with fully automated image analysis pipelines. However, the outcome is rarely verified by ground-truth data, which might lead to underestimated biases. We have used a root model, ArchiSimple, to create a large and diverse library of ground-truth root system images (10,000).

NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference.

Background: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a systematic, yet fully reproducible, evaluation of transcriptional network inference methods.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features.

Total fuel-cycle analysis of heavy-duty vehicles using biofuels and natural gas-based alternative fuels.

Heavy-duty vehicles (HDVs) present a growing energy and environmental concern worldwide. These vehicles rely almost entirely on diesel fuel for propulsion and create problems associated with local pollution, climate change, and energy security. Given these problems and the expected global expansion of HDVs in transportation sectors, industry and governments are pursuing biofuels and natural gas as potential alternative fuels for HDVs.

Identification of functional elements and regulatory circuits by Drosophila modENCODE.

To gain insight into how genomic information is translated into cellular and developmental programs, the Drosophila model organism Encyclopedia of DNA Elements (modENCODE) project is comprehensively mapping transcripts, histone modifications, chromosomal proteins, transcription factors, replication proteins and intermediates, and nucleosome properties across a developmental time course and in multiple cell lines. We have generated more than 700 data sets and discovered protein-coding, noncoding, RNA regulatory, replication, and chromatin elements, more than tripling the annotated portion of the Drosophila genome. Correlated activity patterns of these elements reveal a functional regulatory network, which predicts putative new functions for genes, reveals stage- and tissue-specific regulators, and enables gene-expression prediction.

On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information.

The reverse engineering of transcription regulatory networks from expression data is gaining large interest in the bioinformatics community. An important family of inference techniques is represented by algorithms based on information theoretic measures which rely on the computation of pairwise mutual information. This paper aims to study the impact of the entropy estimator on the quality of the inferred networks.

minet: A R/Bioconductor package for inferring large transcriptional networks using mutual information.

Results: This paper presents the R/Bioconductor package minet (version 1.1.6) which provides a set of functions to infer mutual information networks from a dataset.

Information-theoretic inference of large transcriptional regulatory networks.

The paper presents MRNET, an original method for inferring genetic networks from microarray data. The method is based on maximum relevance/minimum redundancy (MRMR), an effective information-theoretic technique for feature selection in supervised learning. The MRMR principle consists in selecting among the least redundant variables the ones that have the highest mutual information with the target.

Energy use and emissions from marine vessels: a total fuel life cycle approach.

Regional and global air pollution from marine transportation is a growing concern. In discerning the sources of such pollution, researchers have become interested in tracking where along the total fuel life cycle these emissions occur. In addition, new efforts to introduce alternative fuels in marine vessels have raised questions about the energy use and environmental impacts of such fuels.