The Apparent Diffusion Coefficient (ADC) is considered an importantimaging biomarker contributing to the assessment of tissue microstructure and pathophy- siology. It is calculated from Diffusion-Weighted Magnetic Resonance Imaging (DWI) by means of a diffusion model, usually without considering any motion during image acquisition. We propose a method to improve the computation of the ADC by coping jointly with both motion artifacts in whole-body DWI (through group-wise registration) and possible instrumental noise in the diffusion model. The proposed deformable registration method yielded on average the lowest ADC reconstruction error on data with simulated motion and diffusion. Moreover, our approach was applied on whole-body diffusion weighted images obtained with five different b-values from a cohort of 38 patients with histologically confirmed lymphomas of three different types (Hodgkin, diffuse large B-cell lymphoma and follicular lymphoma). Evaluation on the real data showed that ADC-based features, extracted using our joint optimization approach classified lymphomas with an accuracy of approximately 78.6% (yielding a 11% increase in respect to the standard features extracted from unregistered diffusion-weighted images). Furthermore, the correlation between diffusion characteristics and histopathological findings was higher than any other previous approach of ADC computation.

Methods for the analysis of time series single cell expression data (scRNA-Seq) either do not utilize information about transcription factors (TFs) and their targets or only study these as a post-processing step. Using such information can both, improve the accuracy of the reconstructed model and cell assignments, while at the same time provide information on how and when the process is regulated. We developed the Continuous-State Hidden Markov Models TF (CSHMM-TF) method which integrates probabilistic modeling of scRNA-Seq data with the ability to assign TFs to specific activation points in the model. TFs are assumed to influence the emission probabilities for cells assigned to later time points allowing us to identify not just the TFs controlling each path but also their order of activation. We tested CSHMM-TF on several mouse and human datasets. As we show, the method was able to identify known and novel TFs for all processes, assigned time of activation agrees with both expression information and prior knowledge and combinatorial predictions are supported by known interactions. We also show that CSHMM-TF improves upon prior methods that do not utilize TF-gene interaction.

MOTIVATION: Methods for reconstructing developmental trajectories from time-series single-cell RNA-Seq (scRNA-Seq) data can be largely divided into two categories. The first, often referred to as pseudotime ordering methods are deterministic and rely on dimensionality reduction followed by an ordering step. The second learns a probabilistic branching model to represent the developmental process. While both types have been successful, each suffers from shortcomings that can impact their accuracy. RESULTS: We developed a new method based on continuous-state HMMs (CSHMMs) for representing and modeling time-series scRNA-Seq data. We define the CSHMM model and provide efficient learning and inference algorithms which allow the method to determine both the structure of the branching process and the assignment of cells to these branches. Analyzing several developmental single-cell datasets, we show that the CSHMM method accurately infers branching topology and correctly and continuously assign cells to paths, improving upon prior methods proposed for this task. Analysis of genes based on the continuous cell assignment identifies known and novel markers for different cell types. AVAILABILITY AND IMPLEMENTATION: Software and Supporting website: www.andrew.cmu.edu/user/chiehl1/CSHMM/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Intensity variations can often be described by a physiological or temporal model applied on a voxel-wise basis across a group of images. However the voxel correspondence might be unknown, imposing the need for a group-wise deformable registration coupled with the computation of the model parameters. In this paper we propose a group-wise registration method of medical images that incorporates the temporal dimension (reflecting the change of signal amplitude) of the acquisition process. Consistency on the spatiotemporal physiological model, as well as deformation smoothness, is imposed in order to produce anatomically meaningful representations of the 3D images. The performance of the proposed method is compared to two different group-wise registration approaches; one that penalizes the absolute differences in the intensities and one that penalizes the intensity range among the images on corresponding regions. We chose as an application paradigm the registration of diffusion-weighted magnetic resonance (DW-MR) images for the evaluation of patients with lymphomas. A dataset consisting of 25 patients, each scanned with 3 “b values”, was used to evaluate the method's accuracy. The proposed registration method outperfomed the other two registration approaches, making it a very promising method for highlighting the importance of DWI as an imaging biomarker.

UNLABELLED: Cytochrome P450 (CYPs) are the major enzymes involved in drug metabolism and bioactivation. Inhibition models were constructed for five of the most popular enzymes from the CYP superfamily in human liver. The five enzymes chosen for this study, namely CYP1A2, CYP2D6, CYP2C19, CYP2C9 and CYP3A4, account for 90% of the xenobiotic and drug metabolism in human body. CYP enzymes can be inhibited or induced by various drugs or chemical compounds. In this work, a rule-based CYP inhibition prediction online server, CypRules, was created based on predictive models generated by the rule-based C5.0 algorithm. CypRules can predict and provide structural rulesets for CYP inhibition for each compound uploaded to the server. Capable of fast execution performance, it can be used for virtual high-throughput screening (VHTS) of a large set of testing compounds. AVAILABILITY AND IMPLEMENTATION: CypRules is freely accessible at http://cyprules.cmdm.tw/ and models, descriptor and program files for all compounds are publically available at http://cyprules.cmdm.tw/sources/sources.rar.

Article Free Access Share on A new efficient method for substrate-aware device-level placement (short paper) Authors: C. Lin Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5600 MB Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 5600 MBView Profile , D. M. W. Leenaerts Philips Research Labs, Prof. Holstlaan 4, Eindhoven, 5656 AA Philips Research Labs, Prof. Holstlaan 4, Eindhoven, 5656 AAView Profile Authors Info & Claims ASP-DAC '00: Proceedings of the 2000 Asia and South Pacific Design Automation ConferenceJanuary 2000 Pages 533–536https://doi.org/10.1145/368434.368781Online:28 January 2000Publication History 6citation88DownloadsMetricsTotal Citations6Total Downloads88Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper introduces a new sequence pair algorithm which has a complexity lower than O(M/sup 1.25/), which is a significant improvement compared to the original O(M/sup 2/) algorithm. Furthermore, the new algorithm has complexity close to the theoretical lower bound. Experimental results, obtained with a straightforward implementation, confirm this improvement in complexity.