bluuebunny/crossref_metadata_2025_split huggingface dataset:
A combined analysis of genetically correlated traits identifies 107 loci associated with intelligence
Authors: Hill W. D. , Davies G. , McIntosh A. M. , Gale C. R. , Deary I. J. 
Published: July 2017

AbstractIntelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including many physical and mental health variables. Both education and household income are strongly genetically correlated with intelligence, at rg=0.73 and rg=0.70 respectively. This allowed us to utilize a novel approach, Multi-Trait Analysis of Genome-wide association studies (MTAG; Turley et al. 2017), to combine two large genome-wide association studies (GWASs) of education and household income to increase power in the largest GWAS on intelligence so far (Sniekers et al. 2017). This study had four goals: firstly, to facilitate the discovery of new genetic loci associated with intelligence; secondly, to add to our understanding of the biology of intelligence differences; thirdly, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predict phenotypic intelligence variance in an independent sample. We apply MTAG to three large GWAS: Sniekers et al (2017) on intelligence, Okbay et al. (2016) on Educational attainment, and Hill et al. (2016) on household income. By combining these three samples our functional sample size increased from 78 308 participants to 147 194. We found 107 independent loci associated with intelligence, implicating 233 genes, using both SNP-based and gene-based GWAS. We find evidence that neurogenesis may explain some of the biological differences in intelligence as well as genes expressed in the synapse and those involved in the regulation of the nervous system. We show that the results of our combined analysis demonstrate the same pattern of genetic correlations as a single measure/the simple measure of intelligence, providing support for the meta-analysis of these genetically-related phenotypes. We find that our MTAG meta-analysis of intelligence shows similar genetic correlations to 26 other phenotypes when compared with a GWAS consisting solely of cognitive tests. Finally, using an independent sample of 6 844 individuals we were able to predict 7% of intelligence using SNP data alone.
Orb-web spider color polymorphism through the eyes of multiple prey and predators
Authors: Ximenes Nathalia , Gawryszewski Felipe M. 
Published: July 2017

ABSTRACTThe sensory drive theory predicts that signals, sensory systems, and signaling behavior should coevolve. Variation in the sensory systems of prey and predators may explain the diversity of color signals, such as color polymorphism. The spiderGasteracantha cancriformis(Araneidae) possesses several conspicuous color morphs. The aim of the present study was to assess whether the color polymorphism ofG. cancriformismay be maintained by pressure from multiple signal receivers, such as prey and predators with distinct color vision systems. Although, the multiple receivers world is a more realistic scenario, it has received little attention. In orb-web spiders, the prey attraction hypothesis states that conspicuous colors are prey lures that increase spider foraging success via flower mimicry. However, in highly defended species, conspicuous colors could also be a warning signal to predators. We used color vision modelling to estimate chromatic and achromatic contrast ofG. cancriformismorphs as perceived by potential prey and predator taxa. Our results revealed that individual prey and predator taxa perceive the conspicuousness of morphs differently. For instance, the red morph is perceived as quite conspicuous to lepidopteran prey and avian predators, but not by other insects. Therefore, the multiple prey and predator hypotheses may explain the evolution of color polymorphism inG. cancriformis. However, flower mimicry hypothesis was weakly corroborated. Other parameters that are not evaluated by color vision models, such as distance, shape, angle, and pattern geometry could also affect the perception of color morphs by both prey and predators and thereby influence morph survival.
Cortical area and subcortical volume mediate the effect of parental education and adverse experiences on cognitive performance in youth
Authors: Mehta Chintan M. , Malins Jeffrey G. , Noble Kimberly G. , Gruen Jeffrey R. 
Published: July 2017

AbstractEarly adversity and socioeconomic disadvantage are risk factors associated with diminished cognitive outcomes during development. Recent studies also provide evidence that upbringings characterized by stressful experiences and markers of disadvantage during childhood, such as lower parental education or household income, are associated with variation in brain structure. Although disadvantage often confers adversity, these are distinct risk factors whose differential influences on neurodevelopment and neurocognitive outcomes are not well characterized. We examined pathways linking parental education, adverse experiences, brain structure, and cognitive performances through an analysis of 1,413 typically-developing youth, ages 8 through 21, in the Philadelphia Neurodevelopmental Cohort. Parental education and adverse experiences had unique associations with cortical surface area and subcortical volume as well as cognitive performance across several domains. Associations between parental education and several cognitive tasks were explained, in part, by variation in cortical surface area. In contrast, associations between adversity and cognitive tasks were explained primarily by variation in subcortical volume. A composite neurodevelopmental factor derived from principal component analysis of cortical thickness, cortical surface area, and subcortical volume mediated independent associations between both parental education and adverse experiences with reading, geometric reasoning, verbal reasoning, attention, and emotional differentiation tasks. Our analysis provides novel evidence that socioeconomic disadvantage and adversity influence neurodevelopmental pathways associated with cognitive outcomes through independent mechanisms.
An aggrecan fragment drives osteoarthritis pain through Toll-like receptor 2
Authors: Miller Rachel E. , Miller Richard J. , Ishihara Shingo , Tran Phuong B. , Golub Suzanne B. , Last Karena , Fosang Amanda J. , Malfait Anne-Marie 
Published: July 2017

AbstractPain is the predominant symptom of osteoarthritis, but the connection between joint damage and the genesis of pain is not well understood. Loss of articular cartilage is a hallmark of osteoarthritis, and it occurs through enzymatic degradation of aggrecan by ADAMTS-4/5-mediated cleavage in the interglobular domain (E373-374A). Further cleavage by MMPs (N341-342F) releases a 32-amino-acid aggrecan fragment (32-mer). We investigated the role of this 32-mer in driving joint pain. We demonstrated that the 32-mer excites dorsal root ganglion (DRG) nociceptive neurons, both in culture and in intact explants. Treatment of cultured sensory neurons with the 32-mer induced them to express the pro-algesic chemokine, MCP-1/CCL2. These effects were mediated through Toll-like receptor (TLR)2, which we demonstrated was expressed by nociceptive neurons. In addition, intra-articular injection of the 32-mer provoked knee hyperalgesia in wild-type but notTlr2null mice. Blocking the production or action of the 32-mer in transgenic mice prevented the development of knee hyperalgesia in a murine model of osteoarthritis. These findings suggest that the aggrecan 32-mer fragment directly activates TLR2 on joint nociceptors and is an important mediator of the development of osteoarthritis-associated joint pain.
Flexible, Cluster-Based Analysis of the Electronic Medical Record of Sepsis with Composite Mixture Models
Authors: Mayhew Michael B. , Petersen Brenden K. , Sales Ana Paula , Greene John D. , Liu Vincent X. , Wasson Todd S. 
Published: July 2017

AbstractThe widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization. However, there are significant barriers that must be overcome to extract these insights from EMR data. First, EMR datasets consist of both static and dynamic observations of discrete and continuous-valued variables, many of which may be missing, precluding the application of standard multivariate analysis techniques. Second, clinical populations observed via EMRs and relevant to the study and management of conditions like sepsis are often heterogeneous; properly accounting for this heterogeneity is critical. Here, we describe an unsupervised, probabilistic framework called a composite mixture model that can simultaneously accommodate the wide variety of observations frequently observed in EMR datasets, characterize heterogeneous clinical populations, and handle missing observations. We demonstrate the efficacy of our approach on a large-scale sepsis cohort, developing novel techniques built on our model-based clusters to track patient mortality risk over time and identify physiological trends and distinct subgroups of the dataset associated with elevated risk of mortality during hospitalization.AbbreviationsEMRelectronic medical recordCMMcomposite mixture modelKPNCKaiser Permanente Northern CaliforniaBICBayesian information criterionAICAkaike information criterionPAMpartitioning around medoidsMICEmultivariate imputation using chained equations
Intraspecific variation in indirect plant-soil feedbacks as a driver of a wetland plant invasion
Authors: Allen Warwick J. , Meyerson Laura A. , Flick Andrew J. , Cronin James T. 
Published: July 2017

ABSTRACTPlant-soil feedbacks (PSFs) can influence plant competition via direct interactions with pathogens and mutualists or indirectly via apparent competition/mutualisms (i.e., spillover to cooccurring plants) and soil legacy effects. Presently, it is unknown how intraspecific variation in PSFs interacts with the environment (e.g., nutrient availability) to influence competition between native and invasive plants. We conducted a fully crossed multi-factor greenhouse experiment to determine the effects of soil biota, interspecific competition, and nutrient availability on biomass of replicate populations from one native and two invasive lineages of common reed (Phragmites australis) and a single lineage of native smooth cordgrass (Spartina alterniflora). Harmful soil biota consistently dominated PSFs involving all threeP. australislineages, reducing biomass by 10%, regardless of nutrient availability orS. alterniflorapresence as a competitor. Spillover of soil biota derived from the rhizosphere of the two invasiveP. australislineages reducedS. alterniflorabiomass by 7%, whereas soil biota from the nativeP. australislineage increasedS. alterniflorabiomass by 6%. Interestingly, regardless of lineage,P. australissoil biota negatively affectedS. alterniflorabiomass when grown alone (i.e., a soil legacy), but had a positive impact when grown withP. australis, suggesting thatP. australisis preferred by harmful generalist soil biota or facilitatesS. alternifloravia spillover (i.e., apparent mutualism). Soil biota also reduced the negative impacts of interspecific competition onS. alternifloraby 13%, although it remained competitively inferior toP. australisacross all treatments. Moreover, competitive interactions and the response to nutrients did not differ amongP. australislineages, indicating that interspecific competition and nutrient deposition may not be key drivers ofP. australisinvasion in North America. Taken together, although soil biota, interspecific competition, and nutrient availability appear to have no direct impact on the success of invasiveP. australislineages in North America, indirect spillover and soil legacies fromP. australisoccur and may have important implications for co-occurring native species and restoration of invaded habitats. Our study integrates multiple factors linked to plant invasions, highlighting that indirect interactions are likely commonplace in driving successful invasions and their impacts on the local community.
A broad role for YBX1 in defining the small non-coding RNA composition of exosomes
Authors: Shurtleff Matthew J. , Yao Jun , Qin Yidan , Nottingham Ryan M. , Temoche-Diaz Morayma , Schekman Randy , Lambowitz Alan M. 
Published: July 2017

AbstractRNA is secreted from cells enclosed within extracellular vesicles (EVs). Defining the RNA composition of EVs is challenging due to their co-isolation with contaminants, a lack of knowledge of the mechanisms of RNA sorting into EVs and limitations of conventional RNA-seq methods. Here we present our observations using thermostable group II intron reverse transcriptase sequencing (TGIRT-seq) to characterize the RNA extracted from HEK293T cell EVs isolated by flotation gradient ultracentrifugation and from exosomes containing the tetraspannin CD63 further purified from the gradient fractions by immunoisolation. We found that EV-associated transcripts are dominated by full-length, mature tRNAs and other small non-coding RNAs encapsulated within vesicles. A substantial proportion of the reads mapping to protein-coding genes, long non-coding, and antisense RNAs were due to DNA contamination on the surface of vesicles. Nevertheless, sequences mapping to spliced mRNAs were identified within HEK293T cell EVs and exosomes, among the most abundant being transcripts containing a 5’ terminal oligopyrimidine (5’ TOP) motif. Our results indicate that the RNA-binding protein YBX1, which we showed previously is required for the sorting of selected miRNAs into exosomes, plays a role in the sorting of highly abundant small non-coding RNA species, including tRNAs, Y RNAs, and Vault RNAs. Finally, we obtained evidence for an EV-specific tRNA modification, perhaps indicating a role for post-transcriptional modification in the sorting of some RNA species into EVs. The identification of full-length small non-coding RNAs within EVs suggests a role for EVs in the export and possible intercellular functional transfer of abundant cellular transcripts.Statement of SignificanceCells release vesicles containing selectively packaged cargo, including RNA, into the extracellular environment. Prior studies have identified RNA inside extracellular vesicles (EVs) but, due to limitations of conventional sequencing methods, highly structured and post-transcriptionally modified RNA species were not effectively captured. Using an alternative sequencing approach (TGIRT-seq), we found that EVs contain abundant small non-coding RNA species, including full-length tRNAs and Y RNAs. Using a knockout cell line, we obtained evidence that the RNA-binding protein YBX1 plays a role in sorting small non-coding RNAs into a subpopulation of extracellular vesicles termed exosomes. These experiments expand our understanding of EV-RNA composition and provide insights into how RNA is sorted into EVs for export from the cell.
Measuring the gut microbiome in birds: comparison of faecal and cloacal sampling
Authors: Videvall Elin , Strandh Maria , Engelbrecht Anel , Cloete Schalk , Cornwallis Charlie K. 
Published: July 2017

AbstractThe gut microbiomes of birds and other animals are increasingly being studied in ecological and evolutionary contexts. While methods for preserving samples and processing high-throughput sequence data to characterise bacterial communities have received considerable attention, there has been little evaluation of non-invasive sampling methods. Numerous studies on birds and reptiles have made inferences about gut microbiota using cloacal sampling, however, it is not known whether the bacterial community of the cloaca provides an accurate representation of the avian gut microbiome. We examined the accuracy with which cloacal swabs and faecal samples measure the microbiota in three different parts of the gastrointestinal tract (ileum, caecum, and colon) using a case study on juvenile ostriches,Struthio camelus, and high-throughput 16S rRNA sequencing. We found that faeces were significantly better than cloacal swabs in representing the bacterial community of the colon. Cloacal samples had a higher abundance of Gammaproteobacteria and fewer Clostridia relative to the gut and faecal samples. However, both faecal and cloacal samples were poor representatives of the microbial communities in the caecum and ileum. Furthermore, the accuracy of the sampling methods in measuring the abundance of different bacterial taxa was highly variable: Bacteroidetes was the most highly correlated phylum between all three gut sections and both methods, whereas colonic Actinobacteria correlated strongly only with faecal samples. This study demonstrates that sampling methods can have significant effects on the inferred gut microbiome in studies of birds. Based on our results, we recommend sampling faeces, whenever possible, as this provides the most accurate assessment of the colon microbiome. The fact that neither sampling technique portrayed the bacterial community of the ileum or the caecum illustrates the difficulty in non-invasively monitoring gut bacteria located further up in the gastrointestinal tract. These results have important implications for the interpretation of avian gut microbiome studies.
Association of Vancomycin with Lipid Vesicles
Authors: Hu Xiaohui , Tam Kin Yip 
Published: July 2017

AbstractAntibiotics plays a pivotal role in modern medicine for the treatment of bacterial infection in patients. Membrane defines the boundary between single cell and its environment and is a main target for antibacterial agents. To better understand the mechanism of antibiotics action on microbes, we utilized liposome as membrane mimic model to study antibiotics interaction with bacterial membrane by variety of biophysical methods. Isothermal calorimetry and fluorescence photometry experiments were performed to examine interaction between antibiotics and liposome. We found that vancomycin, one of the most important antibiotics for the treatment of serious infections by gram-positive bacteria, binds to the liposome. The association between the drug and the liposome does not involve the tail part of the lipids. Moreover, the binding affinity increases along with the increment of liposome size. Of three major lipid components, phosphatidylglycerol is the preferential target for vancomycin binding. We also showed that vancomycin associates with vesicle derived fromStaphylococcus aureusmembrane in a similar manner as the binding to liposome. Our data suggested that vancomycin associates with bacterial membrane through direct interaction with lipid head groups with the extent of the association depending very much on specific type of lipids and curvature of local membrane structure.
Axe: rapid, competitive sequence read demultiplexing using a trie
Authors: Murray Kevin D. , Borevitz Justin O. 
Published: July 2017

AbstractHere we implement a rapid algorithm for demultiplexing DNA sequence reads with in-read indices. Axe selects the optimal index present in a sequence read, even in the presence of sequencing errors. The algorithm is able to handle combinatorial indexing, indices of differing length, and several mismatches per index sequence.
A Novel, Fast and Efficient Single-Sensor Automatic Sleep-Stage Classification Based on Complementary Cross-Frequency Coupling Estimates
Authors: Dimitriadis Stavros I. , Salis Christos , Linden David 
Published: July 2017

AbstractObjectiveLimitations of the manual scoring of polysomnograms, which include data from electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG) and electromyogram (EMG) channels, have long been recognised. Manual staging is resource-intensive and time-consuming and considerable efforts have to be spent to ensure inter-rater reliability. There is thus great interest in techniques based on signal processing and machine learning for a completely Automatic Sleep Stage Classification (ASSC).MethodsIn this paper, we present a single EEG-sensor ASSC technique based on dynamic reconfiguration of different aspects of cross-frequency coupling (CFC) estimated between predefined frequency pairs over 5s epoch lengths. The proposed analytic scheme is demonstrated using the PhysioNet Sleep European Data Format (EDF) Database using 20 healthy young adults with repeat recordings.ResultsWe achieved very high classification sensitivity, specificity and accuracy of96.2 ± 2.2%, 94.2 ± 2.3%, and94.4 ± 2.2% across 20 folds, respectively and high mean F1-score (92%, range 90–94%) when multi-class Bayes Naive classifier was applied.ConclusionsOur method outperformed the accuracy of previous studies on different datasets but also on the same database.SignificanceSingle-sensor ASSC makes the whole methodology appropriate for longitudinal monitoring using wearable EEG in real world and lab-oriented environments.
Bayesian inference and simulation approaches improve the assessment of Elo-scores in the analysis of social behaviour
Authors: Goffe Adeelia S. , Fischer Julia , Sennhenn-Reulen Holger 
Published: July 2017

AbstractThe construction of rank hierarchies based on agonistic interactions between two individuals (“dyads”) is an important component in the characterization of the social structure of groups. To this end, winner-loser matrices are typically created, which collapse the outcome of dyadic interactions over time, resulting in the loss of all information contained in the temporal domain. Methods that track changes in the outcome of dyadic interactions (such as “Elo-scores”) are receiving increasing interest. Critically, individual scores are not just based on the succession of wins and losses, but depend on the values of starting scores and an update (“tax”) coefficient. Recent studies improved existing methods by introducing a point estimation of these auxiliary parameters on the basis of a maximum likelihood (ML) approach. For a sound assessment of the rank hierarchies generated this way, we argue that measures of uncertainty of the estimates, as well as a quantification of the robustness of the methods, are also needed.We introduce a Bayesian inference (BI) approach using ‘‘partial pooling”, which rests on the assumption that all starting scores are samples from the same distribution. We compare the outcome of the ML approach to that of the BI approach using real-world data. In addition, we simulate different scenarios to explore in which way the Elo-score responds to social events (such as rank changes), and low numbers of observations.Estimates of the starting scores based on ‘partial pooling” are more robust than those based on ML, also in scenarios where some individuals have only few observations. Our simulations show that assumed rank differences may fall well within the ‘uncertain” range, and that low sampling density, unbalanced designs, and coalitionary leaps involving several individuals within the hierarchy may yield unreliable results.Our results support the view that Elo rating can be a powerful tool in the analysis of social behaviour, when the data meet certain criteria. Assessing the uncertainty greatly aids in the interpretation of results. We advocate running simulation approaches to test how well Elo scores reflect the (simulated) true structure and how sensitive the score is to true changes in the hierarchy.