bluuebunny/crossref_metadata_2025_split huggingface dataset:
Density‐dependence mediates coral assemblage structure
Authors: Edmunds Peter J. , Nelson Hannah R. , Bramanti Lorenzo 
Published: October 2018

AbstractDensity dependence (DD) controls community recovery following widespread mortality, yet this principle rarely has been applied to coral assemblages. The reefs of Mo'orea, French Polynesia, provide the opportunity to study DD of coral population growth, because coral assemblages in this location responded to declines in abundance with high recruitment and an increase in cover during which recruitment of pocilloporid corals was inversely associated with density. This study tests for DD in this system, first, by describing the context within which it operates: coral cover changed from 46% in 2005, to <1% in 2010 following an outbreak of a corallivorous sea star and a cyclone, and then increased to 74% by 2017, in large part through inverse density‐associated pocilloporid recruitment. Second, a test for DD of recruitment was conducted by decreasing Pocillopora spp. cover from 33% to 19%: one year later, the density of Pocillopora spp. recruits was 1.65‐fold higher in the low vs. high cover treatment. Finally, the effects of DD were investigated by comparing simulated and empirical distributions of pocilloporid colonies: as predicted by DD, small colonies were randomly distributed, while large colonies were uniformly distributed. Together these results demonstrate DD of population regulation for Pocillopora spp. corals, thus revealing the potential importance of this ecological principle in determining the resilience of coral assemblages.
Foliar fungal endophyte communities are structured by environment but not host ecotype in Panicum virgatum (switchgrass)
Authors: Whitaker Briana K. , Reynolds Heather L. , Clay Keith 
Published: October 2018

AbstractExperimental tests of community assembly mechanisms for host‐associated microbiomes in nature are lacking. Asymptomatic foliar fungal endophytes are a major component of the plant microbiome and are increasingly recognized for their impacts on plant performance, including pathogen defense, hormonal manipulation, and drought tolerance. However, it remains unclear whether fungal endophytes preferentially colonize certain host ecotypes or genotypes, reflecting some degree of biotic adaptation in the symbioses, or whether colonization is simply a function of spore type and abundance within the local environment. Whether host ecotype, local environment, or some combination of both controls the pattern of microbiome formation across hosts represents a new dimension to the age‐old debate of nature versus nurture. Here, we used a reciprocal transplant design to explore the extent of host specificity and biotic adaptation in the plant microbiome, as evidenced by differential colonization of host genetic types by endophytes. Specifically, replicate plants from three locally‐adapted ecotypes of the native grass Panicum virgatum (switchgrass) were transplanted at three geographically distinct field sites (one home and two away) in the Midwestern US. At the end of the growing season, plant leaves were harvested and the fungal microbiome characterized using culture‐dependent sequencing techniques. Our results demonstrated that fungal endophyte community structure was determined by local environment (i.e., site), but not by host ecotype. Fungal richness and diversity also strongly differed by site, with lower fungal diversity at a riparian field site, whereas host ecotype had no effect. By contrast, there were significant differences in plant phenotypes across all ecotypes and sites, indicating ecotypic differentiation of host phenotype. Overall, our results indicate that environmental factors are the primary drivers of community structure in the switchgrass fungal microbiome.
Double‐structured sparse multitask regression with application of statistical downscaling
Authors: Li Yi , Ding A. Adam 
Published: October 2018

AbstractStatistical learning of high‐dimensional data often improves learning performance by taking advantage of structural information hidden in data. In this paper, we investigate the structured multitask learning problem and propose a generalized estimator to utilize the structural information simultaneously from the predictors and from the response variables. The key idea of our methodology is to apply flexible quadratic penalties to regularize the likelihood function that incorporates both types of structural information. We prove the theoretical statistical properties of the proposed estimator, which generalizes several state‐of‐the‐art sparse learning methods. Numerical examples are provided for the proposed estimator on synthetic data and on the statistical downscaling problem in climate research.
Unsupervised space–time clustering using persistent homology
Authors: Islambekov Umar , Gel Yulia R. 
Published: October 2018

AbstractThis paper presents a new clustering algorithm for space–time data based on the concepts of topological data analysis and, in particular, persistent homology. Employing persistent homology—a flexible mathematical tool from algebraic topology used to extract topological information from data—in unsupervised learning is an uncommon and novel approach. A notable aspect of this methodology consists in analyzing data at multiple resolutions, which allows for distinguishing true features from noise based on the extent of their persistence. We evaluate the performance of our algorithm on synthetic data and compare it to other well‐known clustering algorithms such as K‐means, hierarchical clustering, and DBSCAN (density‐based spatial clustering of applications with noise). We illustrate its application in the context of a case study of water quality in the Chesapeake Bay.
A study of links between the Arctic and the midlatitude jet stream using Granger and Pearl causality
Authors: Samarasinghe S. M. , McGraw M. C. , Barnes E. A. , Ebert‐Uphoff I. 
Published: October 2018

AbstractThis paper investigates causal links between Arctic temperatures and the jet streams. We apply two different frameworks for this application based on the concepts of (1) Granger causality and (2) Pearl causality. Both methods show that Arctic temperature and jet speed and position all exhibit strong autocorrelation, but they also show that these variables are linked together by two robust positive feedback loops that operate on time scales of 5–25 days. The dynamical implications of these feedbacks are discussed. This study is only the beginning of a larger effort to apply and compare different causality methods in order to gain a deeper understanding of the causal connections between the Arctic and weather at lower latitudes.
Brief history of anti‐seizure drug development
Authors: Rho Jong M. , White H. Steve 
Published: October 2018

SummaryThe mainstay of therapy for epilepsy is anti‐seizure drugs (ASDs, also referred to as anticonvulsants and anti‐epileptic medications). Through much of the past century, only a handful for ASDs were available for clinical use. However, with the creation of the U.S. National Institutes of Health/National Institute of Neurological Disorders and Stroke (NINDS)–sponsored Anticonvulsant Screening Program (ASP), coupled with the emergence of high‐throughput screening platforms and methodologies, and advances in our understanding of the fundamental neurobiology of epilepsy, ASD development has greatly accelerated over the past 25 years. More than 18 new ASDs have been approved for clinical use since the inception of the ASP. Despite this remarkable success and the emergence of drugs possessing more favorable pharmacokinetic profiles that act on novel molecular targets, there has been increasing recognition that the paradigms for drug discovery have not yielded significant improvements in therapeutic efficacy, and that disease modification (i.e., anti‐epileptogenesis), among other challenges, must be addressed. Thus, with the renewed framework and mission of improving the lives of people with epilepsy, the name of the ASP was changed to the Epilepsy Therapy Screening Program (ETSP). This review briefly summarizes the history of ASD development and outlines some of the challenges and opportunities for the next generation of drug therapies for the epilepsy field.
Probabilistic seismic demand assessment accounting for finite element model class uncertainty: Application to a code‐designed URM infilled reinforced concrete frame building
Authors: Alam Mohammad S. , Barbosa Andre R. 
Published: October 2018

SummaryReliable and robust probabilistic assessment of structures requires explicit consideration of all relevant sources of uncertainty, both aleatory and epistemic. This paper proposes a formulation to incorporate model class uncertainty in probabilistic seismic demand assessment (PSDA) of structures, where model class uncertainty relates to the use of different structural analysis models used to predict the physical response of structural systems. The application of the proposed formulation is illustrated through the assessment of a recent code‐designed reinforced concrete (RC) frame building with unreinforced masonry (URM) infills, which is a prevalent form of construction worldwide. The model class uncertainty analyzed in this paper is related to the potential selection of one of three state‐of‐the‐art masonry infill strut models. In the application example, nonlinear static pushover (NSP) analyses and tornado sensitivity analyses are performed to identify the important parameters of infill strut models affecting the seismic response of the infilled RC frame. A hybrid stripe analysis (HSA) approach is adopted to perform nonlinear response history analyses (NRHAs) of the example RC frame. To account for relevant sources of uncertainty, latin hypercube sampling (LHS) is used to quantify the minimum number of realizations of model parameters necessary to achieve convergence of the coefficient of variation (COV) of the engineering response parameters assessed. Results of the NRHAs indicate that incorporating model class uncertainty significantly affects the estimation of uncertainties of the drift hazard demand.
A Comparative Study on Rapid Wastewater Treatment Response to Refugee Crises
Authors: Kosonen Heta , Kim Amy , Gough Heidi , Mikola Anna , Vahala Riku 
Published: October 2018

AbstractLarge‐scale population displacement can overwhelm wastewater treatment facilities and increase environmental pollution in the host communities. Academic research has discussed features that improve wastewater treatment systems' resiliency toward other types of disasters and rapidly changing operation conditions. Concepts that contribute to successful startup, refurbishment, and operation of biological treatment systems during refugee responses are yet to be identified. This study takes a novel approach to analyzing wastewater treatment system resiliency by presenting an input–mediator–output model analysis on advanced wastewater treatment delivery during refugee response in Jordan and Finland in 2015–2016. By comparing two distinctively different case studies, the research identifies principles that contribute to timely refugee response in advanced wastewater treatment systems on the dimensions of human resources, project environment, and wastewater treatment technology. These principles include 1) clear role division between agencies and stakeholders, 2) improving “human capacity” for rapid response decisions, 3) selecting a process that fits the regulative and operational environment, 4) enabling direct and fast information sharing, and 5) establishing fast‐track permitting processes for disaster conditions. Wastewater treatment system operators, regulative authorities, and aid organizations can use these findings to support rapid decision‐making in future disaster response situations.
Generalizing polygenic risk scores from Europeans to Hispanics/Latinos
Authors: Grinde Kelsey E. , Qi Qibin , Thornton Timothy A. , Liu Simin , Shadyab Aladdin H. , Chan Kei Hang K. , Reiner Alexander P. , Sofer Tamar 
Published: October 2018

AbstractPolygenic risk scores (PRSs) are weighted sums of risk allele counts of single‐nucleotide polymorphisms (SNPs) associated with a disease or trait. PRSs are typically constructed based on published results from Genome‐Wide Association Studies (GWASs), and the majority of which has been performed in large populations of European ancestry (EA) individuals. Although many genotype‐trait associations have generalized across populations, the optimal choice of SNPs and weights for PRSs may differ between populations due to different linkage disequilibrium (LD) and allele frequency patterns. We compare various approaches for PRS construction, using GWAS results from both large EA studies and a smaller study in Hispanics/Latinos: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL, ). We consider multiple approaches for selecting SNPs and for computing SNP weights. We study the performance of the resulting PRSs in an independent study of Hispanics/Latinos from the Women’s Health Initiative (WHI, ). We support our investigation with simulation studies of potential genetic architectures in a single locus. We observed that selecting variants based on EA GWASs generally performs well, except for blood pressure trait. However, the use of EA GWASs for weight estimation was suboptimal. Using non‐EA GWAS results to estimate weights improved results.
Structural covariance across the lifespan: Brain development and aging through the lens of inter‐network relationships
Authors: Aboud Katherine S. , Huo Yuankai , Kang Hakmook , Ealey Ashley , Resnick Susan M. , Landman Bennett A. , Cutting Laurie E. 
Published: October 2018

AbstractRecent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within‐network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big‐data framework and a novel application of covariate‐adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7–90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter‐network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter‐network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter‐network structural covariance across the lifespan.
Data‐driven approaches for tau‐PET imaging biomarkers in Alzheimer's disease
Authors: Vogel Jacob W. , Mattsson Niklas , Iturria‐Medina Yasser , Strandberg Olof T. , Schöll Michael , Dansereau Christian , Villeneuve Sylvia , van der Flier Wiesje M. , Scheltens Philip , Bellec Pierre , Evans Alan C. , Hansson Oskar , Ossenkoppele Rik ,   The Alzheimer's Disease Neuroimaging Initiative,   The Swedish BioFINDER Study
Published: October 2018

AbstractPrevious positron emission tomography (PET) studies have quantified filamentous tau pathology using regions‐of‐interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post‐mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data‐driven method to identify spatial patterns of tau‐PET distribution, and to compare these patterns to previously published “pathology‐driven” ROIs. Tau‐PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [18F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [18F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [18F]AV1451 uptake in the data‐driven clusters, and in 35 previously published pathology‐driven ROIs, was extracted from ADNI [18F]AV1451 scans. We performed linear models comparing [18F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data‐driven ROIs consistently demonstrated the strongest or near‐strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data‐driven, one pathology‐driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [18F]AV1451‐PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.
tDCS modulates behavioral performance and the neural oscillatory dynamics serving visual selective attention
Authors: McDermott Timothy J. , Wiesman Alex I. , Mills Mackenzie S. , Spooner Rachel K. , Coolidge Nathan M. , Proskovec Amy L. , Heinrichs‐Graham Elizabeth , Wilson Tony W. 
Published: October 2018

AbstractTranscranial direct‐current stimulation (tDCS) is a noninvasive method for modulating human brain activity. Although there are several hypotheses about the net effects of tDCS on brain function, the field's understanding remains incomplete and this is especially true for neural oscillatory activity during cognitive task performance. In this study, we examined whether different polarities of occipital tDCS differentially alter flanker task performance and the underlying neural dynamics. To this end, 48 healthy adults underwent 20 min of anodal, cathodal, or sham occipital tDCS, and then completed a visual flanker task during high‐density magnetoencephalography (MEG). The resulting oscillatory responses were imaged in the time‐frequency domain using beamforming, and the effects of tDCS on task‐related oscillations and spontaneous neural activity were assessed. The results indicated that anodal tDCS of the occipital cortices inhibited flanker task performance as measured by reaction time, elevated spontaneous activity in the theta (4–7 Hz) and alpha (9–14 Hz) bands in prefrontal and occipital cortices, respectively, and reduced task‐related theta oscillatory activity in prefrontal cortices during task performance. Cathodal tDCS of the occipital cortices did not significantly affect behavior or any of these neuronal parameters in any brain region. Lastly, the power of theta oscillations in the prefrontal cortices was inversely correlated with reaction time. In conclusion, anodal tDCS modulated task‐related oscillations and spontaneous activity across multiple cortical areas, both near the electrode and in distant sites that were putatively connected to the targeted regions.
The impact of type 1 diabetes on neural activity serving attention
Authors: Embury Christine M. , Wiesman Alex I. , McDermott Timothy J. , Proskovec Amy L. , Heinrichs‐Graham Elizabeth , Lord Grace H. , Brau Kaitlin L. , Drincic Andjela T. , Desouza Cyrus V. , Wilson Tony W. 
Published: October 2018

AbstractType 1 diabetes has been associated with alterations in attentional processing and other cognitive functions, and previous studies have found alterations in both brain structure and function in affected patients. However, these previous neuroimaging studies have generally examined older patients, particularly those with major comorbidities known to affect functioning independent of diabetes. The primary aim of the current study was to examine the neural dynamics of selective attention processing in a young group of patients with type 1 diabetes who were otherwise healthy (i.e., without major comorbidities). Our hypothesis was that these patients would exhibit significant aberrations in attention circuitry relative to closely matched controls. The final sample included 69 participants age 19–35 years old, 35 with type 1 diabetes and 34 matched nondiabetic controls, who completed an Eriksen flanker task while undergoing magnetoencephalography. Significant group differences in flanker interference activity were found across a network of brain regions, including the anterior cingulate, inferior parietal cortices, paracentral lobule, and the left precentral gyrus. In addition, neural activity in the anterior cingulate and the paracentral lobule was correlated with disease duration in patients with type 1 diabetes. These findings suggest that alterations in the neural circuitry underlying selective attention emerge early in the disease process and are specifically related to type 1 diabetes and not common comorbidities. These findings highlight the need for longitudinal studies in large cohorts to clarify the clinical implications of type 1 diabetes on cognition and the brain.
Opposing patterns of neuronal variability in the sensorimotor network mediate cyclothymic and depressive temperaments
Authors: Conio Benedetta , Magioncalda Paola , Martino Matteo , Tumati Shankar , Capobianco Laura , Escelsior Andrea , Adavastro Giulia , Russo Daniel , Amore Mario , Inglese Matilde , Northoff Georg 
Published: October 2018

AbstractAffective temperaments have been described since the early 20th century and may play a central role in psychiatric illnesses, such as bipolar disorder (BD). However, the neuronal basis of temperament is still unclear. We investigated the relationship of temperament with neuronal variability in the resting state signal—measured by fractional standard deviation (fSD) of Blood‐Oxygen‐Level Dependent signal—of the different large‐scale networks, that is, sensorimotor network (SMN), along with default‐mode, salience and central executive networks, in standard frequency band (SFB) and its sub‐frequencies slow4 and slow5, in a large sample of healthy subject (HC, n = 109), as well as in the various temperamental subgroups (i.e., cyclothymic, hyperthymic, depressive, and irritable). A replication study on an independent dataset of 121 HC was then performed. SMN fSD positively correlated with cyclothymic z‐score and was significantly increased in the cyclothymic temperament compared to the depressive temperament subgroups, in both SFB and slow4. We replicated our findings in the independent dataset. A relationship between cyclothymic temperament and neuronal variability, an index of intrinsic neuronal activity, in the SMN was found. Cyclothymic and depressive temperaments were associated with opposite changes in the SMN variability, resembling changes previously described in manic and depressive phases of BD. These findings shed a novel light on the neural basis of affective temperament and also carry important implications for the understanding of a potential dimensional continuum between affective temperaments and BD, on both psychological and neuronal levels.
Tri(1‐naphthyl)phosphine as a ligand in palladium‐free Sonogashira cross‐coupling of arylhalogenides with acetylenes
Authors: Govdi Anastasiya I. , Vasilevsky Sergey F. , Malysheva Svetlana F. , Kazheva Olga N. , Dyachenko Oleg A. , Kuimov Vladimir A. 
Published: October 2018

AbstractTri(1‐naphthyl)phosphine (Np3P) has been easily prepared in 34% yield from red phosphorus and 1‐bromonaphthalene in the superbasic system t‐BuONa/DMSO. The expedient procedures for the synthesis of aryl acetylenes by Sonogashira coupling of aryl iodides with terminal alkynes using Np3P as a ligand have been developed. For the first time, it is found that the reaction with compounds containing electron‐donating substituents preferably affords buta‐1,3‐diynes.