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Introduction Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers’ impact within the research field. Methods Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set. Results DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p Discussion Analysis of the neural network shows that the nature of collaboration strongly impacts an author’s research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy.

Morphological classification of living things has challenged science for several centuries and has led to a wide range of objective morphometric approaches in data gathering and analysis. In this paper we explore those methods using apple cultivars, a model biological system in which discrete groups are pre-defined but in which there is a high level of overall morphological similarity. The effectiveness of morphometric techniques in discovering the groups is evaluated using statistical learning tools. No one technique proved optimal in classification on every occasion, linear morphometric techniques slightly out-performing geometric (72.6% accuracy on test set versus 66.7%). The combined use of these techniques with post-hoc knowledge of their individual successes with particular cultivars achieves a notably higher classification accuracy (77.8%). From this we conclude that even with pre-determined discrete categories, a range of approaches is needed where those categories are intrinsically similar to each other, and we raise the question of whether in studies where potentially continuous natural variation is being categorised the level of match between categories is routinely set too high.

This paper aims to describe the spatial-temporal variability in catch of the main fishery resources of the Amazon River and floodplain lakes of the Lower Amazon, as well as relating the Catch per Unit of Effort with anomalies of some of the Amazon River, atmosphere and Atlantic Ocean system variables, determining the influence of the environment on the Amazonian fishery resources. Finfish landings data from the towns and villages of the Lower Amazon for the fisheries of three sites (Óbidos, Santarém and Monte Alegre), were obtained for the period between January 1993 and December 2004. Analysis of variance, detrended correspondence analysis, redundancy analysis and multiple regression techniques were used for the statistical analysis of the distinct time series. Fisheries production in the Lower Amazon presents differences between the Amazon River and the floodplain lakes. Production in the Amazon River is approximately half of the one of the floodplain lakes. This variability occurs both along the Lower Amazon River region (longitudinal gradient) and laterally (latitudinal gradient) for every fishing ground studied here. The distinct environmental variables alone or in association act differently on the fishery stocks and the success of catches in each fishery group studied here. Important variables are the flooding events; the soil the sea surface temperatures; the humidity; the wind and the occurence of El Niño-Southern Oscillation events. Fishery productivity presents a large difference in quantity and distribution patterns between the river and floodplain lakes. This variability occurs in the region of the Lower Amazon as well as laterally for each fishery group studied, being dependent on the ecological characteristics and life strategies of each fish group considered here.

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Academic research is increasingly cross-disciplinary and collaborative, between and within institutions. In this context, what is the role and relevance of an individual’s spatial position on a campus? We examine the collaboration patterns of faculty at the Massachusetts Institute of Technology, through their academic output (papers and patents), and their organizational structures (institutional affiliation and spatial configuration) over a 10-year time span. An initial comparison of output types reveals: 1. diverging trends in the composition of collaborative teams over time (size, faculty versus non-faculty, etc.); and 2. substantively different patterns of cross-building and cross-disciplinary collaboration. We then construct a multi-layered network of authors, and find two significant features of collaboration on campus: 1. a network topology and community structure that reveals spatial versus institutional collaboration bias; and 2. a persistent relationship between proximity and collaboration, well fit with an exponential decay model. This relationship is consistent for both papers and patents, and present also in exclusively cross-disciplinary work. These insights contribute an architectural dimension to the field of scientometrics, and take a first step toward empirical space-planning policy that supports collaboration within institutions.

The pervasiveness of mobile devices, which is increasing daily, is generating a vast amount of geo-located data allowing us to gain further insights into human behaviors. In particular, this new technology enables users to communicate through mobile social media applications, such as Twitter, anytime and anywhere. Thus, geo-located tweets offer the possibility to carry out in-depth studies on human mobility. In this paper, we study the use of Twitter in transportation by identifying tweets posted from roads and rails in Europe between September 2012 and November 2013. We compute the percentage of highway and railway segments covered by tweets in 39 countries. The coverages are very different from country to country and their variability can be partially explained by differences in Twitter penetration rates. Still, some of these differences might be related to cultural factors regarding mobility habits and interacting socially online. Analyzing particular road sectors, our results show a positive correlation between the number of tweets on the road and the Average Annual Daily Traffic on highways in France and in the UK. Transport modality can be studied with these data as well, for which we discover very heterogeneous usage patterns across the continent., Partial financial support has been received from the Spanish Ministry of Economy (MINECO) and FEDER (EU) under projects MODASS (FIS2011-24785) and INTENSE@COSYP (FIS2012-30634), and from the EU Commission through projects EUNOIA, LASAGNE and INSIGHT. ML acknowledges funding from the Conselleria d'Educacio, Cultura i Universitats of the Government of the Balearic Islands and JJR from the Ramon y Cajal program of MINECO.

textabstractBackground: Italy has a population of 60 million and a universal coverage single-payer healthcare system, which mandates collection of healthcare administrative data in a uniform fashion throughout the country. On the other hand, organization of the health system takes place at the regional level, and local initiatives generate natural experiments. This is happening in particular in primary care, due to the need to face the growing burden of chronic diseases. Health services research can compare and evaluate local initiatives on the basis of the common healthcare administrative data.However reliability of such data in this context needs to be assessed, especially when comparing different regions of the country. In this paper we investigated the validity of healthcare administrative databases to compute indicators of compliance with standards of care for diabetes, ischaemic heart disease (IHD) and heart failure (HF). Methods: We compared indicators estimated from healthcare administrative data collected by Local Health Authorities in five Italian regions with corresponding estimates from clinical data collected by General Practitioners (GPs). Four indicators of diagnostic follow-up (two for diabetes, one for IHD and one for HF) and four indicators of appropriate therapy (two each for IHD and HF) were considered. Results: Agreement between the two data sources was very good, except for indicators of laboratory diagnostic follow-up in one region and for the indicator of bioimaging diagnostic follow-up in all regions, where measurement with administrative data underestimated quality. Conclusion: According to evidence presented in this study, estimating compliance with standards of care for diabetes, ischaemic heart disease and heart failure from healthcare databases is likely to produce reliable results, even though completeness of data on diagnostic procedures should be assessed first. Performing studies comparing regions using such indicators as outcomes is a promising development with potential to improve quality governance in the Italian healthcare system.

Short-term memory can be defined as the capacity for holding a small amount of information in mind in an active state for a short period of time. Although some instruments have been developed to study spatial short-term memory in real environments, there are no instruments that are specifically designed to assess visuospatial short-term memory in an attractive way to children. In this paper, we present the ARSM (Augmented Reality Spatial Memory) task, the first Augmented Reality task that involves a user’s movement to assess spatial short-term memory in healthy children. The experimental procedure of the ARSM task was designed to assess the children’s skill to retain visuospatial information. They were individually asked to remember the real place where augmented reality objects were located. The children (N576) were divided into two groups: preschool (5–6 year olds) and primary school (7–8 year olds). We found a significant improvement in ARSM task performance in the older group. The correlations between scores for the ARSM task and traditional procedures were significant. These traditional procedures were the Dot Matrix subtest for the assessment of visuospatial short-term memory of the computerized AWMA-2 battery and a parent’s questionnaire about a child’s everyday spatial memory. Hence, we suggest that the ARSM task has high verisimilitude with spatial short-term memory skills in real life. In addition, we evaluated the ARSM task’s usability and perceived satisfaction. The study revealed that the younger children were more satisfied with the ARSM task. This novel instrument could be useful in detecting visuospatial short-term difficulties that affect specific developmental navigational disorders and/or school academic achievement, Funded by the Spanish Government (MINECO) and European Regional Development Fund (FEDER) in the CHILDMNEMOS project TIN2012-37381-C02-01, Gobierno de Aragón (Dpt. Industria e Innovación), Fondo Social Europeo, Fundación Universitaria Antonio Gargallo and Obra Social Ibercaja

In small animal imaging studies, when the locations of the micro-structures of interest are unknown a priori, there is a simultaneous need for full-body coverage and high resolution. In MRI, additional requirements to image contrast and acquisition time will often make it impossible to acquire such images directly. Recently, a resolution enhancing post-processing technique called super-resolution reconstruction (SRR) has been demonstrated to improve visualization and localization of micro-structures in small animal MRI by combining multiple low-resolution acquisitions. However, when the field-of-view is large relative to the desired voxel size, solving the SRR problem becomes very expensive, in terms of both memory requirements and computation time. In this paper we introduce a novel local approach to SRR that aims to overcome the computational problems and allow researchers to efficiently explore both global and local characteristics in whole-body small animal MRI. The method integrates state-of-the-art image processing techniques from the areas of articulated atlas-based segmentation, planar reformation, and SRR. A proof-of-concept is provided with two case studies involving CT, BLI, and MRI data of bone and kidney tumors in a mouse model. We show that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.

Stirling et al., (10.1371/journal.pone.0108482) presented an analysis on some of our publications on the formation of stripe-like domains on mixed-ligand coated gold nanoparticles. The authors shed doubts on some of our results however no valid argument is provided against what we have shown since our first publication: scanning tunneling microscopy (STM) images of striped nanoparticles show stripe-like domains that are independent of imaging parameters and in particular of imaging speed. We have consistently ruled out the presence of artifacts by comparing sets of images acquired at different tip speeds, finding invariance of the stipe-like domains. Stirling and co-workers incorrectly analyzed this key control, using a different microscope and imaging conditions that do not compare to ours. We show here data proving that our approach is rigorous. Furthermore, we never solely relied on image analysis to draw our conclusions; we have always used the chemical nature of the particles to assess the veracity of our images. Stirling et al. do not provide any justification for the spacing of the features that we find on nanoparticles: similar to 1 nm for mixed ligand particles and similar to 0.5 nm for homoligand particles. Hence our two central arguments remain unmodified: independence from imaging parameters and dependence on ligand shell chemical composition. The paper report observations on our STM images; none is a sufficient condition to prove that our images are artifacts. We thoroughly addressed issues related to STM artifacts throughout our microscopy work. Stirling et al. provide guidelines for what they consider good STM images of nanoparticles, such images are indeed present in our literature. They conclude that the evidences we provided to date are insufficient, this is a departure from one of the authors' previous article which concluded that our images were composed of artifacts. Given that four independent laboratories have reproduced our measurements and that no scientifically rigorous argument is presented to invalidate our STM images, and also given that Stirling et al. do not contest the quality of our recent STM images, we re-affirm that specific binary mixture of ligands spontaneously form features in their ligand shell that we describe as stripe-like domains similar to 1 nm in width.