Text and Graph Based Approach for Analyzing Patterns of Research Collaboration: An analysis of the TrueImpactDataset

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper, we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how frequently have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset (Herrmannova et al., 2017) (Herrmannova et al., 2017), a new dataset containing two types of publications – publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information

Research Collaboration Analysis Using Text and Graph Features

Patterns of scientific collaboration and their effect on scientific production have been the subject of many studies. In this paper we analyze the nature of ties between co-authors and study collaboration patterns in science from the perspective of semantic similarity of authors who wrote a paper together and the strength of ties between these authors (i.e. how much have they previously collaborated together). These two views of scientific collaboration are used to analyze publications in the TrueImpactDataset [11], a new dataset containing two types of publications - publications regarded as seminal and publications regarded as literature reviews by field experts. We show there are distinct differences between seminal publications and literature reviews in terms of author similarity and the strength of ties between their authors. In particular, we find that seminal publications tend to be written by authors who have previously worked on dissimilar problems (i.e. authors from different fields or even disciplines), and by authors who are not frequent collaborators. On the other hand, literature reviews in our dataset tend to be the result of an established collaboration within a discipline. This demonstrates that our method provides meaningful information about potential future impacts of a publication which does not require citation information

A calcitonin receptor (CALCR) single nucleotide polymorphism is associated with growth performance and bone integrity in response to dietary phosphorus deficiency

Although concerns over the environmental impact of excess P in the excreta from pig production and governmental regulations have driven research toward reducing dietary supplementation of P to swine diets for over a decade, recent dramatic increases in feed costs have further motivated researchers to identify means to further reduce dietary P supplementation. We have demonstrated that genetic background impacts P utilization in young pigs and have identified genetic polymorphisms in several target genes related to mineral utilization. In this study, we examined the impact of a SNP in the calcitonin receptor gene (CALCR) on P utilization in growing pigs. In Exp. 1, 36 gilts representing the 3 genotypes identified by this CALCR SNP (11, 12, and 22) were fed a P-adequate (PA) or a marginally P-deficient (approximately 20% less available P; PD) diet for 14 wk. As expected, P deficiency reduced plasma P concentration, bone strength, and mineral content (P \u3c 0.05). However, the dietary P deficiency was mild enough to not affect the growth performance of these pigs. A genotype × dietary P interaction (P \u3c 0.05) was observed in measures of bone integrity and mineral content, with the greatest reduction in bone strength and mineral content due to dietary P deficiency being associated with the allele 1. In Exp. 2, 168 pigs from a control line and low residual feed intake (RFI) line were genotyped for the CALCR SNP and fed a PA diet. As expected, pigs from the low RFI line consumed less feed but also gained less BW when compared with the control line (P \u3c 0.05). Although ADFI did not differ between genotypes, pigs having the 11 genotype gained less BW (P \u3c 0.05) than pigs having the 12 or 22 genotypes. Pigs of the 11 and 12 genotypes had bones that tolerated greater load when compared with animals having the 22 genotype (P \u3c 0.05). A similar trend was observed in bone modulus and ash % (P \u3c 0.10). These data are supportive of the association of this CALCR SNP with bone integrity and its response to dietary P restriction. Although the allele 1 is associated with greater bone integrity and mineral content during adequate P nutrition, it is also associated with the greatest loss in bone integrity and mineral content in response to dietary P restriction. Understanding the underlying genetic mechanisms that regulate P utilization may lead to novel strategies to produce more environmentally friendly pigs

Occurrence of Salmonella-Specific Bacteriophages in Swine Feces Collected from Commercial Farms

Salmonella is one of the leading causes of human foodborne illness and is associated with swine production. Bacteriophages are naturally occurring viruses that prey on bacteria and have been suggested as a potential intervention strategy to reduce Salmonella levels in food animals on the farm and in the lairage period. If phages are to be used to improve food safety, then we must understand the incidence and natural ecology of both phages and their hosts in the intestinal environment. This study investigates the incidence of phages that are active against Salmonella spp. in the feces of commercial finishing swine. Fecal samples (n = 60) were collected from each of 10 commercial swine finishing operations. Samples were collected from 10 randomly selected pens throughout each operation; a total of 600 fecal samples were collected. Salmonella spp. were found in 7.3% (44/600) of the fecal samples. Bacteriophages were isolated from fecal samples through two parallel methods: (1) initial enrichment in Salmonella Typhimurium; (2) initial enrichment in Escherichia coli B (an indicator strain), followed by direct spot testing against Salmonella Typhimurium. Bacteriophages active against Salmonella Typhimurium were isolated from 1% (6/600) of the individual fecal samples when initially enriched in Salmonella Typhimurium, but E. coli B-killing phages were isolated from 48.3% (290/600) of the fecal samples and only two of these phages infected Salmonella Typhimurium on secondary plating. Collectively, our results indicate that bacteriophages are widespread in commercial swine, but those capable of killing Salmonella Typhimurium may be present at relatively low population levels. These results indicate that phages (predator) populations may vary along with Salmonella (prey) populations; and that phages could potentially be used as a food safety pathogen reduction strategy in swine

Use of fecal volatile organic compound analysis to discriminate between nonvaccinated and BCG-Vaccinated cattle prior to and after \u3ci\u3eMycobacterium bovis\u3c/i\u3e challenge

Bovine tuberculosis is a zoonotic disease of global public health concern. Development of diagnostic tools to improve test accuracy and efficiency in domestic livestock and enable surveillance of wildlife reservoirs would improve disease management and eradication efforts. Use of volatile organic compound analysis in breath and fecal samples is being developed and optimized as a means to detect disease in humans and animals. In this study we demonstrate that VOCs present in fecal samples can be used to discriminate between non-vaccinated and BCG-vaccinated cattle prior to and after Mycobacterium bovis challenge

Use of fecal volatile organic compound analysis to discriminate between nonvaccinated and BCG-Vaccinated cattle prior to and after \u3ci\u3eMycobacterium bovis\u3c/i\u3e challenge

Bovine tuberculosis is a zoonotic disease of global public health concern. Development of diagnostic tools to improve test accuracy and efficiency in domestic livestock and enable surveillance of wildlife reservoirs would improve disease management and eradication efforts. Use of volatile organic compound analysis in breath and fecal samples is being developed and optimized as a means to detect disease in humans and animals. In this study we demonstrate that VOCs present in fecal samples can be used to discriminate between non-vaccinated and BCG-vaccinated cattle prior to and after Mycobacterium bovis challenge

Enhanced AGAMOUS expression in the centre of the Arabidopsis flower causes ectopic expression over its outer expression boundaries

Spatial regulation of C-function genes controlling reproductive organ identity in the centre of the flower can be achieved by adjusting the level of their expression within the genuine central expression domain in Antirrhinum and Petunia. Loss of this control in mutants is revealed by enhanced C-gene expression in the centre and by lateral expansion of the C-domain. In order to test whether the level of central C-gene expression and hence the principle of ‘regulation by tuning’ also applies to spatial regulation of the C-function gene AGAMOUS (AG) in Arabidopsis, we generated transgenic plants with enhanced central AG expression by using stem cell-specific CLAVATA3 (CLV3) regulatory sequences to drive transcription of the AG cDNA. The youngest terminal flowers on inflorescences of CLV3::AG plants displayed homeotic features in their outer whorls indicating ectopic AG expression. Dependence of the homeotic feature on the age of the plant is attributed to the known overall weakening of repressive mechanisms controlling AG. Monitoring AG with an AG-I::GUS reporter construct suggests ectopic AG expression in CLV3::AG flowers when AG in the inflorescence is still repressed, although in terminating inflorescence meristems, AG expression expands to all tissues. Supported by genetic tests, we conclude that upon enhanced central AG expression, the C-domain laterally expands necessitating tuning of the expression level of C-function genes in the wild type. The tuning mechanism in C-gene regulation in Arabidopsis is discussed as a late security switch that ensures wild-type C-domain control when other repressive mechanism starts to fade and fail

Robust automated detection of microstructural white matter degeneration in Alzheimer’s disease using machine learning classification of multicenter DTI data

Diffusion tensor imaging (DTI) based assessment of white matter fiber tract integrity can support the diagnosis of Alzheimer’s disease (AD). The use of DTI as a biomarker, however, depends on its applicability in a multicenter setting accounting for effects of different MRI scanners. We applied multivariate machine learning (ML) to a large multicenter sample from the recently created framework of the European DTI study on Dementia (EDSD). We hypothesized that ML approaches may amend effects of multicenter acquisition. We included a sample of 137 patients with clinically probable AD (MMSE 20.6±5.3) and 143 healthy elderly controls, scanned in nine different scanners. For diagnostic classification we used the DTI indices fractional anisotropy (FA) and mean diffusivity (MD) and, for comparison, gray matter and white matter density maps from anatomical MRI. Data were classified using a Support Vector Machine (SVM) and a Naïve Bayes (NB) classifier. We used two cross-validation approaches, (i) test and training samples randomly drawn from the entire data set (pooled cross-validation) and (ii) data from each scanner as test set, and the data from the remaining scanners as training set (scanner-specific cross-validation). In the pooled cross-validation, SVM achieved an accuracy of 80% for FA and 83% for MD. Accuracies for NB were significantly lower, ranging between 68% and 75%. Removing variance components arising from scanners using principal component analysis did not significantly change the classification results for both classifiers. For the scanner-specific cross-validation, the classification accuracy was reduced for both SVM and NB. After mean correction, classification accuracy reached a level comparable to the results obtained from the pooled cross-validation. Our findings support the notion that machine learning classification allows robust classification of DTI data sets arising from multiple scanners, even if a new data set comes from a scanner that was not part of the training sample

Assessing responsible innovation training

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI

Particulate Oxalate-To-Sulfate Ratio as an Aqueous Processing Marker: Similarity Across Field Campaigns and Limitations

Leveraging aerosol data from multiple airborne and surface-based field campaigns encompassing diverse environmental conditions, we calculate statistics of the oxalate-sulfate mass ratio (median: 0.0217; 95% confidence interval: 0.0154–0.0296; R = 0.76; N = 2,948). Ground-based measurements of the oxalate-sulfate ratio fall within our 95% confidence interval, suggesting the range is robust within the mixed layer for the submicrometer particle size range. We demonstrate that dust and biomass burning emissions can separately bias this ratio toward higher values by at least one order of magnitude. In the absence of these confounding factors, the 95% confidence interval of the ratio may be used to estimate the relative extent of aqueous processing by comparing inferred oxalate concentrations between air masses, with the assumption that sulfate primarily originates from aqueous processing