Planktonic foraminifera distribution during the mid-Piacenzian warm period in the North Atlantic

The mid-Piacenzian (MP) warm period (3.264-3.025 Ma) has been identified as the most recent time in geologic history during which mean global surface temperatures were considerably warmer than today for a sustained period. This interval has therefore been proposed as a potential (albeit imperfect) analog for future climate change and as such, has received much scientific attention over the past two decades. Central to this research effort is the Pliocene Research, Interpretation, and Synoptic Mapping (PRISM) project, an iterative paleoenvironmental reconstruction of the MP focused on increasing our understanding of warm-period climate forcings, dynamics, and feedbacks by providing three-dimensional data sets for general circulation models. A mainstay of the PRISM project has been the development of a global sea surface temperature (SST) data set based primarily upon quantitative analyses of planktic foraminifer assemblages, supplemented with geochemical SST estimates wherever possible. In order to improve spatial coverage of the PRISM faunal data set in the low and mid-latitude North Atlantic, this study provides a description of the MP planktic foraminifer assemblage from five Ocean Drilling Program sites (951, 958, 1006, 1062, and 1063) in the subtropical gyre, a region critical to Atlantic Ocean circulation and tropical heat advection. Assemblages from each core provide evidence for a temperature- and circulation-driven 5-10° northward displacement of MP faunal provinces, as well as regional shifts in planktic foraminifer populations linked to species ecology and interactions. General biogeographic trends also indicate that, relative to modern conditions, gyre circulation was stronger (particularly the Gulf Stream, North Atlantic Current, and North Equatorial Current) and meridionally broader. A comparison of mid-Piacenzian and modern North Atlantic planktic foraminifer assemblages suggests that low latitude western boundary currents were less than 1 °C warmer while eastern boundary currents were ~1-2 °C warmer, supporting the hypothesis of enhanced northward heat advection along western boundary currents and warming of high latitude Northeast Atlantic source regions for the Canary Current. These findings are consistent with a model of reduced meridional SST gradients, with little-to-no low latitude warming, and more vigorous ocean circulation. Results therefore support the theory that enhanced meridional overturn circulation and associated northward heat advection made an important contribution, in conjunction with elevated atmospheric CO2 concentrations, to the 2-3 °C global surface temperature increase (relative to today) and strong polar amplification of SST warmth during the MP warm period.

A Mathematical Representation of "Excitement" in Games: A Contribution to the Theory of Game Systems

Researchers have long believed the concept of "excitement" in games to be subjective and difficult to measure. This paper presents the development of a mathematically computable index that measures the concept from the viewpoint of an audience and from that of a player. One of the key aspects of the index is the differential of the probability of "winning" before and after one specific "play" in a given game. The index makes a large contribution to the study of games and enables researchers to compare and analyze the “excitement” of various games. It may be applied in many fields, especially the area of welfare economics, and applications may range from those related to allocative efficiency to axioms of justice and equity.

Forest connectivity restoration through reforestation: an integrated methodology for prioritizing agricultural lands and selecting reforestation species

Forest connectivity restoration is a major goal in natural resource planning. Given the high amount of abandoned cultivated lands, setting efficient methods for the reforestation of agricultural lands offers a good opportunity to face this issue. However, reforestations must be carefully planned, which poses two main challenges. In first place, to determine those agricultural lands that, once reforested, would meet more effectively the planning goals. As a further step, in order to grant the success of the activity, it is fairly advisable to select those tree species that are more adapted to each particular environment. Here we intend to give response to both requirements by proposing a sequential and integrated methodology that has been implemented in two Spanish forest districts, which are formed by several landscape types that were previously defined and characterized. Using the software Conefor Sensinode, a powerful tool for quantifying habitat availability that is based on graph theory concepts, we determined the landscapes where forest planning should have connectivity as a major concern and, afterwards, we detected the agricultural patches that would contribute most to enhance connectivity if they were reforested. The subsequent reforestation species assessment was performed within these priority patches. Using penalized logistic regressions we fitted ecological niche models for the Spanish native tree species. The models were trained with species distribution data from the Spanish Forest Map and used climatic and lithological variables as predictors. Model predictions were used to build ordered lists of suitable species for each priority patch. The lists include dominant and non dominant tree species and allow adding biodiversity goals to the reforestation planning. The result of this combined methodology is a map of agricultural patches that would contribute most to uphold forest connectivity if they were reforested and a list of suitable tree species for each patch ordered by occurrence probability. Therefore the proposed methodology may be useful for suitable and efficient forest planning and landscape designing.

Reply to Comment on ``Towards a large deviation theory for strongly correlated systems''

The computational study commented by Touchette opens the door to a desirable generalization of standard large deviation theory for special, though ubiquitous, correlations. We focus on three interrelated aspects: (i) numerical results strongly suggest that the standard exponential probability law is asymptotically replaced by a power-law dominant term; (ii) a subdominant term appears to reinforce the thermodynamically extensive entropic nature of q-generalized rate function; (iii) the correlations we discussed, correspond to Q -Gaussian distributions, differing from Lévy?s, except in the case of Cauchy?Lorentz distributions. Touchette has agreeably discussed point (i), but, unfortunately, points (ii) and (iii) escaped to his analysis. Claiming the absence of connection with q-exponentials is unjustified.

Calculus of the uncertainty in acoustic field measurements: comparative study between the uncertainty propagation method and the distribution propagation method

The new Spanish Regulation in Building Acoustic establishes values and limits for the different acoustic magnitudes whose fulfillment can be verify by means field measurements. In this sense, an essential aspect of a field measurement is to give the measured magnitude and the uncertainty associated to such a magnitude. In the calculus of the uncertainty it is very usual to follow the uncertainty propagation method as described in the Guide to the expression of Uncertainty in Measurements (GUM). Other option is the numerical calculus based on the distribution propagation method by means of Monte Carlo simulation. In fact, at this stage, it is possible to find several publications developing this last method by using different software programs. In the present work, we used Excel for the Monte Carlo simulation for the calculus of the uncertainty associated to the different magnitudes derived from the field measurements following ISO 140-4, 140-5 and 140-7. We compare the results with the ones obtained by the uncertainty propagation method. Although both methods give similar values, some small differences have been observed. Some arguments to explain such differences are the asymmetry of the probability distributions associated to the entry magnitudes,the overestimation of the uncertainty following the GUM

Architectures for Video-on-Demand content distribution in managed networks

La demanda de contenidos de vídeo ha aumentado rápidamente en los últimos años como resultado del gran despliegue de la TV sobre IP (IPTV) y la variedad de servicios ofrecidos por los operadores de red. Uno de los servicios que se ha vuelto especialmente atractivo para los clientes es el vídeo bajo demanda (VoD) en tiempo real, ya que ofrece una transmisión (streaming) inmediata de gran variedad de contenidos de vídeo. El precio que los operadores tienen que pagar por este servicio es el aumento del tráfico en las redes, que están cada vez más congestionadas debido a la mayor demanda de contenidos de VoD y al aumento de la calidad de los propios contenidos de vídeo. Así, uno de los principales objetivos de esta tesis es encontrar soluciones que reduzcan el tráfico en el núcleo de la red, manteniendo la calidad del servicio en el nivel adecuado y reduciendo el coste del tráfico. La tesis propone un sistema jerárquico de servidores de streaming en el que se ejecuta un algoritmo para la ubicación óptima de los contenidos de acuerdo con el comportamiento de los usuarios y el estado de la red. Debido a que cualquier algoritmo óptimo de distribución de contenidos alcanza un límite en el que no se puede llegar a nuevas mejoras, la inclusión de los propios clientes del servicio (los peers) en el proceso de streaming puede reducir aún más el tráfico de red. Este proceso se logra aprovechando el control que el operador tiene en las redes de gestión privada sobre los equipos receptores (Set-Top Box) ubicados en las instalaciones de los clientes. El operador se reserva cierta capacidad de almacenamiento y streaming de los peers para almacenar los contenidos de vídeo y para transmitirlos a otros clientes con el fin de aliviar a los servidores de streaming. Debido a la incapacidad de los peers para sustituir completamente a los servidores de streaming, la tesis propone un sistema de streaming asistido por peers. Algunas de las cuestiones importantes que se abordan en la tesis son saber cómo los parámetros del sistema y las distintas distribuciones de los contenidos de vídeo en los peers afectan al rendimiento general del sistema. Para dar respuesta a estas preguntas, la tesis propone un modelo estocástico preciso y flexible que tiene en cuenta parámetros como las capacidades de enlace de subida y de almacenamiento de los peers, el número de peers, el tamaño de la biblioteca de contenidos de vídeo, el tamaño de los contenidos y el esquema de distribución de contenidos para estimar los beneficios del streaming asistido por los peers. El trabajo también propone una versión extendida del modelo matemático mediante la inclusión de la probabilidad de fallo de los peers y su tiempo de recuperación en el conjunto de parámetros del modelo. Estos modelos se utilizan como una herramienta para la realización de exhaustivos análisis del sistema de streaming de VoD asistido por los peers para la amplia gama de parámetros definidos en los modelos. Abstract The demand of video contents has rapidly increased in the past years as a result of the wide deployment of IPTV and the variety of services offered by the network operators. One of the services that has especially become attractive to the customers is real-time Video on Demand (VoD) because it offers an immediate streaming of a large variety of video contents. The price that the operators have to pay for this convenience is the increased traffic in the networks, which are becoming more congested due to the higher demand for VoD contents and the increased quality of the videos. Therefore, one of the main objectives of this thesis is finding solutions that would reduce the traffic in the core of the network, keeping the quality of service on satisfactory level and reducing the traffic cost. The thesis proposes a system of hierarchical structure of streaming servers that runs an algorithm for optimal placement of the contents according to the users’ behavior and the state of the network. Since any algorithm for optimal content distribution reaches a limit upon which no further improvements can be made, including service customers themselves (the peers) in the streaming process can further reduce the network traffic. This process is achieved by taking advantage of the control that the operator has in the privately managed networks over the Set-Top Boxes placed at the clients’ premises. The operator reserves certain storage and streaming capacity on the peers to store the video contents and to stream them to the other clients in order to alleviate the streaming servers. Because of the inability of the peers to completely substitute the streaming servers, the thesis proposes a system for peer-assisted streaming. Some of the important questions addressed in the thesis are how the system parameters and the various distributions of the video contents on the peers would impact the overall system performance. In order to give answers to these questions, the thesis proposes a precise and flexible stochastic model that takes into consideration parameters like uplink and storage capacity of the peers, number of peers, size of the video content library, size of contents and content distribution scheme to estimate the benefits of the peer-assisted streaming. The work also proposes an extended version of the mathematical model by including the failure probability of the peers and their recovery time in the set of parameters. These models are used as tools for conducting thorough analyses of the peer-assisted system for VoD streaming for the wide range of defined parameters.

Key reconciliation for high performance Quantum Key Distribution

Quantum Key Distribution is carving its place among the tools used to secure communications. While a difficult technology, it enjoys benefits that set it apart from the rest, the most prominent is its provable security based on the laws of physics. QKD requires not only the mastering of signals at the quantum level, but also a classical processing to extract a secret-key from them. This postprocessing has been customarily studied in terms of the efficiency, a figure of merit that offers a biased view of the performance of real devices. Here we argue that it is the throughput the significant magnitude in practical QKD, specially in the case of high speed devices, where the differences are more marked, and give some examples contrasting the usual postprocessing schemes with new ones from modern coding theory. A good understanding of its implications is very important for the design of modern QKD devices.

Análisis y modelización de la dinámica emergente durante el proceso de difusión de información en las redes sociales de internet

Durante la actividad diaria, la sociedad actual interactúa constantemente por medio de dispositivos electrónicos y servicios de telecomunicaciones, tales como el teléfono, correo electrónico, transacciones bancarias o redes sociales de Internet. Sin saberlo, masivamente dejamos rastros de nuestra actividad en las bases de datos de empresas proveedoras de servicios. Estas nuevas fuentes de datos tienen las dimensiones necesarias para que se puedan observar patrones de comportamiento humano a grandes escalas. Como resultado, ha surgido una reciente explosión sin precedentes de estudios de sistemas sociales, dirigidos por el análisis de datos y procesos computacionales. En esta tesis desarrollamos métodos computacionales y matemáticos para analizar sistemas sociales por medio del estudio combinado de datos derivados de la actividad humana y la teoría de redes complejas. Nuestro objetivo es caracterizar y entender los sistemas emergentes de interacciones sociales en los nuevos espacios tecnológicos, tales como la red social Twitter y la telefonía móvil. Analizamos los sistemas por medio de la construcción de redes complejas y series temporales, estudiando su estructura, funcionamiento y evolución en el tiempo. También, investigamos la naturaleza de los patrones observados por medio de los mecanismos que rigen las interacciones entre individuos, así como medimos el impacto de eventos críticos en el comportamiento del sistema. Para ello, hemos propuesto modelos que explican las estructuras globales y la dinámica emergente con que fluye la información en el sistema. Para los estudios de la red social Twitter, hemos basado nuestros análisis en conversaciones puntuales, tales como protestas políticas, grandes acontecimientos o procesos electorales. A partir de los mensajes de las conversaciones, identificamos a los usuarios que participan y construimos redes de interacciones entre los mismos. Específicamente, construimos una red para representar quién recibe los mensajes de quién y otra red para representar quién propaga los mensajes de quién. En general, hemos encontrado que estas estructuras tienen propiedades complejas, tales como crecimiento explosivo y distribuciones de grado libres de escala. En base a la topología de estas redes, hemos indentificado tres tipos de usuarios que determinan el flujo de información según su actividad e influencia. Para medir la influencia de los usuarios en las conversaciones, hemos introducido una nueva medida llamada eficiencia de usuario. La eficiencia se define como el número de retransmisiones obtenidas por mensaje enviado, y mide los efectos que tienen los esfuerzos individuales sobre la reacción colectiva. Hemos observado que la distribución de esta propiedad es ubicua en varias conversaciones de Twitter, sin importar sus dimensiones ni contextos. Con lo cual, sugerimos que existe universalidad en la relación entre esfuerzos individuales y reacciones colectivas en Twitter. Para explicar los factores que determinan la emergencia de la distribución de eficiencia, hemos desarrollado un modelo computacional que simula la propagación de mensajes en la red social de Twitter, basado en el mecanismo de cascadas independientes. Este modelo nos permite medir el efecto que tienen sobre la distribución de eficiencia, tanto la topología de la red social subyacente, como la forma en que los usuarios envían mensajes. Los resultados indican que la emergencia de un grupo selecto de usuarios altamente eficientes depende de la heterogeneidad de la red subyacente y no del comportamiento individual. Por otro lado, hemos desarrollado técnicas para inferir el grado de polarización política en redes sociales. Proponemos una metodología para estimar opiniones en redes sociales y medir el grado de polarización en las opiniones obtenidas. Hemos diseñado un modelo donde estudiamos el efecto que tiene la opinión de un pequeño grupo de usuarios influyentes, llamado élite, sobre las opiniones de la mayoría de usuarios. El modelo da como resultado una distribución de opiniones sobre la cual medimos el grado de polarización. Aplicamos nuestra metodología para medir la polarización en redes de difusión de mensajes, durante una conversación en Twitter de una sociedad políticamente polarizada. Los resultados obtenidos presentan una alta correspondencia con los datos offline. Con este estudio, hemos demostrado que la metodología propuesta es capaz de determinar diferentes grados de polarización dependiendo de la estructura de la red. Finalmente, hemos estudiado el comportamiento humano a partir de datos de telefonía móvil. Por una parte, hemos caracterizado el impacto que tienen desastres naturales, como innundaciones, sobre el comportamiento colectivo. Encontramos que los patrones de comunicación se alteran de forma abrupta en las áreas afectadas por la catástofre. Con lo cual, demostramos que se podría medir el impacto en la región casi en tiempo real y sin necesidad de desplegar esfuerzos en el terreno. Por otra parte, hemos estudiado los patrones de actividad y movilidad humana para caracterizar las interacciones entre regiones de un país en desarrollo. Encontramos que las redes de llamadas y trayectorias humanas tienen estructuras de comunidades asociadas a regiones y centros urbanos. En resumen, hemos mostrado que es posible entender procesos sociales complejos por medio del análisis de datos de actividad humana y la teoría de redes complejas. A lo largo de la tesis, hemos comprobado que fenómenos sociales como la influencia, polarización política o reacción a eventos críticos quedan reflejados en los patrones estructurales y dinámicos que presentan la redes construidas a partir de datos de conversaciones en redes sociales de Internet o telefonía móvil. ABSTRACT During daily routines, we are constantly interacting with electronic devices and telecommunication services. Unconsciously, we are massively leaving traces of our activity in the service providers’ databases. These new data sources have the dimensions required to enable the observation of human behavioral patterns at large scales. As a result, there has been an unprecedented explosion of data-driven social research. In this thesis, we develop computational and mathematical methods to analyze social systems by means of the combined study of human activity data and the theory of complex networks. Our goal is to characterize and understand the emergent systems from human interactions on the new technological spaces, such as the online social network Twitter and mobile phones. We analyze systems by means of the construction of complex networks and temporal series, studying their structure, functioning and temporal evolution. We also investigate on the nature of the observed patterns, by means of the mechanisms that rule the interactions among individuals, as well as on the impact of critical events on the system’s behavior. For this purpose, we have proposed models that explain the global structures and the emergent dynamics of information flow in the system. In the studies of the online social network Twitter, we have based our analysis on specific conversations, such as political protests, important announcements and electoral processes. From the messages related to the conversations, we identify the participant users and build networks of interactions with them. We specifically build one network to represent whoreceives- whose-messages and another to represent who-propagates-whose-messages. In general, we have found that these structures have complex properties, such as explosive growth and scale-free degree distributions. Based on the topological properties of these networks, we have identified three types of user behavior that determine the information flow dynamics due to their influence. In order to measure the users’ influence on the conversations, we have introduced a new measure called user efficiency. It is defined as the number of retransmissions obtained by message posted, and it measures the effects of the individual activity on the collective reacixtions. We have observed that the probability distribution of this property is ubiquitous across several Twitter conversation, regardlessly of their dimension or social context. Therefore, we suggest that there is a universal behavior in the relationship between individual efforts and collective reactions on Twitter. In order to explain the different factors that determine the user efficiency distribution, we have developed a computational model to simulate the diffusion of messages on Twitter, based on the mechanism of independent cascades. This model, allows us to measure the impact on the emergent efficiency distribution of the underlying network topology, as well as the way that users post messages. The results indicate that the emergence of an exclusive group of highly efficient users depends upon the heterogeneity of the underlying network instead of the individual behavior. Moreover, we have also developed techniques to infer the degree of polarization in social networks. We propose a methodology to estimate opinions in social networks and to measure the degree of polarization in the obtained opinions. We have designed a model to study the effects of the opinions of a small group of influential users, called elite, on the opinions of the majority of users. The model results in an opinions distribution to which we measure the degree of polarization. We apply our methodology to measure the polarization on graphs from the messages diffusion process, during a conversation on Twitter from a polarized society. The results are in very good agreement with offline and contextual data. With this study, we have shown that our methodology is capable of detecting several degrees of polarization depending on the structure of the networks. Finally, we have also inferred the human behavior from mobile phones’ data. On the one hand, we have characterized the impact of natural disasters, like flooding, on the collective behavior. We found that the communication patterns are abruptly altered in the areas affected by the catastrophe. Therefore, we demonstrate that we could measure the impact of the disaster on the region, almost in real-time and without needing to deploy further efforts. On the other hand, we have studied human activity and mobility patterns in order to characterize regional interactions on a developing country. We found that the calls and trajectories networks present community structure associated to regional and urban areas. In summary, we have shown that it is possible to understand complex social processes by means of analyzing human activity data and the theory of complex networks. Along the thesis, we have demonstrated that social phenomena, like influence, polarization and reaction to critical events, are reflected in the structural and dynamical patterns of the networks constructed from data regarding conversations on online social networks and mobile phones.

Cooperative Multi-robot Patrolling: A study of distributed approaches based on mathematical models of game theory to protect infrastructures

El principio de Teoría de Juegos permite desarrollar modelos estocásticos de patrullaje multi-robot para proteger infraestructuras criticas. La protección de infraestructuras criticas representa un gran reto para los países al rededor del mundo, principalmente después de los ataques terroristas llevados a cabo la década pasada. En este documento el termino infraestructura hace referencia a aeropuertos, plantas nucleares u otros instalaciones. El problema de patrullaje se define como la actividad de patrullar un entorno determinado para monitorear cualquier actividad o sensar algunas variables ambientales. En esta actividad, un grupo de robots debe visitar un conjunto de puntos de interés definidos en un entorno en intervalos de tiempo irregulares con propósitos de seguridad. Los modelos de partullaje multi-robot son utilizados para resolver este problema. Hasta el momento existen trabajos que resuelven este problema utilizando diversos principios matemáticos. Los modelos de patrullaje multi-robot desarrollados en esos trabajos representan un gran avance en este campo de investigación. Sin embargo, los modelos con los mejores resultados no son viables para aplicaciones de seguridad debido a su naturaleza centralizada y determinista. Esta tesis presenta cinco modelos de patrullaje multi-robot distribuidos e impredecibles basados en modelos matemáticos de aprendizaje de Teoría de Juegos. El objetivo del desarrollo de estos modelos está en resolver los inconvenientes presentes en trabajos preliminares. Con esta finalidad, el problema de patrullaje multi-robot se formuló utilizando conceptos de Teoría de Grafos, en la cual se definieron varios juegos en cada vértice de un grafo. Los modelos de patrullaje multi-robot desarrollados en este trabajo de investigación se han validado y comparado con los mejores modelos disponibles en la literatura. Para llevar a cabo tanto la validación como la comparación se ha utilizado un simulador de patrullaje y un grupo de robots reales. Los resultados experimentales muestran que los modelos de patrullaje desarrollados en este trabajo de investigación trabajan mejor que modelos de trabajos previos en el 80% de 150 casos de estudio. Además de esto, estos modelos cuentan con varias características importantes tales como distribución, robustez, escalabilidad y dinamismo. Los avances logrados con este trabajo de investigación dan evidencia del potencial de Teoría de Juegos para desarrollar modelos de patrullaje útiles para proteger infraestructuras. ABSTRACT Game theory principle allows to developing stochastic multi-robot patrolling models to protect critical infrastructures. Critical infrastructures protection is a great concern for countries around the world, mainly due to terrorist attacks in the last decade. In this document, the term infrastructures includes airports, nuclear power plants, and many other facilities. The patrolling problem is defined as the activity of traversing a given environment to monitoring any activity or sensing some environmental variables If this activity were performed by a fleet of robots, they would have to visit some places of interest of an environment at irregular intervals of time for security purposes. This problem is solved using multi-robot patrolling models. To date, literature works have been solved this problem applying various mathematical principles.The multi-robot patrolling models developed in those works represent great advances in this field. However, the models that obtain the best results are unfeasible for security applications due to their centralized and predictable nature. This thesis presents five distributed and unpredictable multi-robot patrolling models based on mathematical learning models derived from Game Theory. These multi-robot patrolling models aim at overcoming the disadvantages of previous work. To this end, the multi-robot patrolling problem was formulated using concepts of Graph Theory to represent the environment. Several normal-form games were defined at each vertex of a graph in this formulation. The multi-robot patrolling models developed in this research work have been validated and compared with best ranked multi-robot patrolling models in the literature. Both validation and comparison were preformed by using both a patrolling simulator and real robots. Experimental results show that the multirobot patrolling models developed in this research work improve previous ones in as many as 80% of 150 cases of study. Moreover, these multi-robot patrolling models rely on several features to highlight in security applications such as distribution, robustness, scalability, and dynamism. The achievements obtained in this research work validate the potential of Game Theory to develop patrolling models to protect infrastructures.

Fundamental finite key limits for information reconciliation in quantum key distribution

The security of quantum key distribution protocols is guaranteed by the laws of quantum mechanics. However, a precise analysis of the security properties requires tools from both classical cryptography and information theory. Here, we employ recent results in non-asymptotic classical information theory to show that information reconciliation imposes fundamental limitations on the amount of secret key that can be extracted in the finite key regime. In particular, we find that an often used approximation for the information leakage during one-way information reconciliation is flawed and we propose an improved estimate.

Waves, analytical signals, and some postulates of quantum theory

In this paper we apply the formalism of the analytical signal theory to the Schrödinger wavefunction. Making use exclusively of the wave-particle duality and the rinciple of relativistic covariance, we actually derive the form of the quantum energy and momentum operators for a single nonrelativistic particle. Without using any more quantum postulates, and employing the formalism of the characteristic function, we also derive the quantum-mechanical prescription for the measurement probability in such cases.

Game theory and reciprocity in some extensive form experimental games

We examine decision making in two-person extensive form game trees using nine treatments that vary matching protocol, payoffs, and payoff information. Our objective is to establish replicable principles of cooperative versus noncooperative behavior that involve the use of signaling, reciprocity, and backward induction strategies, depending on the availability of dominated direct punishing strategies and the probability of repeated interaction with the same partner. Contrary to the predictions of game theory, we find substantial support for cooperation under complete information even in various single-play treatments.

Estimating the probability for a protein to have a new fold: A statistical computational model

Structural genomics aims to solve a large number of protein structures that represent the protein space. Currently an exhaustive solution for all structures seems prohibitively expensive, so the challenge is to define a relatively small set of proteins with new, currently unknown folds. This paper presents a method that assigns each protein with a probability of having an unsolved fold. The method makes extensive use of protomap, a sequence-based classification, and scop, a structure-based classification. According to protomap, the protein space encodes the relationship among proteins as a graph whose vertices correspond to 13,354 clusters of proteins. A representative fold for a cluster with at least one solved protein is determined after superposition of all scop (release 1.37) folds onto protomap clusters. Distances within the protomap graph are computed from each representative fold to the neighboring folds. The distribution of these distances is used to create a statistical model for distances among those folds that are already known and those that have yet to be discovered. The distribution of distances for solved/unsolved proteins is significantly different. This difference makes it possible to use Bayes' rule to derive a statistical estimate that any protein has a yet undetermined fold. Proteins that score the highest probability to represent a new fold constitute the target list for structural determination. Our predicted probabilities for unsolved proteins correlate very well with the proportion of new folds among recently solved structures (new scop 1.39 records) that are disjoint from our original training set.

Columnar distribution of serotonin-dependent plasticity within kitten striate cortex

Recent studies have identified the potential for an important role for serotonin (5-HT) receptors in the developmental plasticity of the kitten visual cortex. 5-HT2C receptors are transiently expressed in a patchy fashion in the visual cortex of kittens between 30–80 days of age complementary to patches demarcated by cytochrome oxidase staining. 5-HT, operating via 5-HT2C receptors, increases cortical synaptic plasticity as assessed both in brain slices and in vivo. Herein, we report that bath application of 5-HT substantially increases the probability of long-term potentiation within 5-HT2C receptor-rich zones of cortex, but this effect is not observed in the 5-HT2C receptor-poor zones. Instead, in these zones, 5-HT application increases the probability of long-term depression. These location-specific effects of 5-HT may promote the formation of compartment-specific cortical responses.