MedlinePlus??: The National Library of Medicine?? Brings Quality Information to Health Consumers

The National Library of Medicine???s (NLM??) MedlinePlus?? is a high-quality gateway to consumer health information from NLM, the National Institutes of Health (NIH), and other authoritative organizations. For decades, NLM has been a leader in indexing, organizing, and distributing health information to health professionals. In creating MedlinePlus, NLM uses years of accumulated expertise and technical knowledge to produce an authoritative, reliable consumer health Web site. This article describes the development of MedlinePlus???its quality control processes, the integration of NLM and NIH information, NLM???s relationship to other institutions, the technical and staffing infrastructures, the use of feedback for quality improvement, and future plans.published or submitted for publicatio

Spatiotemporal Statistical Downscaling for the Fusion of In-lake and Remote Sensing Data

This paper addresses the problem of fusing data from in-lake monitoring programmes with remote sensing data, through statistical downscaling. A Bayesian hierarchical model is developed, in order to fuse the in-lake and remote sensing data using spatially-varying coefficients. The model is applied to an example dataset of log(chlorophyll-a) data for Lake Erie, one of the Great Lakes of North America

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity

Recording brain activity can function as an implied social presence and alter neural connectivity.

People often behave differently when they know they are being watched. Here, we report the first investigation of whether such social presence effects also include brain monitoring technology, and also their impacts on the measured neural activity. We demonstrate that merely informing participants that fMRI has the potential to observe (thought-related) brain activity is sufficient to trigger changes in functional connectivity within and between relevant brain networks that have been previously associated selectively with executive and attentional control as well as self-relevant processing, social cognition, and theory of mind. These results demonstrate that an implied social presence, mediated here by recording brain activity with fMRI, can alter brain functional connectivity. These data provide a new manipulation of social attention, as well as shining light on a methodological hazard for researchers using equipment to monitor brain activity

Shotgun ion mobility mass spectrometry sequencing of heparan sulfate saccharides

Despite evident regulatory roles of heparan sulfate (HS) saccharides in numerous biological processes, definitive information on the bioactive sequences of these polymers is lacking, with only a handful of natural structures sequenced to date. Here, we develop a “Shotgun” Ion Mobility Mass Spectrometry Sequencing (SIMMS2) method in which intact HS saccharides are dissociated in an ion mobility mass spectrometer and collision cross section values of fragments measured. Matching of data for intact and fragment ions against known values for 36 fully defined HS saccharide structures (from di- to decasaccharides) permits unambiguous sequence determination of validated standards and unknown natural saccharides, notably including variants with 3O-sulfate groups. SIMMS2 analysis of two fibroblast growth factor-inhibiting hexasaccharides identified from a HS oligosaccharide library screen demonstrates that the approach allows elucidation of structure-activity relationships. SIMMS2 thus overcomes the bottleneck for decoding the informational content of functional HS motifs which is crucial for their future biomedical exploitation

'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems

An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP. To better differentiate the top performers, additional criteria are required. Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects. Therefore, `harder' test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking. To address the concerns mentioned above, we make two contributions. First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12. Second, we propose an object-part based benchmarking procedure which quantifies classifiers' robustness to a range of visibility and contextual settings. The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping. We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset. Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail. Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie

Moving Domain Computational Fluid Dynamics to Interface with an Embryonic Model of Cardiac Morphogenesis

Peristaltic contraction of the embryonic heart tube produces time- and spatial-varying wall shear stress (WSS) and pressure gradients (∇P) across the atrioventricular (AV) canal. Zebrafish (Danio rerio) are a genetically tractable system to investigate cardiac morphogenesis. The use of Tg(fli1a:EGFP)y1 transgenic embryos allowed for delineation and two-dimensional reconstruction of the endocardium. This time-varying wall motion was then prescribed in a two-dimensional moving domain computational fluid dynamics (CFD) model, providing new insights into spatial and temporal variations in WSS and ∇P during cardiac development. The CFD simulations were validated with particle image velocimetry (PIV) across the atrioventricular (AV) canal, revealing an increase in both velocities and heart rates, but a decrease in the duration of atrial systole from early to later stages. At 20-30 hours post fertilization (hpf), simulation results revealed bidirectional WSS across the AV canal in the heart tube in response to peristaltic motion of the wall. At 40-50 hpf, the tube structure undergoes cardiac looping, accompanied by a nearly 3-fold increase in WSS magnitude. At 110-120 hpf, distinct AV valve, atrium, ventricle, and bulbus arteriosus form, accompanied by incremental increases in both WSS magnitude and ∇P, but a decrease in bi-directional flow. Laminar flow develops across the AV canal at 20-30 hpf, and persists at 110-120 hpf. Reynolds numbers at the AV canal increase from 0.07±0.03 at 20-30 hpf to 0.23±0.07 at 110-120 hpf (p< 0.05, n=6), whereas Womersley numbers remain relatively unchanged from 0.11 to 0.13. Our moving domain simulations highlights hemodynamic changes in relation to cardiac morphogenesis; thereby, providing a 2-D quantitative approach to complement imaging analysis. © 2013 Lee et al