Treatment of fibrolamellar hepatoma with partial or total hepatectomy and transplantation of the liver

Fourteen patients with fibrolamellar hepatoma were treated with radical excision. In eight, a subtotal hepatic resection was performed from 16 months to more than 16 years ago. None of the patients have died and recurrences have been seen in only one patient. Six other patients had total hepatectomy and hepatic replacement. Two of these six patients have died of metastases and a third is living with recurrent tumor. This experience has justified the continuing use of quite aggressive extirpative procedures for the treatment of fibrolamellar hepatoma

A BAYESIAN ANALYSIS OF THE AGES OF FOUR OPEN CLUSTERS

In this paper we apply a Bayesian technique to determine the best fit of stellar evolution models to find the main sequence turn off age and other cluster parameters of four intermediate-age open clusters: NGC 2360, NGC 2477, NGC 2660, and NGC 3960. Our algorithm utilizes a Markov chain Monte Carlo technique to fit these various parameters, objectively finding the best fit isochrone for each cluster. The result is a high precision isochrone fit. We compare these results with the those of traditional “by eye” isochrone fitting methods. By applying this Bayesian technique to NGC 2360, NGC 2477, NGC 2660, and NGC 3960 we determine the ages of these clusters to be 1.35 ± 0.05, 1.02 ± 0.02, 1.64 ± 0.04, and 0.860 ± 0.04 Gyr, respectively. The results of this paper continue our effort to determine cluster ages to higher precision than that offered by these traditional methods of isochrone fitting

Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks

Diffusion magnetic resonance imaging (diffusion MRI) is a non-invasive microstructure assessment technique. Scalar measures, such as FA (fractional anisotropy) and MD (mean diffusivity), quantifying micro-structural tissue properties can be obtained using diffusion models and data processing pipelines. However, it is costly and time consuming to collect high quality diffusion data. Here, we therefore demonstrate how Generative Adversarial Networks (GANs) can be used to generate synthetic diffusion scalar measures from structural T1-weighted images in a single optimized step. Specifically, we train the popular CycleGAN model to learn to map a T1 image to FA or MD, and vice versa. As an application, we show that synthetic FA images can be used as a target for non-linear registration, to correct for geometric distortions common in diffusion MRI

A method for sensitivity analysis to assess the effects of measurement error in multiple exposure variables using external validation data

Measurement error in self-reported dietary intakes is known to bias the association between dietary intake and a health outcome of interest such as risk of a disease. The association can be distorted further by mismeasured confounders, leading to invalid results and conclusions. It is, however, difficult to adjust for the bias in the association when there is no internal validation data

Formalizing Size-Optimal Sorting Networks: Extracting a Certified Proof Checker

Since the proof of the four color theorem in 1976, computer-generated proofs have become a reality in mathematics and computer science. During the last decade, we have seen formal proofs using verified proof assistants being used to verify the validity of such proofs. In this paper, we describe a formalized theory of size-optimal sorting networks. From this formalization we extract a certified checker that successfully verifies computer-generated proofs of optimality on up to 8 inputs. The checker relies on an untrusted oracle to shortcut the search for witnesses on more than 1.6 million NP-complete subproblems.Comment: IMADA-preprint-c

Efficient HTTP based I/O on very large datasets for high performance computing with the libdavix library

Remote data access for data analysis in high performance computing is commonly done with specialized data access protocols and storage systems. These protocols are highly optimized for high throughput on very large datasets, multi-streams, high availability, low latency and efficient parallel I/O. The purpose of this paper is to describe how we have adapted a generic protocol, the Hyper Text Transport Protocol (HTTP) to make it a competitive alternative for high performance I/O and data analysis applications in a global computing grid: the Worldwide LHC Computing Grid. In this work, we first analyze the design differences between the HTTP protocol and the most common high performance I/O protocols, pointing out the main performance weaknesses of HTTP. Then, we describe in detail how we solved these issues. Our solutions have been implemented in a toolkit called davix, available through several recent Linux distributions. Finally, we describe the results of our benchmarks where we compare the performance of davix against a HPC specific protocol for a data analysis use case.Comment: Presented at: Very large Data Bases (VLDB) 2014, Hangzho

Effects of Argonaute on gene expression in Thermus thermophilus.

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Eukaryotic Argonaute proteins mediate RNA-guided RNA interference, allowing both regulation of host gene expression and defense against invading mobile genetic elements. Recently, it has become evident that prokaryotic Argonaute homologs mediate DNA-guided DNA interference, and play a role in host defense. Argonaute of the bacterium Thermus thermophilus (TtAgo) targets invading plasmid DNA during and after transformation. Using small interfering DNA guides, TtAgo can cleave single and double stranded DNAs. Although TtAgo additionally has been demonstrated to cleave RNA targets complementary to its DNA guide in vitro, RNA targeting by TtAgo has not been demonstrated in vivo. METHODS: To investigate if TtAgo also has the potential to control RNA levels, we analyzed RNA-seq data derived from cultures of four T. thermophilus strain HB27 variants: wild type, TtAgo knockout (Δago), and either strain transformed with a plasmid. Additionally we determined the effect of TtAgo on expression of plasmid-encoded RNA and plasmid DNA levels. RESULTS: In the absence of exogenous DNA (plasmid), TtAgo presence or absence had no effect on gene expression levels. When plasmid DNA is present, TtAgo reduces plasmid DNA levels 4-fold, and a corresponding reduction of plasmid gene transcript levels was observed. We therefore conclude that TtAgo interferes with plasmid DNA, but not with plasmid-encoded RNA. Interestingly, TtAgo presence stimulates expression of specific endogenous genes, but only when exogenous plasmid DNA was present. Specifically, the presence of TtAgo directly or indirectly stimulates expression of CRISPR loci and associated genes, some of which are involved in CRISPR adaptation. This suggests that TtAgo-mediated interference with plasmid DNA stimulates CRISPR adaptation.Funding: This study was financially supported by a TOP grant from the Netherlands Organisation for Scientific Research (NWO) to John van der Oost (NWO-TOP 854.10.003). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Glucose enhancement of memory is modulated by trait anxiety in healthy adolescent males

Glucose administration is associated with memory enhancement in healthy young individuals under conditions of divided attention at encoding. While the specific neurocognitive mechanisms underlying this ‘glucose memory facilitation effect’ are currently uncertain, it is thought that individual differences in glucoregulatory efficiency may alter an individual’s sensitivity to the glucose memory facilitation effect. In the present study, we sought to investigate whether basal hypothalamic–pituitary–adrenal axis function (itself a modulator of glucoregulatory efficiency), baseline self-reported stress and trait anxiety influence the glucose memory facilitation effect. Adolescent males (age range = 14–17 years) were administered glucose and placebo prior to completing a verbal episodic memory task on two separate testing days in a counter-balanced, within-subjects design. Glucose ingestion improved verbal episodic memory performance when memory recall was tested (i) within an hour of glucose ingestion and encoding, and (ii) one week subsequent to glucose ingestion and encoding. Basal hypothalamic–pituitary–adrenal axis function did not appear to influence the glucose memory facilitation effect; however, glucose ingestion only improved memory in participants reporting relatively higher trait anxiety. These findings suggest that the glucose memory facilitation effect may be mediated by biological mechanisms associated with trait anxiety

Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Initial steps towards a production platform for DNA sequence analysis on the grid

ABSTRACT: BACKGROUND: Bioinformatics is confronted with a new data explosion due to the availability of high throughput DNA sequencers. Data storage and analysis becomes a problem on local servers, and therefore it is needed to switch to other IT infrastructures. Grid and workflow technology can help to handle the data more efficiently, as well as facilitate collaborations. However, interfaces to grids are often unfriendly to novice users. RESULTS: In this study we reused a platform that was developed in the VL-e project for the analysis of medical images. Data transfer, workflow execution and job monitoring are operated from one graphical interface. We developed workflows for two sequence alignment tools (BLAST and BLAT) as a proof of concept. The analysis time was signicantly reduced. All workflows and executables are available for the members of the Dutch Life Science Grid and the VL-e Medical virtual organizations. All components are open source and can be transported to other grid infrastructures. CONCLUSIONS: The availability of in-house expertise and tools facilitates the usage of grid resources by new users. Our first results indicate that this is a practical, powerful and scalable solution to address the capacity and collaboration issues raised by the deployment of next generation sequencers. We currently adopt this methodology on a daily basis for DNA sequencing and other applications. More information and source code is available via http://www.bioinformaticslaboratory.nl