bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rational Design of Fe/N/S‐Doped Nanoporous Carbon Catalysts from Covalent Triazine Frameworks for Efficient Oxygen Reduction
Authors: Zhu Yuanzhi , Chen Xifan , Liu Jing , Zhang Junfeng , Xu DanYun , Peng Wenchao , Li Yang , Zhang Guoliang , Zhang Fengbao , Fan Xiaobin 
Published: May 2018

AbstractPorous organic polymers (POPs) are promising precursors for developing high performance transition metal–nitrogen–carbon (M/N/C) catalysts for the oxygen reduction reaction (ORR). The rational design of POP precursors remain a great challenge, because of the elusive structural association between the sacrificial POPs and the final M/N/C catalysts. Based on covalent triazine frameworks (CTFs), we developed a series of S‐doped Fe/N/C catalysts by selecting six different aromatic nitriles as building blocks. A new mixed solvent of molten FeCl3 and S was used for CTF polymerization, which benefited the formation of Fe–Nx sites and made the subsequent pyrolysis process more convenient. Comprehensive study of these CTF‐derived catalysts showed that their ORR activities are not directly dependent on the theoretical N/C ratio of the building block, but closely correlated to the ratio of the nitrile group to benzene ring (Nnitrile/Nbenzene) and geometries of the building blocks. The high ratios of Nnitrile/Nbenzene are crucial for ORR activity of the final catalysts owing to the formation of more N‐doped micropores and Fe–Nx sites in pyrolysis possess. The optimized catalyst shows high ORR performances in acid and superior ORR activity to the Pt/C catalysts under alkaline conditions.
When beggars are choosers—How nesting of a solitary bee is affected by temporal dynamics of pollen plants in the landscape
Authors: Persson Anna S. , Mazier Florence , Smith Henrik G. 
Published: May 2018

AbstractWild bees are declining in intensively farmed regions worldwide, threatening pollination services to flowering crops and wild plants. To halt bee declines, it is essential that conservation actions are based on a mechanistic understanding of how bee species utilize landscapes. We aimed at teasing apart how foraging resources in the landscape through the nesting season affected nesting and reproduction of a solitary bee in a farmland region. We investigated how availability of floral resources and potentially resource‐rich habitats surrounding nests affected nest provisioning and reproduction in the solitary polylectic bee Osmia bicornis. The study was performed in 18 landscape sectors dominated by agriculture, but varying in agricultural intensity in terms of proportion of organic crop fields and seminatural permanent pastures. Pasture‐rich sectors contained more oak (Quercus robur), which pollen analysis showed to be favored forage in early season. More oaks ≤100 m from nests led to higher proportions of oak pollen in nest provisions and increased speed of nest construction in early season, but this effect tapered off as flowering decreased. Late‐season pollen foraging was dominated by buttercup (Ranunculus spp.), common in various noncrop habitats. Foraging trips were longer with more oaks and increased further through the season. The opposite was found for buttercup. Oak and buttercup interacted to explain the number of offspring; buttercup had a positive effect only when the number of oaks was above the mean for the studied sectors. The results show that quality of complex and pasture‐rich landscapes for O. bicornis depends on preserving existing and generating new oak trees. Lignose plants are key early‐season forage resources in agricultural landscapes. Increasing habitat heterogeneity with trees and shrubs and promoting suitable late‐flowering forbs can benefit O. bicornis and other wild bees active in spring and early summer, something which existing agri‐environment schemes seldom target.
Automating analysis of vegetation with computer vision: Cover estimates and classification
Authors: McCool Chris , Beattie James , Milford Michael , Bakker Jonathan D. , Moore Joslin L. , Firn Jennifer 
Published: May 2018

AbstractThis study develops an approach to automating the process of vegetation cover estimates using computer vision and pattern recognition algorithms. Visual cover estimation is a key tool for many ecological studies, yet quadrat‐based analyses are known to suffer from issues of consistency between people as well as across sites (spatially) and time (temporally). Previous efforts to estimate cover from photograps require considerable manual work. We demonstrate that an automated system can be used to estimate vegetation cover and the type of vegetation cover present using top–down photographs of 1 m by 1 m quadrats. Vegetation cover is estimated by modelling the distribution of color using a multivariate Gaussian. The type of vegetation cover is then classified, using illumination robust local binary pattern features, into two broad groups: graminoids (grasses) and forbs. This system is evaluated on two datasets from the globally distributed experiment, the Nutrient Network (NutNet). These NutNet sites were selected for analyses because repeat photographs were taken over time and these sites are representative of very different grassland ecosystems—a low stature subalpine grassland in an alpine region of Australia and a higher stature and more productive lowland grassland in the Pacific Northwest of the USA. We find that estimates of treatment effects on grass and forb cover did not differ between field and automated estimates for eight of nine experimental treatments. Conclusions about total vegetation cover did not correspond quite as strongly, particularly at the more productive site. A limitation with this automated system is that the total vegetation cover is given as a percentage of pixels considered to contain vegetation, but ecologists can distinguish species with overlapping coverage and thus can estimate total coverage to exceed 100%. Automated approaches such as this offer techniques for estimating vegetation cover that are repeatable, cheaper to use, and likely more reliable for quantifying changes in vegetation over the long‐term. These approaches would also enable ecologists to increase the spatial and temporal depth of their coverage estimates with methods that allow for vegetation sampling over large spatial scales quickly.
Male‐biased dispersal and the potential impact of human‐induced habitat modifications on the Neotropical batTrachops cirrhosus
Authors: Halczok Tanja K. , Brändel Stefan D. , Flores Victoria , Puechmaille Sébastien J. , Tschapka Marco , Page Rachel A. , Kerth Gerald 
Published: May 2018

AbstractGene flow, maintained through natal dispersal and subsequent mating events, is one of the most important processes in both ecology and population genetics. Among mammalian populations, gene flow is strongly affected by a variety of factors, including the species’ ability to disperse, and the composition of the environment which can limit dispersal. Information on dispersal patterns is thus crucial both for conservation management and for understanding the social system of a species. We used 16 polymorphic nuclear microsatellite loci in addition to mitochondrialDNAsequences (1.61 kbp) to analyse the population structure and the sex‐specific pattern of natal dispersal in the frog‐eating fringe‐lipped bat,Trachops cirrhosus, in Central Panama. Our study revealed that—unlike most of the few other investigated Neotropical bats—gene flow in this species is mostly male‐mediated. Nevertheless, distinct genetic clusters occur in both sexes. In particular, the presence of genetic differentiation in the dataset only consisting of the dispersing sex (males) indicates that gene flow is impeded within our study area. Our data are in line with the Panama Canal in connection with the widening of the Río Chagres during the canal construction acting as a recent barrier to gene flow. The sensitivity ofT. cirrhosusto human‐induced habitat modifications is further indicated by an extremely low capture success in highly fragmented areas. Taken together, our genetic and capture data provide evidence for this species to be classified as less mobile and thus vulnerable to habitat change, information that is important for conservation management.
A guide to Bayesian model checking for ecologists
Authors: Conn Paul B. , Johnson Devin S. , Williams Perry J. , Melin Sharon R. , Hooten Mevin B. 
Published: May 2018

AbstractChecking that models adequately represent data is an essential component of applied statistical inference. Ecologists increasingly use hierarchical Bayesian statistical models in their research. The appeal of this modeling paradigm is undeniable, as researchers can build and fit models that embody complex ecological processes while simultaneously accounting for observation error. However, ecologists tend to be less focused on checking model assumptions and assessing potential lack of fit when applying Bayesian methods than when applying more traditional modes of inference such as maximum likelihood. There are also multiple ways of assessing the fit of Bayesian models, each of which has strengths and weaknesses. For instance, BayesianPvalues are relatively easy to compute, but are well known to be conservative, producingPvalues biased toward 0.5. Alternatively, lesser known approaches to model checking, such as prior predictive checks, cross‐validation probability integral transforms, and pivot discrepancy measures may produce more accurate characterizations of goodness‐of‐fit but are not as well known to ecologists. In addition, a suite of visual and targeted diagnostics can be used to examine violations of different model assumptions and lack of fit at different levels of the modeling hierarchy, and to check for residual temporal or spatial autocorrelation. In this review, we synthesize existing literature to guide ecologists through the many available options for Bayesian model checking. We illustrate methods and procedures with several ecological case studies including (1) analysis of simulated spatiotemporal count data, (2) N‐mixture models for estimating abundance of sea otters from an aircraft, and (3) hidden Markov modeling to describe attendance patterns of California sea lion mothers on a rookery. We find that commonly used procedures based on posterior predictivePvalues detect extreme model inadequacy, but often do not detect more subtle cases of lack of fit. Tests based on cross‐validation and pivot discrepancy measures (including the “sampled predictivePvalue”) appear to be better suited to model checking and to have better overall statistical performance. We conclude that model checking is necessary to ensure that scientific inference is well founded. As an essential component of scientific discovery, it should accompany most Bayesian analyses presented in the literature.
Implantable cardioverter‐defibrillators in heart failure patients with reduced ejection fraction and diabetes
Authors: Sharma Abhinav , Al‐Khatib Sana M. , Ezekowitz Justin A. , Cooper Lauren B. , Fordyce Christopher B. , Michael Felker G. , Bardy Gust H. , Poole Jeanne E. , Thomas Bigger J. , Buxton Alfred E. , Moss Arthur J. , Friedman Daniel J. , Lee Kerry L. , Steinman Richard , Dorian Paul , Cappato Riccardo , Kadish Alan H. , Kudenchuk Peter J. , Mark Daniel B. , Peterson Eric D. , Inoue Lurdes Y.T. , Sanders Gillian D. 
Published: May 2018

AimThere is limited information on the outcomes after primary prevention implantable cardioverter‐defibrillator (ICD) implantation in patients with heart failure (HF) and diabetes. This analysis evaluates the effectiveness of a strategy of ICD plus medical therapy vs. medical therapy alone among patients with HF and diabetes.Methods and resultsA patient‐level combined‐analysis was conducted from a combined dataset that included four primary prevention ICD trials of patients with HF or severely reduced ejection fractions: Multicenter Automatic Defibrillator Implantation Trial I (MADIT I), MADIT II, Defibrillators in Non‐Ischemic Cardiomyopathy Treatment Evaluation (DEFINITE), and Sudden Cardiac Death in Heart Failure Trial (SCD‐HeFT). In total, 3359 patients were included in the analysis. The primary outcome of interest was all‐cause death. Compared with patients without diabetes (n = 2363), patients with diabetes (n = 996) were older and had a higher burden of cardiovascular risk factors. During a median follow‐up of 2.6 years, 437 patients without diabetes died (178 with ICD vs. 259 without) and 280 patients with diabetes died (128 with ICD vs. 152 without). ICDs were associated with a reduced risk of all‐cause mortality among patients without diabetes [hazard ratio (HR) 0.56, 95% confidence interval (CI) 0.46–0.67] but not among patients with diabetes (HR 0.88, 95% CI 0.7–1.12; interaction P = 0.015).ConclusionAmong patients with HF and diabetes, primary prevention ICD in combination with medical therapy vs. medical therapy alone was not significantly associated with a reduced risk of all‐cause death. Further studies are needed to evaluate the effectiveness of ICDs among patients with diabetes.
Development of Impedimetric Immunosensors for the Diagnosis of DOCK8 and STAT3 Related Hyper‐Immunoglobulin E Syndrome
Authors: Eissa Shimaa , Abdulkarim Haya , Hawalta Ibrahim , Jacob Minnie , Dasouki Majed , Rahman Anas Abdel , Zourob Mohammed 
Published: May 2018

AbstractHyper Immunoglobulin E syndrome (HIES) is a rare inherited inborn error of primary immunodeficiency. HIES is characterized by high levels of serum immunoglobulin E (IgE), severe eczema, which overlap with atopic dermatitis, as well as lung infections and even death. HIESs are genetically inherited by heterozygous or homozygous mutations in signal transducer and activator of transcription 3 (STAT3) or Dedicator of cytokinesis 8 (DOCK8) genes, in autosomal dominant and recessive forms, respectively. Therefore, the early detection of DOCK8 and STAT3 protein levels in humans would facilitate the early diagnosis of these disorders and thus, help in the disease management. Here, we present the development of immunosensors for the detection of DOCK8 and STAT3 using electrochemical impedance spectroscopy in a label‐free format. The immunosensors were fabricated by the covalent attachment of specific antibodies for DOCK8 and STAT3 on gold electrodes via cysteamine/phenylene diisothiocyanate linkers. The detection was achieved by monitoring the change in the charge transfer resistance (Rct) of ferro/ferricyanide redox couple upon binding of the proteins to the immunosensor surface. These biosensors enabled the detection of DOCK8 and STAT3 levels with low detection limits of 1.2 and 9.0 pg/ml, respectively. The immunosensor was also applied for the detection of DOCK8 in human serum samples showing high recovery percentages which indicates great promise of this method for early diagnosis of HIES in newborn infants.
Preparative free‐flow electrophoresis, a versatile technology complementing gradient centrifugation in the isolation of highly purified cell organelles
Authors: Islinger Markus , Wildgruber Robert , Völkl Alfred 
Published: May 2018

AbstractFree‐flow electrophoresis (FFE) exploits differences in the overall charge of bio‐particles separating cells, organelles, macromolecules, ions, etc. according to their distinct electrophoretic mobility and isoelectric point (pI) values. Indeed, around a neutral pH organelles usually exhibit a negative surface charge, migrating in an electric field from the cathode toward the anode. Since its introduction more than five decades ago by Barrollier et al., Z. Naturforsch. 1958, 13b, 745–755 and Hannig, Z. Anal. Chem. 1961, 181, 244–254, FFE has become an established analytical and preparative separation method for the isolation of a variety of organelles. Particularly, in sophisticated, multistep separating processes to separate subpopulations of organelles, it has gained, meanwhile, a position as a versatile technology and essential element. Relying on the distinct surface charges instead of buoyant densities of cell organelles, the FFE technology is best supporting a preceding centrifugation‐based fractionation of subcellular compartments in the second dimension. In the following review, the two‐step isolation and purification of subpopulations of classic animal and plant cell organelles will be mainly exemplified.
Zinc toxicity to Daphnia magna in a two-species microcosm can be predicted from single-species test data: The effects of phosphorus supply and pH
Authors: Fettweis Andreas , De Schamphelaere Karel , Smolders Erik 
Published: May 2018

Abstract

Ecological interactions and abiotic stress factors may significantly affect species sensitivities to toxicants, and these are not incorporated in standard single-species tests. The present study tests whether a model, calibrated solely on single-species data, can explain abiotic stress factors in a two-species microcosm, a test applied to the effects of nutritional stress (phosphorus [P] limitation) on zinc (Zn) toxicity to Daphnia magna. A population model was developed based on P- and Zn-dependent algal and daphnid growth. Two separate two-species (phytoplankton Pseudokirchneriella subcapitata and consumer D. magna) microcosm experiments with P × Zn factorial combinations and a different pH (7.3 and 7.8) were set up to validate the model. The 21-d daphnid population size was considerably reduced by increased Zn and by decreased P supply, with a significant (p &lt; 0.001) interaction between the 2 factors. The observed median effective concentration (EC50) of Zn on D. magna population size varied 12-fold (25 to 310 μg Zn L−1), with the lowest EC50 values found at the highest pH and high P treatments. For both experiments, Zn toxicity to D. magna was correctly predicted within a factor of 2 for EC50 values, and this is explained by the model through 1) a higher phytoplankton Zn sensitivity at higher pH, affecting food supply to D. magna, and 2) an increased algal P content at higher Zn, offering a nutritional benefit to daphnids that counteracts direct Zn toxicity under P limitation. The present study illustrates that indirect effects of Zn via producer–consumer relationships can outweigh the direct toxic effects and that models calibrated solely on single-species test data can help with interpreting these results in two-species systems. Environ Toxicol Chem 2018;37:2153–2164. © 2018 SETAC
Abstract
Graphical abstract showing how the use of a model, calibrated on single-species tests data only, can help in understanding the underlying mechanisms of the interaction between Zn and P in a two-species system (Pseudokirchneriella subcapitata and Daphnia magna).
Oregano powder substitution and shelf life in pork chorizo using Mexican oregano essential oil
Authors: Perales‐Jasso Yessica J. , Gamez‐Noyola Stephannie A. , Aranda‐Ruiz Juana , Hernandez‐Martinez Carlos A. , Gutierrez‐Soto Guadalupe , Luna‐Maldonado Alejandro I. , Silva‐Vazquez Ramon , Hume Michael E. , Mendez‐Zamora Gerardo 
Published: May 2018

AbstractThe aim of this study was to evaluate the effect of oregano essential oil (MOO) from Mexican oregano, Lippia berlandieri Schauer, as substitute for Mexican oregano powder (MOP) on pork chorizo physicochemical characteristics, texture, antioxidant capacity, aerobic bacteria colony counts, and sensory evaluation under storage conditions over 7 d. The treatments were T1 = chorizo + 0.1% MOP and T2 = chorizo + 0.1% MOO. The pH, redness (a*), yellowness (b*), Chroma, and browning index (BI) were affected by treatments and storage time. T2 presented lower pH (5.27) at d 1 than at d 7 (5.34), as well as a* (23.13 vs. 25.27), b* (14.85 vs. 17.45), Chroma (28.60 vs. 30.79), and BI (103.42 vs. 109.82) were higher at d 7. At d 1, hardness (1392.75 vs. 872.29 g), springiness (0.3675 vs. 0.3351 mm), gumminess (491.45 vs. 284.38 g), and chewiness (180.25 vs. 95.43 g mm) were higher in T1 than T2. Aerobic bacteria counts (T1—4.19 vs. 4.73 log CFU/g and T2—4.37 vs. 4.50 log CFU/g, respectively) increased within each treatment at d 7. Antioxidant capacity was not affected (26.48 and 27.42%). Oregano odor was different at 7 d with T2 having a stronger odor (5.70) than T1 with oregano powder (4.63). Mexican oregano essential oil in the pork chorizo formulation improved pH, color parameters, textural profile, and sensory characteristics.
Cognitive and functional progression in Alzheimer disease: A prediction model of latent classes
Authors: Haaksma Miriam L. , Calderón-Larrañaga Amaia , Olde Rikkert Marcel G.M. , Melis René J.F. , Leoutsakos Jeannie‐Marie S. 
Published: May 2018

ObjectiveWe sought to replicate a previously published prediction model for progression, developed in the Cache County Dementia Progression Study, using a clinical cohort from the National Alzheimer's Coordinating Center.MethodsWe included 1120 incident Alzheimer disease (AD) cases with at least one assessment after diagnosis, originating from 31 AD centres from the United States. Trajectories of the Mini‐Mental State Examination (MMSE) and Clinical Dementia Rating sum of boxes (CDR‐sb) were modelled jointly over time using parallel‐process growth mixture models in order to identify latent classes of trajectories. Bias‐corrected multinomial logistic regression was used to identify baseline predictors of class membership and compare these with the predictors found in the Cache County Dementia Progression Study.ResultsThe best‐fitting model contained 3 classes: Class 1 was the largest (63%) and showed the slowest progression on both MMSE and CDR‐sb; classes 2 (22%) and 3 (15%) showed moderate and rapid worsening, respectively. Significant predictors of membership in classes 2 and 3, relative to class 1, were worse baseline MMSE and CDR‐sb, higher education, and lack of hypertension. Combining all previously mentioned predictors yielded areas under the receiver operating characteristic curve of 0.70 and 0.75 for classes 2 and 3, respectively, relative to class 1.ConclusionsOur replication study confirmed that it is possible to predict trajectories of progression in AD with relatively good accuracy. The class distribution was comparable with that of the original study, with most individuals being members of a class with stable or slow progression. This is important for informing newly diagnosed AD patients and their caregivers.
Preoperative predictors of occult nodal disease in cT1N0 oral cavity squamous cell carcinoma: Review of 2623 cases
Authors: Zhan Kevin Y. , Morgan Patrick F. , Neskey David M. , Kim Joanne J. , Huang Andrew T. , Garrett‐Mayer Elizabeth , Day Terry A. 
Published: May 2018

AbstractBackgroundNodal disease predicts survival in oral cavity squamous cell carcinoma (SCC). Currently, no large studies on predictors of occult nodal disease in cT1N0 oral cavity SCC exist.MethodsThe National Cancer Database (NCDB) review for cT1N0 oral cavity SCC with surgical resection and elective neck dissection (END).ResultsThe number of patients found with occult nodal disease was 2623 (15.1%). In multivariable regression, female sex and tumor differentiation predict occult nodal disease. Occult nodal disease incidence was 5.9% in well‐differentiated tumors, 17.4% in moderately differentiated tumors, and 28.5% in poorly differentiated tumor (P < .001). Women with oral tongue tumors had higher occult nodal disease (19.1%) than men (12%; P = .001). Adjusted odds ratios (aORs) for occult nodal disease in women were: aOR 1.26; 95% confidence interval (CI) 1.01‐1.59; P = .045; moderately differentiated aOR 3.52; 95% CI 2.47‐5.01; P < .001; and poorly differentiated aOR 6.25; 95% CI 4.17‐9.38; P < .001.ConclusionSex and tumor differentiation significantly predict occult nodal disease. END is recommended for all moderately and poorly differentiated cT1N0 oral cavity SCC, regardless of the depth of invasion. One can consider not performing END in well‐differentiated tumors.