Extraction of chemical-induced diseases using prior knowledge and textual information

We describe our approach to the chemical-disease relation (CDR) task in the BioCreative V challenge. The CDR task consists of two subtasks: Automatic disease-named entity recognition and normalization (DNER), and extraction of chemical-induced diseases (CIDs) from Medline abstracts. For the DNER subtask, we used our concept recognition tool Peregrine, in combination with several optimization steps. For the CID subtask, our system, which we named RELigator, was trained on a rich feature set, comprising features derived from a graph database containing prior knowledge about chemicals and diseases, and linguistic and statistical features derived from the abstracts in the CDR training corpus. We describe the systems that were developed and present evaluation results for both subtasks on the CDR test set. For DNER, our Peregrine system reached an F-score of 0.757. For CID, the system achieved an F-score of 0.526, which ranked second among 18 participating teams. Several post-challenge modifications of the systems resulted in substantially improved F-scores (0.828 for DNER and 0.602 for CID)

Learning to segment when experts disagree

Recent years have seen an increasing use of supervised learning methods for segmentation tasks. However, the predictive performance of these algorithms depend on the quality of labels, especially in medical image domain, where both the annotation cost and inter-observer variability are high. In a typical annotation collection process, different clinical experts provide their estimates of the “true” segmentation labels under the influence of their levels of expertise and biases. Treating these noisy labels blindly as the ground truth can adversely affect the performance of supervised segmentation models. In this work, we present a neural network architecture for jointly learning, from noisy observations alone, both the reliability of individual annotators and the true segmentation label distributions. The separation of the annotators’ characteristics and true segmentation label is achieved by encouraging the estimated annotators to be maximally unreliable while achieving high fidelity with the training data. Our method can also be viewed as a translation of STAPLE, an established label aggregation framework proposed in Warfield et al. [1] to the supervised learning paradigm. We demonstrate first on a generic segmentation task using MNIST data and then adapt for usage with MRI scans of multiple sclerosis (MS) patients for lesion labelling. Our method shows considerable improvement over the relevant baselines on both datasets in terms of segmentation accuracy and estimation of annotator reliability, particularly when only a single label is available per image. An open-source implementation of our approach can be found at https://github.com/UCLBrain/MSLS

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study

Emerging cytokines in allergic airway inflammation: A genetic update

Purpose: We aim to discuss the current status of knowledge on the role of recently identified cytokines in airway hyper responsiveness as well as the genetic predisposition conferred by their coding genes to asthma. Methods: We focused on three cytokines and their coding genes,-IL-9, IL-17, and IL-22, and conducted a narrative review of all the relevant publications known to the authors. Results: A great body of evidence regarding the involvement of these three cytokines in asthma was discussed and interpreted. These range from studies on the murine models of asthma to clinical and human genetic approaches. Despite the large amounts of information existing on the genetics of IL-9 and IL-17, there is a lacking trend towards the IL-22 genetic studies in asthma. Conclusion: The emergence of new classes of T-helper effector cells and their cytokines has led to a change in our understanding of asthma pathogenesis. This has created both new opportunities and challenges for researchers involved in this field, and is likely to result in improvements and progress in identifying and developing novel therapeutic measures and innovative treatments for asthma. © 2016 Bentham Science Publishers

Gravity-driven membrane filtration as pretreatment for seawater reverse osmosis: Linking biofouling layer morphology with flux stabilization

© 2014 Elsevier Ltd. In this study gravity-driven membrane (GDM) ultrafiltration is investigated for the pretreatment of seawater before reverse osmosis (RO). The impacts of temperature (21±1 and 29±1°C) and hydrostatic pressure (40 and 100mbar) on dynamic flux development and biofouling layer structure were studied. The data suggested pore constriction fouling was predominant at the early stage of filtration, during which the hydrostatic pressure and temperature had negligible effects on permeate flux. With extended filtration time, cake layer fouling played a major role, during which higher hydrostatic pressure and temperature improved permeate flux. The permeate flux stabilized in a range of 3.6L/m2h (21±1°C, 40mbar) to 7.3L/m2h (29±1°C, 100mbar) after slight fluctuations and remained constant for the duration of the experiments (almost 3 months). An increase in biofouling layer thickness and a variable biofouling layer structure were observed over time by optical coherence tomography and confocal laser scanning microscopy. The presence of eukaryotic organisms in the biofouling layer was observed by light microscopy and the microbial community structure of the biofouling layer was analyzed by sequences of 16S rRNA genes. The magnitude of permeate flux was associated with the combined effect of the biofouling layer thickness and structure. Changes in the biofouling layer structure were attributed to (1) the movement and predation behaviour of the eukaryotic organisms which increased the heterogeneous nature of the biofouling layer; (2) the bacterial debris generated by eukaryotic predation activity which reduced porosity; (3) significant shifts of the dominant bacterial species over time that may have influenced the biofouling layer structure. As expected, most of the particles and colloids in the feed seawater were removed by the GDM process, which led to a lower RO fouling potential. However, the dissolved organic carbon in the permeate was not be reduced, possibly because some microbial species (e.g. algae) could convert CO2 into organic substances. To further improve the removal efficiency of the organic carbon, combining carrier biofilm processes with a submerged GDM filtration system is proposed

Changes in global nutrition practices in critically ill children and the impact of emerging evidence: a secondary analysis of the Pediatric International Nutrition Studies (PINS), 2009-2018

Background: The timeline of the three Pediatric International Nutrition Studies (PINS) coincided with the publication of 2 major guidelines for the timing of parenteral nutrition (PN) and recommended energy and protein delivery dose. Objective: The study's main objective was to describe changes in the nutrition delivery practice recorded in PINS 1 and 2 (conducted in 2009 and 2011, pre-exposure epoch) versus PINS 3 (conducted in 2018,post-exposure epoch), in relation to the published practice guidelines. Design: This study is a secondary analysis of data from a multi-center prospective cohort study. Participants: /setting. Data from 3650 participants, aged 1 month to 18 years, admitted to 100 unique hospitals that participated in three PINS was used for this study. Main outcome measures: The time in days from PICU admission to the initiation of PN and enteral nutrition (EN) delivery were the primary outcomes. Prescribed energy and protein goals were the secondary outcomes. Statistical analyses performed: A frailty model with a random intercept per hospital with stratified baseline hazard function by region for the primary outcomes and a mixed-effects negative binomial regression with random intercept per hospital for the secondary outcomes. Results: The proportion of patients receiving EN (88.3% vs. 80.6%, p-value<0.001) was higher, and those receiving PN (20.6% vs. 28.8%, p-value<0.001) was lower in the PINS3 cohort compared to PINS1-2. In the PINS3 cohort, the odds of initiating PN during the 1st 10 days of PICU admission were lower, compared to the PINS1-2 cohort (HR=0.8, CI=[0.67-0.95], p-value=0.013); and prescribed energy goal was lower compared to the PINS1-2 cohort (IRR=0.918, CI=[0.874-0.965], p=0.001). Conclusions: The likelihood of initiation of PN delivery significantly decreased in the first ten days post-admission in the PINS3 cohort compared to PINS1-2. Energy goal prescription in mechanically ventilated children significantly decreased in the post-guidelines epoch compared to the pre-guidelines epoch