Population aging through survival of the fit and stable

Motivated by the wide range of known self-replicating systems, some far from genetics, we study a system composed by individuals having an internal dynamics with many possible states that are partially stable, with varying mutation rates. Individuals reproduce and die with a rate that is a property of each state, not necessarily related to its stability, and the offspring is born on the parent's state. The total population is limited by resources or space, as for example in a chemostat or a Petri dish. Our aim is to show that mutation rate and fitness become more correlated, \emph{even if they are completely uncorrelated for an isolated individual}, underlining the fact that the interaction induced by limitation of resources is by itself efficient for generating collective effects

Hydrodynamics of confined active fluids

We theoretically describe the dynamics of swimmer populations confined in thin liquid films. We first demonstrate that hydrodynamic interactions between confined swimmers only depend on their shape and are independent of their specific swimming mechanism. We also show that due to friction with the walls, confined swimmers do not reorient due to flow gradients but the flow field itself. We then quantify the consequences of these microscopic interaction rules on the large-scale hydrodynamics of isotropic populations. We investigate in details their stability and the resulting phase behavior, highlighting the differences with conventional active, three-dimensional suspensions. Two classes of polar swimmers are distinguished depending on their geometrical polarity. The first class gives rise to coherent directed motion at all scales whereas for the second class we predict the spontaneous formation of coherent clusters (swarms).Comment: 5 pages, 2 figure

Progressive insular cooperative genetic programming algorithm for multiclass classification

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn contrast to other types of optimisation algorithms, Genetic Programming (GP) simultaneously optimises a group of solutions for a given problem. This group is named population, the algorithm iterations are named generations and the optimisation is named evolution as a reference o the algorithm’s inspiration in Darwin’s theory on the evolution of species. When a GP algorithm uses a one-vs-all class comparison for a multiclass classification (MCC) task, the classifiers for each target class (specialists) are evolved in a subpopulation and the final solution of the GP is a team composed of one specialist classifier of each class. In this scenario, an important question arises: should these subpopulations interact during the evolution process or should they evolve separately? The current thesis presents the Progressively Insular Cooperative (PIC) GP, a MCC GP in which the level of interaction between specialists for different classes changes through the evolution process. In the first generations, the different specialists can interact more, but as the algorithm evolves, this level of interaction decreases. At a later point in the evolution process, controlled through algorithm parameterisation, these interactions can be eliminated. Thus, in the beginning of the algorithm there is more cooperation among specialists of different classes, favouring search space exploration. With elimination of cooperation, search space exploitation is favoured. In this work, different parameters of the proposed algorithm were tested using the Iris dataset from the UCI Machine Learning Repository. The results showed that cooperation among specialists of different classes helps the improvement of classifiers specialised in classes that are more difficult to discriminate. Moreover, the independent evolution of specialist subpopulations further benefits the classifiers when they already achieved good performance. A combination of the two approaches seems to be beneficial when starting with subpopulations of differently performing classifiers. The PIC GP also presented great performance for the more complex Thyroid and Yeast datasets of the same repository, achieving similar accuracy to the best values found in literature for other MCC models.Diferente de outros algoritmos de otimiação computacional, o algoritmo de Programação Genética PG otimiza simultaneamente um grupo de soluções para um determinado problema. Este grupo de soluções é chamado população, as iterações do algoritmo são chamadas de gerações e a otimização é chamada de evolução em alusão à inspiração do algoritmo na teoria da evolução das espécies de Darwin. Quando o algoritmo GP utiliza a abordagem de comparação de classes um-vs-todos para uma classificação multiclasses (CMC), os classificadores específicos para cada classe (especialistas) são evoluídos em subpopulações e a solução final do PG é uma equipe composta por um especialista de cada classe. Neste cenário, surge uma importante questão: estas subpopulações devem interagir durante o processo evolutivo ou devem evoluir separadamente? A presente tese apresenta o algoritmo Cooperação Progressivamente Insular (CPI) PG, um PG CMC em que o grau de interação entre especialistas em diferentes classes varia ao longo do processo evolutivo. Nas gerações iniciais, os especialistas de diferentes classes interagem mais. Com a evolução do algoritmo, estas interações diminuem e mais tarde, dependendo da parametriação do algoritmo, elas podem ser eliminadas. Assim, no início do processo evolutivo há mais cooperação entre os especialistas de diferentes classes, o que favorece uma exploração mais ampla do espaço de busca. Com a eliminação da cooperação, favorece-se uma exploração mais local e detalhada deste espaço. Foram testados diferentes parâmetros do PG CPl utilizando o conjunto de dados iris do UCI Machine Learning Repository. Os resultados mostraram que a cooperação entre especialistas de diferentes classes ajudou na melhoria dos classificadores de classes mais difíceis de modelar. Além disso, que a evolução sem a interação entre as classes de diferentes especialidades beneficiou os classificadores quando eles já apresentam boa performance Uma combinação destes dois modos pode ser benéfica quando o algoritmo começa com classificadores que apresentam qualidades diferentes. O PG CPI também apresentou ótimos resultados para outros dois conjuntos de dados mais complexos o thyroid e o yeast, do mesmo repositório, alcançando acurácia similar aos melhores valores encontrados na literatura para outros modelos de CMC

Un'artista e lo specchio della critica. Il carteggio inedito tra Neera e Gargàno.

Sin resume

Temporal Adaptive Changes in Contractility and Fatigability of Diaphragm Muscles from Streptozotocin-Diabetic Rats

Diabetes is characterized by ventilatory depression due to decreased diaphragm (DPH) function. This study investigated the changes in contractile properties of rat DPH muscles over a time interval encompassing from 4 days to 14 weeks after the onset of streptozotocin-induced diabetes, with and without insulin treatment for 2 weeks. Maximum tetanic force in intact DPH muscle strips and recovery from fatiguing stimulation were measured. An early (4-day) depression in contractile function in diabetic DPH was followed by gradual improvement in muscle function and fatigue recovery (8 weeks). DPH contractile function deteriorated again at 14 weeks, a process that was completely reversed by insulin treatment. Maximal contractile force and calcium sensitivity assessed in Triton-skinned DPH fibers showed a similar bimodal pattern and the same beneficial effect of insulin treatment. While an extensive analysis of the isoforms of the contractile and regulatory proteins was not conducted, Western blot analysis of tropomyosin suggests that the changes in diabetic DPH response depended, at least in part, on a switch in fiber type