Scaling laws and the growth dynamics in biological structures

Neste artigo vamos apresentar de forma sucinta e interdisciplinar a relação entre as leis de escala na física e a dinâmica do crescimento em estruturas biológicas. Inicialmente, serão discutidos os conceitos preliminares nos quais se baseiam as leis de escala aplicadas na biologia. Em seguida, usaremos a hipótese de similaridade de West para formular, de maneira didática e detutiva, uma equação diferencial generalizada para estudar o crescimento dos organismos em geral.In this article we will briefly present an interdisciplinary relationship between scaling laws in physics and growth dynamics in biological structures. First, will be discussed the preliminary concepts of the scaling laws applied in biology. By using the West similarity hypothesis, we formulate, in a deductive and didactic way, a generalized differential equation to study the growth of organisms in general

De todos en mi familia

Por eso aborrecía a mi padre y juré que nunca sería como él. Ese juramento me llevó a huir de su recuerdo estancado en esa casa, en esa patria ya dejada atrás. Pero ahora que veo esa misma imagen reflejada en mi sombra sobre este libro grueso y abierto de teoría, sé que ella me hunde, en este instante, el mismo cuchillo que utilizó mi madre

The Kardar-Parisi-Zhang exponents for the 2+12+1 dimensions

The Kardar-Parisi-Zhang (KPZ) equation has been connected to a large number of important stochastic processes in physics, chemistry and growth phenomena, ranging from classical to quantum physics. The central quest in this field is the search for ever more precise universal growth exponents. Notably, exact growth exponents are only known for $1+1$ dimensions. In this work, we present physical and geometric analytical methods that directly associate these exponents to the fractal dimension of the rough interface. Based on this, we determine the growth exponents for the $2+1$ dimensions, which are in agreement with the results of thin films experiments and precise simulations. We also make a first step towards a solution in $d+1$ dimensions, where our results suggest the inexistence of an upper critical dimension

Immunohistochemical expression of intrarenal renin angiotensin system components in response to tempol in rats fed a high salt diet

AIM:To determine the effect of tempol in normal rats fed high salt on arterial pressure and the balance between antagonist components of the renal renin-angiotensin system.METHODS:Sprague-Dawley rats were fed with 8% NaCl high-salt (HS) or 0.4% NaCl (normal-salt, NS) diet for 3 wk, with or without tempol (T) (1 mmol/L, administered in drinking water). Mean arterial pressure (MAP), glomerular filtration rate (GFR), and urinary sodium excretion (UVNa) were measured. We evaluated angiotensin II (Ang II), angiotensin 1-7 (Ang 1-7), angiotensin converting enzyme 2 (ACE2), mas receptor (MasR), angiotensin type 1 receptor (AT1R) and angiotensin type 2 receptor (AT2R) in renal tissues by immunohistochemistry.RESULTS:The intake of high sodium produced a slight but significant increase in MAP and differentially regulated components of the renal renin-angiotensin system (RAS). This included an increase in Ang II and AT1R, and decrease in ACE-2 staining intensity using immunohistochemistry. Antioxidant supplementation with tempol increased natriuresis and GFR, prevented changes in blood pressure and reversed the imbalance of renal RAS components. This includes a decrease in Ang II and AT1R, as increase in AT2, ACE2, Ang (1-7) and MasR staining intensity using immunohistochemistry. In addition, the natriuretic effects of tempol were observed in NS-T group, which showed an increased staining intensity of AT2, ACE2, Ang (1-7) and MasR.CONCLUSION:These findings suggest that a high salt diet leads to changes in the homeostasis and balance between opposing components of the renal RAS in hypertension to favour an increase in Ang II. Chronic antioxidant supplementation can modulate the balance between the natriuretic and antinatriuretic components of the renal RAS.Fil: Cao, Gabriel Fernando. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; ArgentinaFil: Della Penna, Silvana Lorena. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Kouyoumdzian, Nicolás Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Choi, Marcelo Roberto. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; ArgentinaFil: Gorzalczany, Susana Beatriz. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Fernandez, Belisario Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; ArgentinaFil: Toblli, Jorge Eduardo. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; ArgentinaFil: Roson, Maria Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones Cardiológicas. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Cardiológicas; Argentina. Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica; Argentin

Self-organization and pattern formation in physical and biological systems

Neste trabalho apresentamos uma breve discussão sobre a descrição matemática do fenômeno formação de padrão em sistemas biológicos, observando os modelos matemáticos de dinâmica de populações. Listamos vários exemplos de sistemas físicos, químicos e biológicos que exibem este fenômeno enfatizando, em cada um, os parâmetros principais envolvidos em seu entendimento. Mostramos que, no caso das populações, o fenômeno padrão pode ser modelado ao modificarmos a equação de Fisher-Kolmogorov, considerando uma interação não-local para o termo de competição. Apresentamos um estudo analítico e numérico da equação de Fisher-Kolmogorov com difusão e analisamos o papel dos termos de crescimento, difusão e competição na formação dos padrões.In this work we present a brief discussion of the mathematical description of pattern formation phenomena in biological systems through the mathematical models of population dynamics. We present some examples of physical, chemical and biological systems which exhibit this phenomena. For each system we show the main parameters that describe the patterns. We show that in the case of population, patterns can be described when we modify the Fisher-Kolmogorov equation, considering a non-local interaction for the competition term. We present an analytical and numerical study of the Fisher-Kolmogorov equation with diffusion and we analyze the role of growth, diffusion and competition term in the pattern formation

Evolution of physical processes in models of population dynamics

Neste texto apresentamos e discutimos um breve panorama cronológico para a dinâmica de populações, observando o ponto de vista dos autores, bem como a evolução dos principais modelos matemáticos e sua importância histórica. Com foco na predição temporal e espacial da variação do número de indivíduos de uma população, analisamos como modelar matematicamente os processos físicos como crescimento, interação, difusão e fluxo de um coletivo de indivíduos. Partimos do bem conhecido modelo de Fibonacci e discutimos como modelos que o sucederam, a saber, o modelo Malthusiano, Lotka-Volterra e Fisher-Kolmogorov, foram capazes de ampliar o entendimento do comportamento de uma população. Apresentamos, nesta linha temporal sinuosa, como as interações entre uma mesma espécie e entre espécies podem ser explicadas e modeladas. Mostramos como funciona o processo de extinção de uma espécie predadora, o fenômeno de difusão de um coletivo devido as mais diversas exigências espaciais, as migrações e invasões de territórios por meio de uma dinâmica convectiva nos modelos de dinâmica de uma população e também como a não-localidade nas interações e no crescimento ampliam enormemente nosso entendimento sobre os padrões na natureza.In this paper we present and discuss a brief overview chronological for the population dynamics, observing the point of view of the authors, as well as the evolution of the main mathematical models and its historical importance. Focusing on temporal and spatial prediction of the variation in the number of individuals in a population, we analyze how to mathematically model the physical processes such as growth, interaction, dissemination and flow of a collective of individuals. We start from the well-known model of Fibonacci and discussed how models who succeeded him, namely the Malthusian model, Lotka-Volterra and Fisher-Kolmogorov were able to expand the understanding of the behavior of a population. Here, in this winding timeline as the interactions between species and between species can be explained and modeled. We show how the process of extinguishing a predatory species works, the diffusion phenomenon of a collective because the most diverse space requirements, migration and invasions of territories by means of convective momentum in dynamic models of a population as well as non-locality in interactions and growth greatly expand our understanding of the patterns in nature

230504

The advancements in wireless communication technologies have enabled unprecedented pervasiveness and ubiquity of Cyber-Physical Systems (CPS). Such technologies can now empower true Systemsof-Systems, which cooperate to achieve more complex and efficient functionalities. However, for CPS applications to become a reality, safety and security must be guaranteed, particularly in critical systems, since they rely on open communication systems prone to intentional and non-intentional interferences. We propose designing a Wireless Safety and Security Layer (WSSL) architecture to be implemented in critical CPS applications to address these issues. WSSL increases the reliability of these critical communications by enabling the detection of communication errors. Furthermore, it increases the CPS security using a message signature process that uniquely identifies the sender. So, we present the WSSL architecture and its implementation over an MQTT protocol. We prove that WSSL does not significantly increase the system transmission costs and demonstrate its capability to ensure safety and security, allowing it to be used in any general or critical CPS.info:eu-repo/semantics/publishedVersio

Pattern formation and coexistence domains for a nonlocal population dynamics

In this communication we propose a most general equation to study pattern formation for one-species population and their limit domains in systems of length L. To accomplish this we include non-locality in the growth and competition terms where the integral kernels are now depend on characteristic length parameters alpha and beta. Therefore, we derived a parameter space (alpha,beta) where it is possible to analyze a coexistence curve alpha*=alpha*(\beta) which delimits domains for the existence (or not) of pattern formation in population dynamics systems. We show that this curve has an analogy with coexistence curve in classical thermodynamics and critical phenomena physics. We have successfully compared this model with experimental data for diffusion of Escherichia coli populations