BackgroundSponges (Porifera) harbor distinct microbial consortia within their mesohyl interior. We herein analysed the hologenomes of Stylissa carteri and Xestospongia testudinaria, which notably differ in their microbiome content.ResultsOur analysis revealed that S. carteri has an expanded repertoire of immunological domains, specifically Scavenger Receptor Cysteine-Rich (SRCR)-like domains, compared to X. testudinaria. On the microbial side, metatranscriptome analyses revealed an overrepresentation of potential symbiosis-related domains in X. testudinaria.ConclusionsOur findings provide genomic insights into the molecular mechanisms underlying host-symbiont coevolution and may serve as a roadmap for future hologenome analyses.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2501-0) contains supplementary material, which is available to authorized users.

A combination of the sparse coding and transfer learning techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from different underlying distributions, i.e., belong to different domains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small number of them. In this paper, we explore such possibility and show how a small number of labeled data in the target domain can significantly leverage classification accuracy of the state-of-the-art transfer sparse coding methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

Background: Iron deficiency anemia (IDA) is a global health problem affecting the quality of life of more than 2 billion individuals. The current practice guidelines diagnose and monitor IDA via conventional hematological and iron biomarkers, which take several months before they are corrected under an iron-treatment plan. Reticulocyte hemoglobin equivalent (Ret-He) is used as a marker in most new hematology analyzers to assess iron incorporation into erythrocyte hemoglobin directly. This study aims to examine the efficacy of Ret-He as a marker for iron deficiency (ID) and IDA and investigate whether Ret-He is sensitive to iron therapy. Methods: Two blood samples were drawn from 182 participants for CBC and iron profile measurements. Follow-up samples were drawn from participants with a confirmed diagnosis of ID and/or IDA. Results: Ret-He levels were lower in the ID and IDA groups compared to the control (p < 0.0001), and lower in the IDA group compared to the ID group (p < 0.0001). Ret-He was correlated with ferritin at ID level (<30.0 mg/mL; r = 0.39) and severe IDA (<13.0 ng/mL; p-value < 0.01, r = 0.57). Cut-off values of <28.25 pg for ID and <21.55 pg for IDA showed a higher specificity and sensitivity (ID; AUC: 0.99, sensitivity: 92.73%, specificity: 97.87%) and (IDA; AUC: 0.94, sensitivity: 90.63%, specificity: 92.31%). Finally, Ret-He successfully reflected the iron therapy (p < 0.001) when compared to hemoglobin (Hb) (p = 0.1). Conclusions: Ret-He is a potential marker for detecting and diagnosing different stages of ID with high validity and is very sensitive in reflecting the iron incorporation in a short time.

MotivationGrowth phenotype profiling of genome-wide gene-deletion strains over stress conditions can offer a clear picture that the essentiality of genes depends on environmental conditions. Systematically identifying groups of genes from such high-throughput data that share similar patterns of conditional essentiality and dispensability under various environmental conditions can elucidate how genetic interactions of the growth phenotype are regulated in response to the environment.ResultsWe first demonstrate that detecting such ‘co-fit’ gene groups can be cast as a less well-studied problem in biclustering, i.e. constant-column biclustering. Despite significant advances in biclustering techniques, very few were designed for mining in growth phenotype data. Here, we propose Gracob, a novel, efficient graph-based method that casts and solves the constant-column biclustering problem as a maximal clique finding problem in a multipartite graph. We compared Gracob with a large collection of widely used biclustering methods that cover different types of algorithms designed to detect different types of biclusters. Gracob showed superior performance on finding co-fit genes over all the existing methods on both a variety of synthetic data sets with a wide range of settings, and three real growth phenotype datasets for E. coli, proteobacteria and yeast.Availability and ImplementationOur program is freely available for download at http://sfb.kaust.edu.sa/Pages/Software.aspx.Supplementary information Supplementary data are available at Bioinformatics online.

Predicting stuck pipe problems during oil and gas drilling operation is one of the most complex problems in the drilling business. The complexity of the problem is driven not only by the complexity of the natural factors, but it extends to the nature of the drilling operation itself. The drilling operation is continuously influenced by a dynamic smart system. The dynamic part of the system is impacted by natural forces like formation related characteristics, and also is impacted by human activities during the operation such as drilling, tripping and hole cleaning. The smartness of this system is driven by the fact that the operation is controlled by a number of experts, i.e. drilling engineers, trying to run the best sequence of operations using best operation parameters to achieve operation objective. At the top of that, the engineers can change their operation plan whenever they find it necessary to address any operational condition, including a potential stuck pipe problem. In this paper we prove the stuck pipe prediction problem is not a binary classification problem. Instead, we define the stuck pipe prediction problem as a multi-class problem which takes into consideration the dynamic nature of the drilling operation. A reinforcement learning based algorithm is proposed to solve the redefined problem, and its performance and evaluation results is shared in details. The accuracy of the developed algorithm in terms of detecting true stuck pipe events is shown. The results will compare the performance of different machine learning algorithms, which is then used to justify the selection of the best performing method. In addition, we show the accuracy performance improvement through time by employing the feedback channel to retrain the model. The presented method is using a reinforcement logic, in which the solution is connected to the operation reporting to label the solution prediction for false and true predictions. This information is then used to return the neural networks to learn new operational patterns to enhance accuracy.