bluuebunny/crossref_metadata_2025 huggingface dataset:
Electrokinetic ion transport in nanofluidics and membranes with applications in bioanalysis and beyond
Authors: Cheng Li-Jing
Published: April 2018

Electrokinetic transport of ions between electrolyte solutions and ion permselective solid media governs a variety of applications, such as molecular separation, biological detection, and bioelectronics. These applications rely on a unique class of materials and devices to interface the ionic and electronic systems. The devices built on ion permselective materials or micro-/nanofluidic channels are arranged to work with aqueous environments capable of either manipulating charged species through applied electric fields or transducing biological responses into electronic signals. In this review, we focus on recent advances in the application of electrokinetic ion transport using nanofluidic and membrane technologies. We start with an introduction into the theoretical basis of ion transport kinetics and their analogy to the charge transport in electronic systems. We continue with discussions of the materials and nanofabrication technologies developed to create ion permselective membranes and nanofluidic devices. Accomplishments from various applications are highlighted, including biosensing, molecular separation, energy conversion, and bio-electronic interfaces. We also briefly outline potential applications and challenges in this field.
Giant plasmonic mode splitting in THz metamaterials mediated by coupling with Lorentz phonon mode
Authors: Yu Leilei , Huang Yuanyuan , Liu Changji , Hu Fangrong , Jin Yanping , Yan Yi , Xu Xinlong
Published: April 2018

Giant plasmonic mode splitting has been observed in THz metamaterials due to the mediation by the Lorentz phonon dielectric material. This splitting mode is confirmed by the surface current distribution, indicating that plasmonic modes behave like dipole resonances, while the phonon mode behaves like multipole resonance due to coupling. The splitting of the plasmonic modes demonstrates an anti-crossing behavior with the change in Lorentz central frequency, which suggests that there is energy redistribution between plasmon and phonon modes. Similar to the Stark effect, the splitting frequency difference increases with the increasing direct current dielectric function. We also propose an interaction Hamiltonian to understand the physical mechanism of the plasmonic splitting. Furthermore, the splitting is convincible for small Lorentz dielectrics such as sugar and amino acid in the THz region, which could be used for biomolecular sensing applications.
Exciton diffusion in WSe2 monolayers embedded in a van der Waals heterostructure
Authors: Cadiz F. , Robert C. , Courtade E. , Manca M. , Martinelli L. , Taniguchi T. , Watanabe K. , Amand T. , Rowe A. C. H. , Paget D. , Urbaszek B. , Marie X.
Published: April 2018

We have combined spatially resolved steady-state micro-photoluminescence with time-resolved photoluminescence to investigate the exciton diffusion in a WSe2 monolayer encapsulated with hexagonal boron nitride. At 300 K, we extract an exciton diffusion length of LX = 0.36 ± 0.02 μm and an exciton diffusion coefficient of DX = 14.5 ± 2 cm2/s. This represents a nearly 10-fold increase in the effective mobility of excitons with respect to several previously reported values on nonencapsulated samples. At cryogenic temperatures, the high optical quality of these samples has allowed us to discriminate the diffusion of the different exciton species: bright and dark neutral excitons, as well as charged excitons. The longer lifetime of dark neutral excitons yields a larger diffusion length of LXD=1.5±0.02 μm.
Collisionless coupling of a high-β expansion to an ambient, magnetized plasma. II. Experimental fields and measured momentum coupling
Authors: Bonde Jeffrey , Vincena Stephen , Gekelman Walter
Published: April 2018

The momentum coupled to a magnetized, ambient argon plasma from a high-β, laser-produced carbon plasma is examined in a collisionless, weakly coupled limit. The total electric field was measured by separately examining the induced component associated with the rapidly changing magnetic field of the high-β (kinetic β∼106), expanding plasma and the electrostatic component due to polarization of the expansion. Their temporal and spatial structures are discussed and their effect on the ambient argon plasma (thermal β∼10−2) is confirmed with a laser-induced fluorescence diagnostic, which directly probed the argon ion velocity distribution function. For the given experimental conditions, the electrostatic field is shown to dominate the interaction between the high-β expansion and the ambient plasma. Specifically, the expanding plasma couples energy and momentum into the ambient plasma by pulling ions inward against the flow direction.
Forage management and concentrate supplementation effects on performance of beef calves
Authors: Vendramini Joao M. B. , Moriel Philipe
Published: April 2018

Proper grazing management is the most effective practice to provide adequate forage quantity and quality to cow–calf systems, and optimise beef-calf performance in tropical and subtropical regions. Supplementation of beef calves is not a commonly used management practice in beef-cattle production, primarily because calves can benefit from the cow’s milk production to offset some limitations in forage quantity and quality. Creep-feeding and supplementation of early weaned calves are the two main strategies to supplement beef calves. Creep-feeding can be used to overcome limited herbage allowance, improve calves uniformity, supply extra nutrients for calves, provide adaptation to concentrate diets before weaning, and increase weaning weight. Early weaning is an effective management practice to increase the likelihood of re-breeding of first-calf beef heifers in the south-eastern USA; however, it was observed that concentrate supplementation is necessary for early weaned calves to achieve desirable levels of gain on pasture in tropical and subtropical regions. In addition, concentrate supplementation during early stages of a calf life may permanently change calf development and performance in a process called ‘metabolic imprinting’. The metabolic imprinting concept in production agriculture is in the early stages of knowledge and further research should elucidate the benefits of this management practice in beef-cattle production.
Effect of tree density on competition between Leucaena leucocephala and Chloris gayana using a Nelder Wheel trial. I. Aboveground interactions
Authors: Pachas A. Nahuel A. , Shelton H. Max , Lambrides Christopher J. , Dalzell Scott A. , Murtagh G. John
Published: April 2018

Silvopastoral systems with the tree legume leucaena (Leucaena leucocephala (Lam.) de Wit) and grass pastures are widely used for ruminant feeding in subtropical and tropical regions. Different densities and planting configurations of leucaena will influence relative yields of both species because of intra- and interspecific competition. With the aim to describe the effects of competition between leucaena and Rhodes grass (Chloris gayana Kunth), a Nelder Wheel trial with 10 different leucaena tree densities (100–80 000 trees ha–1) growing with and without Rhodes grass was established in a subtropical environment at Gatton, south-east Queensland, in November 2013. From 2014 to 2016, the biomass of leucaena (six harvests) and Rhodes grass (seven harvests) was measured by using allometric equations and the BOTANAL sampling procedure over 742 and 721 days, respectively. No complementary or facilitative aboveground interactions were observed between the leucaena and Rhodes grass components of the pasture system. Increasing leucaena tree density resulted in greater aboveground intra- and interspecific competition.
Average maximum individual tree yield (38.9 kg DM tree–1 year–1) was reached at 100 trees ha–1 without grass competition and was reduced by 60% with grass competition. Rhodes grass biomass yield was negatively affected by shading from the leucaena canopy, with negligible grass yield at tree densities ≥8618 trees ha–1. Therefore, there was effectively no grass competition on individual tree yield at higher leucaena densities. Accordingly, edible leucaena biomass per unit area was positively related to log10 leucaena density (R2 = 0.99) regardless of grass competition, reaching 21.7 t DM ha–1 year–1 (2014–15) and 27 t DM ha–1 year–1 (2015–16) at the highest leucaena density of 80 000 trees ha–1. By contrast, the yield of Rhodes grass was linearly and inversely correlated with log10 tree density (R2 = 0.99). Practical implications for the design and management of commercial leucaena–grass pastures are discussed.
Waking sleeping beauties: a molecular phylogeny and nomenclator of Halgania (Ehretiaceae, Boraginales)
Authors: Holstein Norbert , Gottschling Marc
Published: April 2018

Halgania (Ehretiaceae, Boraginales) comprises ~20 species of ericoid shrubs endemic to Australia. The current taxonomic concepts based on morphology are confusing and are sometimes based on misidentification or lack of information about the type material. We describe the morphological diversity and infer relationships using molecular phylogenetics. The five petals in all Halgania species are blue to violet (rarely white). The yellow or yellow-purple anthers are connected into a cone surrounding the single style. Differences among the species are mainly found in the indumentum of the plants, the leaf margin (i.e. degree of serration), the sepals (being either of equal or of unequal size) and the length of the beak-like anther appendages. A phylogenetic tree mainly based on ITS sequences retrieves three highly supported groups. The H. andromedifolia clade uniquely has sepals of unequal size, and the H. anagalloides clade exhibits dolabriform trichomes. The remaining taxa (constituting the H. littoralis clade) lack such diagnostic morphological traits, but are reliably retrieved as sister group to the H. anagalloides clade. As a first step towards the taxonomic revision of Halgania, we have compiled a nomenclatural list of all validly published names (at the species level and below), provide information about herbarium deposition of original material and discuss the current use of the names.
A call for more science in forensic science
Authors: Bell Suzanne , Sah Sunita , Albright Thomas D. , Gates S. James , Denton M. Bonner , Casadevall Arturo
Published: April 2018

Forensic science is critical to the administration of justice. The discipline of forensic science is remarkably complex and includes methodologies ranging from DNA analysis to chemical composition to pattern recognition. Many forensic practices developed under the auspices of law enforcement and were vetted primarily by the legal system rather than being subjected to scientific scrutiny and empirical testing. Beginning in the 1990s, exonerations based on DNA-related methods revealed problems with some forensic disciplines, leading to calls for major reforms. This process generated a National Academy of Science report in 2009 that was highly critical of many forensic practices and eventually led to the establishment of the National Commission for Forensic Science (NCFS) in 2013. The NCFS was a deliberative body that catalyzed communication between nonforensic scientists, forensic scientists, and other stakeholders in the legal community. In 2017, despite continuing problems with forensic science, the Department of Justice terminated the NCFS. Just when forensic science needs the most support, it is getting the least. We urge the larger scientific community to come to the aid of our forensic colleagues by advocating for urgently needed research, testing, and financial support.
Why is the recognition of credentials not just a matter of good will? Five theories and the Austrian case
Authors: Pfeffer Thomas , Skrivanek Isabella
Published: April 2018

We live in a period of heightened internationalisation of population movement for work, education, and varied forms of forced and voluntary migration. Recognition of qualifications earned by the individuals concerned is a key practical, social, and psychological issue for themselves, as well as impacting on prospective employers, host communities and nations, and a variety of political and cultural debates. This paper analyses the education and qualification system in Austria, a longstanding recipient country for migration, and develops a theoretical framework for classifying institutional procedures that can recognise foreign degrees and the results of both non-formal and informal learning. It argues that both technocratic objectives and moral claims for a quick (or centralised) simplification of these procedures are unrealistic but identifies complementary measures to improve the situation incrementally. Additionally, the paper compares five theoretical approaches – namely human capital theory, theories of credentialism and professionalism, neo-institutionalism, and systems theory – which provide different explanations for the function of credentials in education and their use in labour markets, and offer new perspectives on the contribution of credentials to cultural diversification and to socio-economic inequality. This comparison helps us to understand the potential of and the challenges for recognition in different sectors of qualification systems.
Carrageenan :the difference between PNG and KCL gel precipitation method as Lactobacillus acidophilus encapsulation material
Authors: Setijawati D , Nursyam H , Salis H
Published: April 2018

The study on the effects of using of materials and methods in the preparation of the microcapsules Lactobacillus acidophilus towards the viability has been done. The research method used is experimental laboratory design. Variable research was kind of material (A) as the first factor with sub factor (A1 = Eucheuma cottonii) (A2 = Eucheuma spinosum) (A3 = mixture of Eucheuma cottonii and Eucheuma spinosum 1:1 ratio), while the second factor is a method of extraction to produce caragenan (B) with sub factor (B1 = Philipine Natural Grade modification) (B2 = KCl gel Press Precipitation). Analysis of different influences uses Analysis Of Varians followed by Fisher’s test. Analysis of data uses Mini tab 16. The results shows that the kind of extraction factors and methods gave significantly different effects on the viability of Lactobacillus acidophilus. The highest mean of Viablity obtained in the treatment of materials with a mixture of Eucheuma cottonii and Eucheuma spinosum and used KCl Gel Press method is equal to 7.14 log (CFU / mL). It is ssuggested using of kappa-iota carrageenanmixture asencapsulation material with KCl Gel Press method on Lactobacillus acidophilus microencapsulation process because it treatment gavethe highest average of Lactobacillus acidophilus viability.
MRI and Neuropsychological Correlates in African Americans With Hypertension and Left Ventricular Hypertrophy
Authors: Waldron-Perrine Brigid , Kisser Jason E , Brody Aaron , Haacke E Mark , Dawood Rachelle , Millis Scott , Levy Phillip
Published: April 2018

BACKGROUND

African Americans (AAs) are at high risk for hypertension (HTN) and poor blood pressure (BP) control. Persistently elevated BP contributes to cardiovascular morbidity. White matter hyperintensities (WMHs) are a definable magnetic resonance imaging (MRI) marker of cerebrovascular injury linked to impairments in higher level thinking (i.e., executive functions), memory formation, and speed of perceptual-motor processing.

METHODS

This subinvestigation evaluated neuropsychological functioning in association with WMH on brain MRIs in 23 otherwise-healthy hypertensive AAs participating in an NIH-funded study of the effects of vitamin D on BP and cardiac remodeling in AA patients 30–74 years of age with HTN and left ventricular hypertrophy. Neuropsychological assessment included psychomotor processing speed [(Symbol Digit Modality Test (SDMT) and Trail Making Test], executive functioning (Controlled Oral Word Association Test and Trail Making Test Part B), memory (Rey Auditory Verbal Learning Test), and fine motor functioning (Finger Tapping).

RESULTS

Significant correlations (P < 0.05) were found between volume of periventricular lesions and trails A (r = 0.51) and dominant hand finger tapping speed (r = −0.69) and between subcortical lesion volume and trails A (r = 0.60), both dominant (r = −0.62) and nondominant hand finger tapping speed (r = −0.76) and oral SDMT (r = −0.60); higher lesion volumes correlated to worse neuropsychological performance.

CONCLUSIONS

Psychomotor tests including the Trail Making Test and finger tapping speed are sensitive indicators of subclinical deficits in mental processing speed and could serve as early markers of deep subcortical cerebrovascular injury in otherwise-healthy individuals with uncontrolled chronic HTN.
Discriminating early- and late-stage cancers using multiple kernel learning on gene sets
Authors: Rahimi Arezou , Gönen Mehmet
Published: April 2018

Motivation

Identifying molecular mechanisms that drive cancers from early to late stages is highly important to develop new preventive and therapeutic strategies. Standard machine learning algorithms could be used to discriminate early- and late-stage cancers from each other using their genomic characterizations. Even though these algorithms would get satisfactory predictive performance, their knowledge extraction capability would be quite restricted due to highly correlated nature of genomic data. That is why we need algorithms that can also extract relevant information about these biological mechanisms using our prior knowledge about pathways/gene sets.

Results

In this study, we addressed the problem of separating early- and late-stage cancers from each other using their gene expression profiles. We proposed to use a multiple kernel learning (MKL) formulation that makes use of pathways/gene sets (i) to obtain satisfactory/improved predictive performance and (ii) to identify biological mechanisms that might have an effect in cancer progression. We extensively compared our proposed MKL on gene sets algorithm against two standard machine learning algorithms, namely, random forests and support vector machines, on 20 diseases from the Cancer Genome Atlas cohorts for two different sets of experiments. Our method obtained statistically significantly better or comparable predictive performance on most of the datasets using significantly fewer gene expression features. We also showed that our algorithm was able to extract meaningful and disease-specific information that gives clues about the progression mechanism.

Availability and implementation

Our implementations of support vector machine and multiple kernel learning algorithms in R are available at https://github.com/mehmetgonen/gsbc together with the scripts that replicate the reported experiments.
Improved pathway reconstruction from RNA interference screens by exploiting off-target effects
Authors: Srivatsa Sumana , Kuipers Jack , Schmich Fabian , Eicher Simone , Emmenlauer Mario , Dehio Christoph , Beerenwinkel Niko
Published: April 2018

Motivation

Pathway reconstruction has proven to be an indispensable tool for analyzing the molecular mechanisms of signal transduction underlying cell function. Nested effects models (NEMs) are a class of probabilistic graphical models designed to reconstruct signalling pathways from high-dimensional observations resulting from perturbation experiments, such as RNA interference (RNAi). NEMs assume that the short interfering RNAs (siRNAs) designed to knockdown specific genes are always on-target. However, it has been shown that most siRNAs exhibit strong off-target effects, which further confound the data, resulting in unreliable reconstruction of networks by NEMs.

Results

Here, we present an extension of NEMs called probabilistic combinatorial nested effects models (pc-NEMs), which capitalize on the ancillary siRNA off-target effects for network reconstruction from combinatorial gene knockdown data. Our model employs an adaptive simulated annealing search algorithm for simultaneous inference of network structure and error rates inherent to the data. Evaluation of pc-NEMs on simulated data with varying number of phenotypic effects and noise levels as well as real data demonstrates improved reconstruction compared to classical NEMs. Application to Bartonella henselae infection RNAi screening data yielded an eight node network largely in agreement with previous works, and revealed novel binary interactions of direct impact between established components.

Availability and implementation

The software used for the analysis is freely available as an R package at https://github.com/cbg-ethz/pcNEM.git.

Supplementary information

Supplementary data are available at Bioinformatics online.
DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Authors: Li Yu , Xu Fan , Zhang Fa , Xu Pingyong , Zhang Mingshu , Fan Ming , Li Lihua , Gao Xin , Han Renmin
Published: April 2018

Motivation

Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.

Results

Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster.

Availability and implementation

The main program is available at https://github.com/lykaust15/DLBI

Supplementary information

Supplementary data are available at Bioinformatics online.
Classifying tumors by supervised network propagation
Authors: Zhang Wei , Ma Jianzhu , Ideker Trey
Published: April 2018

Motivation

Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes.

Results

To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS2), which learns the mutated subnetworks underlying tumor subtypes using a supervised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS2 is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS2 on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classifying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes.

Availability and implementation

The NBS2 package is freely available at: https://github.com/wzhang1984/NBSS.

Supplementary information

Supplementary data are available at Bioinformatics online.
Learning with multiple pairwise kernels for drug bioactivity prediction
Authors: Cichonska Anna , Pahikkala Tapio , Szedmak Sandor , Julkunen Heli , Airola Antti , Heinonen Markus , Aittokallio Tero , Rousu Juho
Published: April 2018

Motivation

Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs.

Results

We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem.

Availability and implementation

Code is available at https://github.com/aalto-ics-kepaco.

Supplementary information

Supplementary data are available at Bioinformatics online.
Viral quasispecies reconstruction via tensor factorization with successive read removal
Authors: Ahn Soyeon , Ke Ziqi , Vikalo Haris
Published: April 2018

Motivation

As RNA viruses mutate and adapt to environmental changes, often developing resistance to anti-viral vaccines and drugs, they form an ensemble of viral strains––a viral quasispecies. While high-throughput sequencing (HTS) has enabled in-depth studies of viral quasispecies, sequencing errors and limited read lengths render the problem of reconstructing the strains and estimating their spectrum challenging. Inference of viral quasispecies is difficult due to generally non-uniform frequencies of the strains, and is further exacerbated when the genetic distances between the strains are small.

Results

This paper presents TenSQR, an algorithm that utilizes tensor factorization framework to analyze HTS data and reconstruct viral quasispecies characterized by highly uneven frequencies of its components. Fundamentally, TenSQR performs clustering with successive data removal to infer strains in a quasispecies in order from the most to the least abundant one; every time a strain is inferred, sequencing reads generated from that strain are removed from the dataset. The proposed successive strain reconstruction and data removal enables discovery of rare strains in a population and facilitates detection of deletions in such strains. Results on simulated datasets demonstrate that TenSQR can reconstruct full-length strains having widely different abundances, generally outperforming state-of-the-art methods at diversities 1–10% and detecting long deletions even in rare strains. A study on a real HIV-1 dataset demonstrates that TenSQR outperforms competing methods in experimental settings as well. Finally, we apply TenSQR to analyze a Zika virus sample and reconstruct the full-length strains it contains.

Availability and implementation

TenSQR is available at https://github.com/SoYeonA/TenSQR.

Supplementary information

Supplementary data are available at Bioinformatics online.
Modeling polypharmacy side effects with graph convolutional networks
Authors: Zitnik Marinka , Agrawal Monica , Leskovec Jure
Published: April 2018

Motivation

The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug–drug interactions, in which activity of one drug may change, favorably or unfavorably, if taken with another drug. The knowledge of drug interactions is often limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality and morbidity.

Results

Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein–protein interactions, drug–protein target interactions and the polypharmacy side effects, which are represented as drug–drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Unlike approaches limited to predicting simple drug–drug interaction values, Decagon can predict the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well polypharmacy side effects that have a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon opens up opportunities to use large pharmacogenomic and patient population data to flag and prioritize polypharmacy side effects for follow-up analysis via formal pharmacological studies.

Availability and implementation

Source code and preprocessed datasets are at: http://snap.stanford.edu/decagon.
Effectiveness of 13-Valent Pneumococcal Conjugate Vaccine Against Hospitalization for Community-Acquired Pneumonia in Older US Adults: A Test-Negative Design
Authors: McLaughlin John M , Jiang Qin , Isturiz Raul E , Sings Heather L , Swerdlow David L , Gessner Bradford D , Carrico Ruth M , Peyrani Paula , Wiemken Timothy L , Mattingly William A , Ramirez Julio A , Jodar Luis
Published: April 2018

Background

Following universal recommendation for use of 13-valent pneumococcal conjugate vaccine (PCV13) in US adults aged ≥65 years in September 2014, we conducted the first real-world evaluation of PCV13 vaccine effectiveness (VE) against hospitalized vaccine-type community-acquired pneumonia (CAP) in this population.

Methods

Using a test-negative design, we identified cases and controls from a population-based surveillance study of adults in Louisville, Kentucky, who were hospitalized with CAP. We analyzed a subset of CAP patients enrolled 1 April 2015 through 30 April 2016 who were aged ≥65 years and consented to have their pneumococcal vaccination history confirmed by health insurance records. Cases were defined as hospitalized CAP patients with PCV13 serotypes identified via culture or serotype-specific urinary antigen detection assay. Remaining CAP patients served as test-negative controls.

Results

Of 2034 CAP hospitalizations, we identified PCV13 serotypes in 68 (3.3%) participants (ie, cases), of whom 6 of 68 (8.8%) had a positive blood culture. Cases were less likely to be immunocompromised (29.4% vs 46.4%, P = .02) and overweight or obese (41.2% vs 58.6%, P = .01) compared to controls, but were otherwise similar. Cases were less likely to have received PCV13 than controls (3/68 [4.4%] vs 285/1966 [14.5%]; unadjusted VE, 72.8% [95% confidence interval, 12.8%−91.5%]). No confounding was observed during adjustment for patient characteristics, including immunocompromised status, body mass index, and history of influenza and pneumococcal polysaccharide vaccination (adjusted VE range, 71.1%−73.3%).

Conclusions

Our study is the first to demonstrate real-world effectiveness of PCV13 against vaccine-type CAP in adults aged ≥65 years following introduction into a national immunization program.
High discordance in blood and genital tract HIV-1 drug resistance in Indian women failing first-line therapy
Authors: Saravanan Shanmugam , Gomathi Selvamurthi , Delong Allison , Kausalya Bagavathi , Sivamalar Sathasivam , Poongulali Selvamuthu , Brooks Katherine , Kumarasamy Nagalingeswaran , Balakrishnan Pachamuthu , Solomon Sunil S , Cu-Uvin Susan , Kantor Rami
Published: April 2018

Objectives

Examine HIV-1 plasma viral load (PVL) and genital tract (GT) viral load (GVL) and drug resistance in India.

Methods

At the YRG Centre for AIDS Research and Education, Chennai, we tested: PVL in women on first-line ART for ≥6 months; GVL when PVL >2000 copies/mL; and plasma, genital and proviral reverse transcriptase drug resistance when GVL >2000 copies/mL. Wilcoxon rank-sum and Fisher's exact tests were used to identify failure and resistance associations. Pearson correlations were calculated to evaluate PVL–GVL associations. Inter-compartmental resistance discordance was evaluated using generalized estimating equations.

Results

Of 200 women, 37% had detectable (>400 copies/mL) PVL and 31% had PVL >1000 copies/mL. Of women with detectable PVL, 74% had PVL >2000 copies/mL, of which 74% had detectable GVL. Higher PVL was associated with higher GVL. Paired plasma and genital sequences were available for 21 women; mean age of 34 years, median ART duration of 33 months, median CD4 count of 217 cells/mm3, median PVL of 5.4 log10 copies/mL and median GVL of 4.6 log10 copies/mL. Drug resistance was detected in 81%–91% of samples and 67%–76% of samples had dual-class resistance. Complete three-compartment concordance was seen in only 10% of women. GT–proviral discordance was significantly larger than plasma–proviral discordance. GT or proviral mutations discordant from plasma led to clinically relevant resistance in 24% and 30%, respectively.

Conclusions

We identified high resistance and high inter-compartmental resistance discordance in Indian women, which might lead to unrecognized resistance transmission and re-emergence compromising treatment outcomes, particularly relevant to countries like India, where sexual HIV transmission is predominant.
Provider and patient satisfaction with the integration of ambulatory and hospital EHR systems
Authors: Meyerhoefer Chad D , Sherer Susan A , Deily Mary E , Chou Shin-Yi , Guo Xiaohui , Chen Jie , Sheinberg Michael , Levick Donald
Published: April 2018

Objective

The installation of EHR systems can disrupt operations at clinical practice sites, but also lead to improvements in information availability. We examined how the installation of an ambulatory EHR at OB/GYN practices and its subsequent interface with an inpatient perinatal EHR affected providers’ satisfaction with the transmission of clinical information and patients’ ratings of their care experience.

Methods

We collected data on provider satisfaction through 4 survey rounds during the phased implementation of the EHR. Data on patient satisfaction were drawn from Press Ganey surveys issued by the healthcare network through a standard process. Using multivariable models, we determined how provider satisfaction with information transmission and patient satisfaction with their care experience changed as the EHR system allowed greater information flow between OB/GYN practices and the hospital.

Results

Outpatient OB/GYN providers became more satisfied with their access to information from the inpatient perinatal triage unit once system capabilities included automatic data flow from triage back to the OB/GYN offices. Yet physicians were generally less satisfied with how the EHR affected their work processes than other clinical and non-clinical staff. Patient satisfaction dropped after initial EHR installation, and we find no evidence of increased satisfaction linked to system integration.

Conclusions

Dissatisfaction of providers with an EHR system and difficulties incorporating EHR technology into patient care may negatively impact patient satisfaction. Care must be taken during EHR implementations to maintain good communication with patients while satisfying documentation requirements.
Primary Melanoma Histologic Subtype: Impact on Survival and Response to Therapy
Authors: Lattanzi Michael , Lee Yesung , Simpson Danny , Moran Una , Darvishian Farbod , Kim Randie H , Hernando Eva , Polsky David , Hanniford Doug , Shapiro Richard , Berman Russell , Pavlick Anna C , Wilson Melissa A , Kirchhoff Tomas , Weber Jeffrey S , Zhong Judy , Osman Iman
Published: April 2018

Background

Two primary histologic subtypes, superficial spreading melanoma (SSM) and nodular melanoma (NM), comprise the majority of all cutaneous melanomas. NM is associated with worse outcomes, which have been attributed to increased thickness at presentation, and it is widely expected that NM and SSM would exhibit similar behavior once metastasized. Herein, we tested the hypothesis that primary histologic subtype is an independent predictor of survival and may impact response to treatment in the metastatic setting.

Methods

We examined the most recent Surveillance, Epidemiology, and End Results (SEER) cohort (n = 118 508) and the New York University (NYU) cohort (n = 1621) with available protocol-driven follow-up. Outcomes specified by primary histology were studied in both the primary and metastatic settings with respect to BRAF-targeted therapy and immunotherapy. We characterized known driver mutations and examined a 140-gene panel in a subset of NM and SSM cases using next-generation sequencing. All statistical tests were two-sided.

Results

NM was an independent risk factor for death in both the SEER (hazard ratio [HR] = 1.55, 95% confidence interval [CI] = 1.41 to 1.70, P < .001) and NYU (HR = 1.47, 95% CI = 1.05, 2.07, P = .03) cohorts, controlling for thickness, ulceration, stage, and other variables. In the metastatic setting, NM remained an independent risk factor for death upon treatment with BRAF-targeted therapy (HR = 3.33, 95% CI = 1.06 to 10.47, P = .04) but showed no statistically significant difference with immune checkpoint inhibition. NM was associated with a higher rate of NRAS mutation (P < .001), and high-throughput sequencing revealed NM-specific genomic alterations in NOTCH4, ANK3, and ZNF560, which were independently validated.

Conclusions

Our data reveal distinct clinical and biological differences between NM and SSM that support revisiting the prognostic and predictive impact of primary histology subtype in the management of cutaneous melanoma.
Proteinuria-associated renal magnesium wasting leads to hypomagnesemia: a common electrolyte abnormality in chronic kidney disease
Authors: Oka Tatsufumi , Hamano Takayuki , Sakaguchi Yusuke , Yamaguchi Satoshi , Kubota Keiichi , Senda Masamitsu , Yonemoto Sayoko , Shimada Karin , Matsumoto Ayumi , Hashimoto Nobuhiro , Mori Daisuke , Monden Chikako , Takahashi Atsushi , Obi Yoshitsugu , Yamamoto Ryohei , Takabatake Yoshitsugu , Kaimori Jun-Ya , Moriyama Toshiki , Horio Masaru , Matsui Isao , Isaka Yoshitaka
Published: April 2018

Background

Hypomagnesemia (Hypo-Mg) predicts mortality and chronic kidney disease (CKD) progression. However, in CKD, its prevalence, kidney-intrinsic risk factors, and the effectiveness of oral magnesium (Mg) therapy on serum Mg levels is uncertain.

Methods

In a cross-sectional study enrolling pre-dialysis outpatients with CKD, the prevalence of electrolyte abnormalities (Mg, sodium, potassium, calcium and phosphorus) was compared. In an open-label randomized controlled trial (RCT), we randomly assigned CKD patients to either the magnesium oxide (MgO) or control arm. The outcome was serum Mg levels at 1 year.

Results

In 5126 patients, Hypo-Mg was the most common electrolyte abnormality (14.7%) with similar prevalence across stages of CKD. Positive proteinuria was a risk factor of Hypo-Mg (odds ratio 2.2; 95% confidence interval 1.2–4.0). However, stratifying the analyses by diabetes mellitus (DM), it was not significant in DM (Pinteraction = 0.04). We enrolled 114 patients in the RCT. Baseline analyses showed that higher proteinuria was associated with higher fractional excretion of Mg. This relationship between proteinuria and renal Mg wasting was mediated by urinary tubular markers in mediation analyses. In the MgO arm, higher proteinuria or tubular markers predicted a significantly lower 1-year increase in serum Mg. In patients with a urinary protein-to-creatinine ratio (uPCR) <0.3 g/gCre, serum Mg at 1 year was 2.4 and 2.0 mg/dL in the MgO and control arms, respectively (P < 0.001), with no significant between-group difference in patients whose uPCR was ≥0.3 g/gCre (Pinteraction=0.001).

Conclusions

Proteinuria leads to renal Mg wasting through tubular injuries, which explains the high prevalence of Hypo-Mg in CKD.
A Novel Disease of Big-Leaf Mahogany Caused by TwoFusariumSpecies in Mexico
Authors: Santillán-Mendoza R. , Fernández-Pavía S. P. , O’Donnell K. , Ploetz R. C. , Ortega-Arreola R. , Vázquez-Marrufo G. , Benítez-Malvido J. , Montero-Castro J. C. , Soto-Plancarte A. , Rodríguez-Alvarado G.
Published: April 2018

Big-leaf mahogany (Swietenia macrophylla) is valued for its high-quality wood and use in urban landscapes in Mexico. During surveys of mango-producing areas in the central western region of Mexico, symptoms of malformation, the most important disease of mango in the area, were observed on big-leaf mahogany trees. The objectives of this research were to describe this new disease and determine its cause. Symptoms on big-leaf mahogany at four sites in Michoacán, Mexico resembled those of the vegetative phase of mango malformation, including compact, bunched growth of apical and lateral buds, with greatly shortened internodes and small leaves that curved back toward the supporting stem. Of 163 isolates that were recovered from symptomatic tissues, most were identified as Fusarium pseudocircinatum (n = 121) and F. mexicanum (n = 39) using molecular systematic data; two isolates represented unnamed phylospecies within the F. incarnatum-equiseti species complex (FIESC 20-d and FIESC 37-a) and another was in the F. solani species complex (FSSC 25-m). However, only F. mexicanum and F. pseudocircinatum induced malformation symptoms on 14-day-old seedlings of big-leaf mahogany. The results indicate that F. mexicanum and F. pseudocircinatum, previously reported in Mexico as causal agents of mango malformation disease, also affect big-leaf mahogany. This is the first report of this new disease and the first time that F. mexicanum was shown to affect a host other than mango.
Induced Systemic Resistance Against Citrus Canker Disease by Rhizobacteria
Authors: Riera Nadia , Wang Han , Li Yong , Li Jinyun , Pelz-Stelinski Kirsten , Wang Nian
Published: April 2018

Citrus canker, caused by Xanthomonas citri subsp. citri, is an important citrus disease that causes significant economic losses worldwide. All commercial citrus varieties are susceptible to citrus canker. Currently, chemical control with copper based products is the main approach to control X. citri subsp. citri dispersal and plant colonization. However, extensive use of copper compounds can result in copper-resistant strains and cause adverse effects on the environment. Alternatives to chemical control involve the activation of citrus immunity to control the disease. Here, we investigated the ability of multiple rhizobacteria to induce a systemic defense response in cultivar Duncan grapefruit. Burkholderia territorii strain A63, Burkholderia metallica strain A53, and Pseudomonas geniculata strain 95 were found to effectively activate plant defense and significantly reduce symptom development in leaves challenged with X. citri subsp. citri. In the priming phase, root application of P. geniculata induced the expression of salicylic acid (SA)-signaling pathway marker genes (PR1, PR2, PR5, and salicylic acid carboxyl methyltransferase [SAM-SACM]). Gene expression analyses after X. citri subsp. citri challenge showed that root inoculation with P. geniculata strain 95 increased the relative levels of phenylalanine ammonia lyase 1 and SAM-SACM, two genes involved in the phenylpropanoid pathway as well as the biosynthesis of SA and methyl salicylate (MeSA), respectively. However, hormone analyses by UPLC-MS/MS showed no significant difference between SA in P. geniculata-treated plants and control plants at 8 days post-beneficial bacteria root inoculation. Moreover, P. geniculata root-treated plants contained higher reactive oxygen species levels in aerial tissues than control plants 8 days post-treatment application. This study demonstrates that rhizobacteria can modulate citrus immunity resulting in a systemic defense response against X. citri subsp. citri under greenhouse conditions.