Discover the most talked about and latest scientific content & concepts.

Concept: Metric space


Biology presents many examples of planar distribution and structural networks having dense sets of closed loops. An archetype of this form of network organization is the vasculature of dicotyledonous leaves, which showcases a hierarchically-nested architecture containing closed loops at many different levels. Although a number of approaches have been proposed to measure aspects of the structure of such networks, a robust metric to quantify their hierarchical organization is still lacking. We present an algorithmic framework, the hierarchical loop decomposition, that allows mapping loopy networks to binary trees, preserving in the connectivity of the trees the architecture of the original graph. We apply this framework to investigate computer generated graphs, such as artificial models and optimal distribution networks, as well as natural graphs extracted from digitized images of dicotyledonous leaves and vasculature of rat cerebral neocortex. We calculate various metrics based on the asymmetry, the cumulative size distribution and the Strahler bifurcation ratios of the corresponding trees and discuss the relationship of these quantities to the architectural organization of the original graphs. This algorithmic framework decouples the geometric information (exact location of edges and nodes) from the metric topology (connectivity and edge weight) and it ultimately allows us to perform a quantitative statistical comparison between predictions of theoretical models and naturally occurring loopy graphs.

Concepts: Mathematics, Structure, Topology, Graph theory, Topological space, Metric space, Graph, Architecture


Many studies show that open access (OA) articles-articles from scholarly journals made freely available to readers without requiring subscription fees-are downloaded, and presumably read, more often than closed access/subscription-only articles. Assertions that OA articles are also cited more often generate more controversy. Confounding factors (authors may self-select only the best articles to make OA; absence of an appropriate control group of non-OA articles with which to compare citation figures; conflation of pre-publication vs. published/publisher versions of articles, etc.) make demonstrating a real citation difference difficult. This study addresses those factors and shows that an open access citation advantage as high as 19% exists, even when articles are embargoed during some or all of their prime citation years. Not surprisingly, better (defined as above median) articles gain more when made OA.

Concepts: Mathematics, Topology, Peer review, Metric space, Open set, Open access, Wealth condensation, The rich get richer and the poor get poorer


Topological networks lie at the heart of our cities and social milieu. However, it remains unclear how and when the brain processes topological structures to guide future behaviour during everyday life. Using fMRI in humans and a simulation of London (UK), here we show that, specifically when new streets are entered during navigation of the city, right posterior hippocampal activity indexes the change in the number of local topological connections available for future travel and right anterior hippocampal activity reflects global properties of the street entered. When forced detours require re-planning of the route to the goal, bilateral inferior lateral prefrontal activity scales with the planning demands of a breadth-first search of future paths. These results help shape models of how hippocampal and prefrontal regions support navigation, planning and future simulation.

Concepts: Brain, Topology, Manifold, Graph theory, Topological space, Metric space, Open set, Network topology


The purpose of this study was to compare the distances covered during a 11-a-side soccer match after players had consumed either a high carbohydrate (CHO) or a low CHO diet. Twenty two male professional soccer players formed two teams (A and B), of similar age, body characteristics, and training experience. The two teams played against each other twice with a week interval between. For 3.5 days before the 1 match the players of team A followed a high CHO diet that provided 8 g CHO per Kg body mass (BM) (HC), whereas team B players followed a low CHO diet that provided 3 g CHO / Kg BM (LC) for the same time period. Before the 2 match the dietary treatment was reversed and followed for the same time period. Training during the study was controlled and distances covered were measured using GPS technology. Every player covered a greater total distance in HC compared to the distance covered in LC (HC: 9380 ± 98 m vs. LC: 8077 ± 109 m; p< 0.01). All distances covered from easy jogging (7.15 Km.h) to sprinting (24.15 Km.h) were also higher in HC compared to LC (p< 0.01). When players followed the HC treatment won the match (Team A vs. Team B: 3-1 for the first game and 1-2 for the 2 game). The HC diet probably helped players to cover a greater distance compared to LC. Soccer players should avoid eating a low (3 g CHO / Kg BM) CHO diet 3-4 days before an important soccer match and have a high CHO intake that provides at least 8 g CHO / Kg BM.

Concepts: Nutrition, Obesity, Players, Distance, Metric space, Diets, Periodization, Low-carbohydrate diet


The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive “pruning” of short-distance functional connections in schizophrenia.

Concepts: Brain, Magnetic resonance imaging, Cerebral cortex, Topology, Graph theory, Frontal lobe, Metric space, Network topology


A general morphometric method for describing shape variation in a sample consisting of landmarks and multiple outline shapes is developed in this article. A distance metric is developed for such data and is used to embed the data in a low-dimensional Euclidean space. The Euclidean space is used to generate summary statistics such as mean and principal shape variation which are implicitly represented in the original space using elements of the sample. A new distance metric for outline shapes is proposed based on Procrustes distance that does not require the extraction of discrete points along the curve. The outline distance metric can be naturally combined with distances between landmarks. A method for aligning outlines and multiple outlines is developed that minimizes the distance metric. The method is compared with semilandmarks on synthetic data and 2 real data sets. Outline methods produce useful and valid results when suitably constrained by landmarks and are useful visualization aids, but questions remain about their suitability for answering biological questions until appropriate distance metrics can be biologically validated. [Morphometrics; outline analysis; semilandmark.].

Concepts: Topology, Distance, Analytic geometry, Metric space, Shape, Procrustes analysis, Euclidean distance, Non-Euclidean geometry


Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique ‘window’ into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; ‘ecstasy’) and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-minute speech task after MDMA (0.75, 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically-relevant alterations to mental state, including those occurring in psychiatric illness.Neuropsychopharmacology accepted article peview online, 03 April 2014; doi:10.1038/npp.2014.80.

Concepts: Drug, Amphetamine, Psychoactive drug, Metric space, Methamphetamine, Recreational drug use, Convention on Psychotropic Substances, Psychedelic drug


AIM:: To present first experience of the use N-butyl cyanoacrylate with metacryloxisulfolane (Glubran 2) synthetic surgical glue, in the nonsurgical closure of oroantral communication (OAC). MATERIAL AND METHODS:: Two OACs, created after the exodontia of tooth 27 in 2 female patients, were sealed and closed with Glubran 2 surgical glue and monitored OACs, until the epithelization of the sockets was ended successfully. Two months postclosure of OACs, the sealed OACs were evaluated on the panoramic image and Water’s view radiography. RESULTS:: The extraction wounds with OACs were monitored until 23rd and 25th postinterventional days, when epithelization of socket ended successfully. On the panoramic image and Water’s view radiography, there were no radiological signs of maxillary sinus pathoses. CONCLUSION:: Glubran 2 can be successfully applied in the closure of OAC from 3 to 5 mm in diameter.

Concepts: Sinusitis, Maxillary sinus, Metric space, Panorama


To evaluate the 7-hole angle plate for open reduction, internal fixation of mandibular angle fractures when the Champy technique is inadequate and more rigid or semirigid fixation is beneficial and to provide rational indications for the choice of the 7-hole angle plate.

Concepts: Orthopedic surgery, Metric space, Choice theory


Person re-identification in a camera network is a valuable yet challenging problem to solve. Existing methods learn a common Mahalanobis distance metric by using the data collected from different cameras and then exploit the learned metric for identifying people in the images. However, the cameras in a camera network have different settings and the recorded images are seriously affected by variability in illumination conditions, camera viewing angles, and background clutter. Using a common metric to conduct person re-identification tasks on different camera pairs overlooks the differences in camera settings; however, it is very time-consuming to label people manually in images from surveillance videos. For example, in most existing person re-identification datasets, only one image of a person is collected from each of only two cameras; therefore, directly learning a unique Mahalanobis distance metric for each camera pair is susceptible to over-fitting by using insufficiently labeled data. In this paper, we reformulate person re-identification in a camera network as a multi-task distance metric learning problem. The proposed method designs multiple Mahalanobis distance metrics to cope with the complicated conditions that exist in typical camera networks. We address the fact that these Mahalanobis distance metrics are different but related, and learned by adding joint regularization to alleviate overfitting. Furthermore, by extending [1], we present a novel Multitask Maximally Collapsing Metric Learning (MtMCML) model for person re-identification in a camera network. Experimental results demonstrate that formulating person re-identification over camera networks as multi-task distance metric learning problem can improve performance, and our proposed MtMCML works substantially better than other current state-of-the-art person reidentification methods.

Concepts: Machine learning, Learning, Developmental psychology, Intelligence, Distance, Metric space, Photography, Camera