Pleiotropy as the Mechanism for Evolving Novelty: Same Signal, Different Result.

In contrast to the probabilistic way of thinking about pleiotropy as the random expression of a single gene that generates two or more distinct phenotypic traits, it is actually a deterministic consequence of the evolution of complex physiology from the unicellular state. Pleiotropic novelties emerge through recombinations and permutations of cell-cell signaling exercised during reproduction based on both past and present physical and physiologic conditions, in service to the future needs of the organism for its continued survival. Functional homologies ranging from the lung to the kidney, skin, brain, thyroid and pituitary exemplify the evolutionary mechanistic strategy of pleiotropy. The power of this perspective is exemplified by the resolution of evolutionary gradualism and punctuated equilibrium in much the same way that Niels Bohr resolved the paradoxical duality of light as Complementarity

Estratégias eficientes para identificação de falhas utilizando o diagnóstico baseado em comparações

Orientador: Prof. Dr. Elias Procópio Duarte Jr.Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Curso de Pós-Graduaçao em Informática. Defesa: Curitiba, 12/04/2013Bibliografia: fls. 126-148Resumo: O diagnóstico baseado em comparações e uma forma realista para detectar falhas em hardware, software, redes e sistemas distribuídos. O diagnostico se baseia na comparaçao de resultados de tarefas produzidos por pares de unidades para determinar quais sao as unidades falhas e sem-falha do sistema. Qualquer diferenca no resultado da comparacao indica que uma ou ambas as unidades estao falhas. O diagnostico completo do sistema e baseado no resultado de todas as comparações. Este trabalho apresenta um novo algoritmo de diagnostico para identificar falhas em sistemas de topologia arbitraria com base no modelo MM*. A complexidade do algoritmo proposto e O(t2AN) no pior caso para sistemas de N unidades, onde t denota o numero maximo permitido de unidades falhas e A e o grau da unidade de maior grau no sistema. Esta complexidade e significativamente menor que a dos outros algoritmos previamente publicados. Alem da especificacao do algoritmo e das provas de correcão, resultados obtidos atraves da execucao exaustiva de experimentos sao apresentados, mostrando o desempenho me dio do algoritmo para diferentes sistemas. Al em do novo algoritmo para sistemas de topologia arbitraria, este trabalho tambem apresenta duas outras solucoes para deteccão e combate a poluicao de conteudo, ou alteracoes nao autorizadas, em transmissões de mídia contínua ao vivo em redes P2P - a primeira e uma solucão centralizada e que realiza o diagnostico da poluicao na rede, e a segunda e uma solucao completamente distribuída e descentralizada que tem o objetivo de combater a propagacao da poluicao na rede. Ambas as solucoes utilizam o diagnostico baseado em comparacoes para detectar alterações no conteudo dos dados transmitidos. As soluções foram implementadas no Fireflies, um protocolo escalavel para redes overlay, e diversos experimentos atraves de simulacao foram conduzidos. Os resultados mostram que ambas as estrategias sao solucães viaveis para identificar e combater a poluiçcãao de conteudo em transmissãoes ao vivo e que adicionam baixa sobrecarga ao trafego da rede. Em particular a estrategia de combate a poluicao foi capaz de reduzir consideravelmente a poluicão de conteudo em diversas configurações, em varios casos chegando a elimina-la no decorrer das transmissoães.Abstract: Comparison-based diagnosis is a practical approach to detect faults in hardware, software, and network-based systems. Diagnosis is based on the comparison of task outputs returned by pairs of system units in order to determine whether those units are faulty or fault-free. If the comparison results in a mismatch then one ore both units are faulty. System diagnosis is based on the complete set of all comparison results. This work introduces a novel diagnosis algorithm to identify faults in t-diagnosable systems of arbitrary topology under the MM* model. The complexity of the proposed algorithm is O(t2AN) in the worst case for systems with N units, where t denotes the maximum number of faulty units allowed and A corresponds to the maximum degree of a unit in the system. This complexity is significantly lower than those of previously published algorithms. Besides the algorithm specification and correctness proofs, exhaustive simulations results are presented, showing the typical performance of the algorithm for different systems. Moreover, this work also presents two different strategies to detect and fight content pollution in P2P live streaming transmissions - the first strategy is centralized and performs the diagnosis of content pollution in the network, and the second strategy is a completely distributed solution to combat the propagation of the pollution. Both strategies employ comparison-based diagnosis in order to detect any modification in the data transmitted. The solutions were also implemented in Fireflies, a scalable and fault-tolerant overlay network protocol, and a large number of simulation experiments were conduced. Results show that both strategies are feasible solutions to identify and fight content pollution in live streaming sessions and that they add low overhead in terms of network bandwidth usage. In particular, the solution proposed to combat content pollution was able to significantly reduce the pollution over the system in diverse network configurations - in many cases the solution nearly eliminated the pollution during the transmission

Robust RANSAC-based blood vessel segmentation

International audienceMany vascular clinical applications require a vessel segmentation process that is able to both extract the centerline and the surface of the blood vessels. However, noise and topology issues (such as kissing vessels) prevent existing algorithms from being able to easily retrieve such a complex system as the brain vasculature. We propose here a new blood vessel tracking algorithm that 1) detect the vessel centerline; 2) provide a local radius estimate; and 3) extracts a dense set of points at the blood vessel surface. This algorithm is based on a RANSAC-based robust fitting of successive cylinders along the vessel. Our method was validated against the Multiple Hypothesis Testing (MHT) algorithm on 10 3DRA patient data of the brain vasculature. Over 30 blood vessels of various sizes were considered for each patient. Our results demonstrated a greater ability of our algorithm to track small, tortuous and touching vessels (96% success rate), compared to MHT (65% success rate). The computed centerline precision was below 1 voxel when compared to MHT. Moreover, our results were obtained with the same set of parameters for all patients and all blood vessels, except for the seed point for each vessel, also necessary for MHT. The proposed algorithm is thereafter able to extract the full intracranial vasculature with little user interaction

Machine learning approaches for early prediction of hypertension.

Hypertension afflicts one in every three adults and is a leading cause of mortality in 516, 955 patients in USA. The chronic elevation of cerebral perfusion pressure (CPP) changes the cerebrovasculature of the brain and disrupts its vasoregulation mechanisms. Reported correlations between changes in smaller cerebrovascular vessels and hypertension may be used to diagnose hypertension in its early stages, 10-15 years before the appearance of symptoms such as cognitive impairment and memory loss. Specifically, recent studies hypothesized that changes in the cerebrovasculature and CPP precede the systemic elevation of blood pressure. Currently, sphygmomanometers are used to measure repeated brachial artery pressure to diagnose hypertension after its onset. However, this method cannot detect cerebrovascular alterations that lead to adverse events which may occur prior to the onset of hypertension. The early detection and quantification of these cerebral vascular structural changes could help in predicting patients who are at a high risk of developing hypertension as well as other cerebral adverse events. This may enable early medical intervention prior to the onset of hypertension, potentially mitigating vascular-initiated end-organ damage. The goal of this dissertation is to develop a novel efficient noninvasive computer-aided diagnosis (CAD) system for the early prediction of hypertension. The developed CAD system analyzes magnetic resonance angiography (MRA) data of human brains gathered over years to detect and track cerebral vascular alterations correlated with hypertension development. This CAD system can make decisions based on available data to help physicians on predicting potential hypertensive patients before the onset of the disease

Physics Avoidance & Cooperative Semantics: Inferentialism and Mark Wilson’s Engagement with Naturalism Qua Applied Mathematics

Mark Wilson argues that the standard categorizations of "Theory T thinking"— logic-centered conceptions of scientific organization (canonized via logical empiricists in the mid-twentieth century)—dampens the understanding and appreciation of those strategic subtleties working within science. By "Theory T thinking," we mean to describe the simplistic methodology in which mathematical science allegedly supplies ‘processes’ that parallel nature's own in a tidily isomorphic fashion, wherein "Theory T’s" feigned rigor and methodological dogmas advance inadequate discrimination that fails to distinguish between explanatory structures that are architecturally distinct. One of Wilson's main goals is to reverse such premature exclusions and, thus, early on Wilson returns to John Locke's original physical concerns regarding material science and the congeries of descriptive concern insofar as capturing varied phenomena (i.e., cohesion, elasticity, fracture, and the transmission of coherent work) encountered amongst ordinary solids like wood and steel are concerned. Of course, Wilson methodologically updates such a purview by appealing to multiscalar techniques of modern computing, drawing from Robert Batterman's work on the greediness of scales and Jim Woodward's insights on causation

Cellular Automata

Modelling and simulation are disciplines of major importance for science and engineering. There is no science without models, and simulation has nowadays become a very useful tool, sometimes unavoidable, for development of both science and engineering. The main attractive feature of cellular automata is that, in spite of their conceptual simplicity which allows an easiness of implementation for computer simulation, as a detailed and complete mathematical analysis in principle, they are able to exhibit a wide variety of amazingly complex behaviour. This feature of cellular automata has attracted the researchers' attention from a wide variety of divergent fields of the exact disciplines of science and engineering, but also of the social sciences, and sometimes beyond. The collective complex behaviour of numerous systems, which emerge from the interaction of a multitude of simple individuals, is being conveniently modelled and simulated with cellular automata for very different purposes. In this book, a number of innovative applications of cellular automata models in the fields of Quantum Computing, Materials Science, Cryptography and Coding, and Robotics and Image Processing are presented

Women in Science 2017

Ever since its 1967 start, SURF has been a cornerstone of Smith’s science education. Women in Science 2017 summarizes research done by Smith College’s SURF Program participants during the summer of 2017. 151 students participated in SURF (144 hosted on campus and nearby eld sites), supervised by 58 faculty mentor-advisors drawn from the Clark Science Center and connected to its eighteen science, mathematics, and engineering departments and programs and associated centers and units. At summer’s end, SURF participants summarized their research experiences for this publication.https://scholarworks.smith.edu/clark_womeninscience/1006/thumbnail.jp

Simulation-Oriented Methodology for Distortion Minimisation during Laser Beam Welding

Distortion is one of the drawbacks of any welding process, most of the time needed to be suppressed. One doubtful factor that could affect welding deformation is the shape of the liquid melt pool, which can be modified via variation of process parameters. The aim of this work was to numerically study the dynamics of the weld pool and its geometrical influence on welding distortion during laser beam welding. To achieve such a goal, a promising novel process simulation model, employed in investigating the keyhole and weld pool dynamics, has successfully been invented. The model incorporated all distinctive behaviours of the laser beam welding process. Moreover, identification of the correlation between the weld pool geometry and welding distortion as well as, eventually, weld pool shapes that favour distortion minimisation has also been simulatively demonstrated

Simulation-Oriented Methodology for Distortion Minimisation during Laser Beam Welding

Distortion is one of the drawbacks of any welding process, most of the time needed to be suppressed. One doubtful factor that could affect welding deformation is the shape of the liquid melt pool, which can be modified via variation of process parameters. The aim of this work was to numerically study the dynamics of the weld pool and its geometrical influence on welding distortion during laser beam welding. To achieve such a goal, a promising novel process simulation model, employed in investigating the keyhole and weld pool dynamics, has successfully been invented. The model incorporated all distinctive behaviours of the laser beam welding process. Moreover, identification of the correlation between the weld pool geometry and welding distortion as well as, eventually, weld pool shapes that favour distortion minimisation has also been simulatively demonstrated

Adaptive optics for laser processing

The overall aim of the work presented in this thesis is to develop an adaptive optics (AO) technique for application to laser-based manufacturing processes. The Gaussian beam shape typically coming from a laser is not always ideal for laser machining. Wavefront modulators, such as deformable mirrors (DM) and liquid crystal spatial light modulators (SLM), enable the generation of a variety of beam shapes and furthermore offer the ability to alter the beam shape during the actual process. The benefits of modifying the Gaussian beam shape by means of a deformable mirror towards a square flat top profile for nanosecond laser marking and towards a ring shape intensity distribution for millisecond laser drilling are presented. Limitations of the beam shaping capabilities of DM are discussed. The application of a spatial light modulator to nanosecond laser micromachining is demonstrated for the first time. Heat sinking is introduced to increase the power handling capabilities. Controllable complex beam shapes can be generated with sufficient intensity for direct laser marking. Conventional SLM devices suffer from flickering and hence a process synchronisation is introduced to compensate for its impact on the laser machining result. For alternative SLM devices this novel technique can be beneficial when fast changes of the beam shape during the laser machining are required. The dynamic nature of SLMs is utilised to improve the marking quality by reducing the inherent speckle distribution of the generated beam shape. In addition, adaptive feedback on the intensity distribution can further improve the quality of the laser machining. In general, beam shaping by means of AO devices enables an increased flexibility and an improved process control, and thus has a significant potential to be used in laser materials processing