Universal temporal features of rankings in competitive sports and games

Many complex phenomena, from the selection of traits in biological systems to hierarchy formation in social and economic entities, show signs of competition and heterogeneous performance in the temporal evolution of their components, which may eventually lead to stratified structures such as the wealth distribution worldwide. However, it is still unclear whether the road to hierarchical complexity is determined by the particularities of each phenomena, or if there are universal mechanisms of stratification common to many systems. Human sports and games, with their (varied but simplified) rules of competition and measures of performance, serve as an ideal test bed to look for universal features of hierarchy formation. With this goal in mind, we analyse here the behaviour of players and team rankings over time for several sports and games. Even though, for a given time, the distribution of performance ranks varies across activities, we find statistical regularities in the dynamics of ranks. Specifically the rank diversity, a measure of the number of elements occupying a given rank over a length of time, has the same functional form in sports and games as in languages, another system where competition is determined by the use or disuse of grammatical structures. Our results support the notion that hierarchical phenomena may be driven by the same underlying mechanisms of rank formation, regardless of the nature of their components. Moreover, such regularities can in principle be used to predict lifetimes of rank occupancy, thus increasing our ability to forecast stratification in the presence of competition

Neuropeptide kyotorphin (tyrosyl-arginine) has decreased levels in the cerebro-spinal fluid of Alzheimer’s disease patients: potential diagnostic and pharmacological implications

In Alzheimer’s disease (AD), besides the characteristic deterioration of memory, studies also point to a higher pain tolerance in spite of sensibility preservation. A change in the normal tau protein phosphorylation is also characteristic of AD, which contributes to the pathogenesis of the disease and is useful in early diagnosis. Kyotorphin (KTP) is an endoge-nous analgesic dipeptide (Tyr-Arg) for which there is evidence of eventual neuroprotective and neuromodulatory properties. The objective of this work was to study the possible cor-relation between KTP and phosphorylated tau protein (p-tau) levels in cerebro-spinal fluid (CSF) samples of AD patients. CSF samples were collected from 25 AD patients and 13 age-matched controls (N), where p-tau and KTP levels were measured.We found a statis-tically significant difference between p-tau/KTP values in AD and N groups with an inverse correlation between p-tau and KTP values in AD samples. These results suggest that in the future KTP may be a candidate biomarker for neurodegeneration and may be a lead compound to be used pharmacologically for neuroprotection.Fundação para a Ciência e Tecnologia (FCT, Portugal) is acknowledged for fellowship SFRH/BPD/79542/2011 to Sónia Sá Santos and Grant PTDC/QUI-BIQ/112929/2009. MarieCurie International Research Staff Exchange Scheme (IRSES) is also acknowledged forfunding (FP7-PEOPLE-2009-IRSES, project MEMPEPACROSS)

Quantitative Clinical Chemistry Proteomics (qCCP) using mass spectrometry: general characteristics and application

Proteomics studies typically aim to exhaustively detect peptides/proteins in a given biological sample. Over the past decade, the number of publications using proteomics methodologies has exploded. This was made possible due to the availability of high-quality genomic data and many technological advances in the fields of microfluidics and mass spectrometry. Proteomics in biomedical research was initially used in ‘functional' studies for the identification of proteins involved in pathophysiological processes, complexes and networks. Improved sensitivity of instrumentation facilitated the analysis of even more complex sample types, including human biological fluids. It is at that point the field of clinical proteomics was born, and its fundamental aim was the discovery and (ideally) validation of biomarkers for the diagnosis, prognosis, or therapeutic monitoring of disease. Eventually, it was recognized that the technologies used in clinical proteomics studies [particularly liquid chromatography-tandem mass spectrometry (LC-MS/MS)] could represent an alternative to classical immunochemical assays. Prior to deploying MS in the measurement of peptides/proteins in the clinical laboratory, it seems likely that traditional proteomics workflows and data management systems will need to adapt to the clinical environment and meet in vitro diagnostic (IVD) regulatory constraints. This defines a new field, as reviewed in this article, that we have termed quantitative Clinical Chemistry Proteomics (qCCP

Phase transitions in tumor growth VI: Epithelial–Mesenchymal transition

Herewith we discuss a network model of the epithelial–mesenchymal transition (EMT) based on our previous proposed framework. The EMT appears as a “first order” phase transition process, analogous to the transitions observed in the chemical–physical field. Chiefly, EMT should be considered a transition characterized by a supercritical Andronov–Hopf bifurcation, with the emergence of limit cycle and, consequently, a cascade of saddle-foci Shilnikov's bifurcations. We eventually show that the entropy production rate is an EMT-dependent function and, as such, its formalism reminds the van der Waals equation.Fil: Guerra, A.. Universidad de La Habana; CubaFil: Rodriguez, D. J.. Universidad de La Habana; CubaFil: Montero, S.. Medical Sciences University Of Havana; CubaFil: Betancourt Mar, J. A.. Universidad de La Habana; CubaFil: Martín Pardo, Reinaldo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Investigaciones en Tecnología Química. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Investigaciones en Tecnología Química; Argentina. Mexican Institute Of Complex Systems. Tamaulipas; MéxicoFil: Silva Lamar, Eduardo. Universidad de La Habana; CubaFil: Bizzarri, María Julia. Universidad de La Habana; CubaFil: Cocho, G.. Universidad Nacional Autónoma de México; MéxicoFil: Mansilla, R.. Universidad Nacional Autónoma de México; MéxicoFil: Nieto Villar, José Manuel. Universidad de La Habana; Cub

Diagnóstico precoz de los errores congénitos del metabolismo

Los avances científicos recientes han permitido identificar un número importante de enfermedades consideradas como raras, aquellas que tienen una baja prevalencia o aparecen ocasionalmente en la población –en Europa, menos de 1 caso cada 2.000 ciudadanos–, entre las que se encuentran las enfermedades metabólicas. En España, se estima que hay, aproximadamente, tres millones de afectados por estas dolencias, cuya atención está siendo objeto de interés por las administraciones sanitarias. Las enfermedades metabólicas, o errores congénitos del metabolismo (ECM), son un grupo numeroso de dolencias hereditarias, cada una producida por el bloqueo de alguna vía metabólica en el organismo. La mayoría de ellas se heredan de forma autosómica recesiva, y su frecuencia se estima entre 1/1.000-3.000 recién nacidos vivos. Esta memoria recoge las investigaciones y trabajos llevados a cabo en la Unidad de Diagnóstico y Tratamiento de los Errores Congénitos del Metabolismo (UDTECM), del Departamento de Pediatría del Hospital Clínico Universitario de Santiago de Compostela, dedicado a evitar enfermedades que supongan un riesgo para el pleno desarrollo del recién nacido. En ella se presentan los resultados globales del programa de diagnóstico precoz neonatal desarrollado durante más de 30 años, del que se han beneficiado cerca de 700.000 niños. La unidad, formada por un equipo multiprofesional, ha recibido el Premio Reina Sofía 2008, de Prevención de la Discapacidad

Diagnóstico precoz de los errores congénitos del metabolismo

Los avances científicos recientes han permitido identificar un número importante de enfermedades consideradas como raras, aquellas que tienen una baja prevalencia o aparecen ocasionalmente en la población –en Europa, menos de 1 caso cada 2.000 ciudadanos–, entre las que se encuentran las enfermedades metabólicas. En España, se estima que hay, aproximadamente, tres millones de afectados por estas dolencias, cuya atención está siendo objeto de interés por las administraciones sanitarias. Las enfermedades metabólicas, o errores congénitos del metabolismo (ECM), son un grupo numeroso de dolencias hereditarias, cada una producida por el bloqueo de alguna vía metabólica en el organismo. La mayoría de ellas se heredan de forma autosómica recesiva, y su frecuencia se estima entre 1/1.000-3.000 recién nacidos vivos. Esta memoria recoge las investigaciones y trabajos llevados a cabo en la Unidad de Diagnóstico y Tratamiento de los Errores Congénitos del Metabolismo (UDTECM), del Departamento de Pediatría del Hospital Clínico Universitario de Santiago de Compostela, dedicado a evitar enfermedades que supongan un riesgo para el pleno desarrollo del recién nacido. En ella se presentan los resultados globales del programa de diagnóstico precoz neonatal desarrollado durante más de 30 años, del que se han beneficiado cerca de 700.000 niños. La unidad, formada por un equipo multiprofesional, ha recibido el Premio Reina Sofía 2008, de Prevención de la Discapacidad

Rank Dynamics of Word Usage at Multiple Scales

The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations

Clinical Mass spectrometry proteomics (cMSP) for medical laboratory: What does the future hold?

BACKGROUND: Mass spectrometry (MS) methods are being widely used these days in medical laboratories for quantifying many small molecular analytes as well as for microbiological purposes

Rank dynamics of word usage at multiple scales

The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.Peer reviewe

Quantitative Clinical Chemistry Proteomics (qCCP) using mass spectrometry: general characteristics and application

Proteomics studies typically aim to exhaustively detect peptides/proteins in a given biological sample. Over the past decade, the number of publications using pro teomics methodologies has exploded. This was made possible due to the availability of high-quality genomic data and many technological advances in the fields of microfluidics and mass spectrometry. Proteomics in biomedical research was initially used in ‘ functional ' studies for the identification of proteins involved in pathophysiological processes, complexes and networks. Improved sensitivity of instrumentation facilitated the analysis of even more complex sample types, including human biological fluids. It is at that point the field of clinical proteomics was born, and its fundamental aim was the discovery and (ideally) validation of biomarkers for the diagnosis, prognosis, or therapeutic monitoring of disease. Eventually, it was recognized that the technologies used in clinical proteomics studies [particularly liquid chromatography-tandem mass spectrometry (LC-MS/MS)] could represent an alternative to classical immunochemical assays. Prior to deploying MS in the measurement of peptides/proteins in the clinical laboratory, it seems likely that traditional proteo mics workflows and data management systems will need to adapt to the clinical environment and meet in vitro diagnostic (IVD) regulatory constraints. This defines a new field, as reviewed in this article, that we have termed quantitative Clinical Chemistry Proteomics (qCCP)