Extraction of Antioxidants from Animal Blood and its Potential Application as a Pet Food Preservative

Nowadays, more and more people are having pets as members of their family. To the year of 2012, there are 78.2 million dogs and 86.4 million cats owned in the U.S according to the report of the Humane Society of the U.S. The pet food industry as a result has been prosperous, with an estimated market size of $21 billion in the year of 2013. However, there is a common problem for the industry - fat rancidification. Pet foods usually contain relatively high levels of fat, which, if not well protected, are prone to oxidation and generate unfavorable products including acids, ketones and aldehydes. The resulting small volatile molecules will not only lead to unpleasant flavors and odors, but also could be unsafe if accumulated at high concentrations. In order to better preserve the quality of foods, it is a common and necessary practice to add antioxidant preservatives, which can scavenge free radicals and hence prevent or slow down the oxidation of fats. Currently available antioxidants can be generally divided into two categories: synthetic and natural antioxidants. Commonly used synthetic antioxidants include butylated hydroxytoluene (BHT), butylated hydroxyanisole (BHA) and tert-butylhydroquinone (TBHQ) and ethoxyquin (ETQ). Synthetic antioxidants are advantageous because of their high efficiency and low cost; however, they are criticized for having potential safety issues [3-7]. The natural options such as tocopherols and ascorbic acid are recognized as safer but less effective and are much more expensive compared to their synthetic counterparts. In spite of higher price and lower efficiency, there is a great customer demand for natural antioxidant, which is perceived to be beneficial for pet’s health. Consequently, there is a need to develop an alternative natural antioxidant, which is effective, inexpensive and safe

Reconciling cooperation, biodiversity and stability in complex ecological communities

Empirical observations show that ecological communities can have a huge number of coexisting species, also with few or limited number of resources. These ecosystems are characterized by multiple type of interactions, in particular displaying cooperative behaviors. However, standard modeling of population dynamics based on Lotka-Volterra type of equations predicts that ecosystem stability should decrease as the number of species in the community increases and that cooperative systems are less stable than communities with only competitive and/or exploitative interactions. Here we propose a stochastic model of population dynamics, which includes exploitative interactions as well as cooperative interactions induced by cross-feeding. The model is exactly solved and we obtain results for relevant macro-ecological patterns, such as species abundance distributions and correlation functions. In the large system size limit, any number of species can coexist for a very general class of interaction networks and stability increases as the number of species grows. For pure mutualistic/commensalistic interactions we determine the topological properties of the network that guarantee species coexistence. We also show that the stationary state is globally stable and that inferring species interactions through species abundance correlation analysis may be misleading. Our theoretical approach thus show that appropriate models of cooperation naturally leads to a solution of the long-standing question about complexity-stability paradox and on how highly biodiverse communities can coexist.Comment: 25 pages, 10 figure

Resilience, Complexity and Cooperation in Socio-ecological System

Advances in experimental technologies, both in the laboratory and in the field, are generating an increasing volume of ecologically and sociologically relevant data, spanning a wide range of scales, revealing recurrent emergence of patterns in these systems. This "data explosion"' is both a challenge (inventing new tools for their analysis) and an opportunity (identifying rules driving the functioning of complex systems). However, data alone do not necessarily lead to an understanding of the systems of interest. At present, we are in a situation where in front of a rich (but common to many systems) phenomenology we have innumerable models for very specific cases that call for a general vision. This challenge is very fascinating for physicists, that have in their veins the search for general principles of apparently different phenomena. In particular, a very important property that seems to be shared by most of the socio-ecological systems is their ability to respond to perturbations, i.e. the system resilience. Cell biology, ecology, environmental science, and food security are just some of the many areas of investigation on the mechanisms increasing the system resilience. Nevertheless, not all socio-ecological systems display high resilience. In food security, the intensification of international food trade and local shocks in food production led to global food crises, and for example Suweis develops a framework to investigate the coupled global food-population dynamics and finds that the global food system is losing resilience (increasingly unstable and susceptible to conditions of crisis); In ecology, the concept of resilience has evolved considerably since Holling's (1973) seminal paper to describe the persistence of natural systems in the face of changes in ecosystem variables due to natural or anthropogenic causes. It has been suggested that in many ecosystems we are facing a lost of resilience and consequent loss of biodiversity. Therefore an important challenge is to understand what are the main drivers ruling the resilience of ecological communities, so that proper ecosystem management strategy can be developed. From data is emerging that one of the key feature of socio-ecological system resilience may lie in the architecture of the interaction networks. The topology of the interaction network may actually represent the "parameter" that system somehow self-tunes so that the system's responses to stimuli is optimized with respect to some feature (e.g. stability). In inanimate matter, spins or particles always have their mutual interactions turned on (with an intensity decaying with their relative distance) and the network describing their interaction is dense, with most of the connections present. In contrast, if we consider for instance an ecosystem, species interact selectively even if they coexist at short distances, and the species interaction network is sparse, that is, most of the interactions are turned off. At the same time, the interactions that are turned on form non-random evolving structures that are the result of some optimization process under adaptive/evolution pressure. Thanks to massive databases now easy available, characteristics similar to those just mentioned for ecological networks, have been observed also in gene-interaction network, in neuronal networks and even in social networks. These networks are very different and yet share a crucial aspect: they all have undergone biological/social evolution that has driven their incremental complexity. One particular long-standing question regards the relationship between stability (resilience) and complexity in ecological system. Many of the population dynamics modeling frameworks proposed in the literature cannot elude the celebrated May's theorem. This theorem, recently refined by Allesina and Tang states that the stability of the system depends on the product [SC], where [S] is the number of species and [C] is the fraction of non-zero pairwise interactions between species. This result leads to the so-called stability and complexity paradox debate: a system in order to be stable cannot be too large ([S] large) or too connected (large [C]). The paradox lies in the fact that empirically, ecosystems with a large number of species seem to be very stable. Moreover, recently it has been suggested that because of this stability paradox, in microbial ecosystems competition may play a much important roles than what expected until now. In fact in these models, competition has a stabilizing role in ecosystem dynamics, contrarily to cooperation that decreases the ecosystem resilience. During my Ph.D. I have used a physicists approach based on complex networks and statistical physics, to study the resilience in Socio-Ecological systems, how it is related to the system complexity and what is the role of cooperation in the ecosystem dynamics. I have used a comprehensive approach that includes data mining, theoretical modeling (both computational and analytical) and statistical analyses. In particular, I have investigated the efficiency of a recently proposed framework to study the resilience of complex interacting systems, what the role of cooperation and competition in the universal patterns theoretically predicted by the model, and its validation with data. I have then focused on the long-standing open question of the relation between complexity and resilience in ecosystems, by specifically focusing on how the architecture of interaction networks may confer to living systems their ability to promptly react to to perturbations (e.g. increase resilience). To do that we have developed a stochastic population dynamics model, generalizing an interacting non-equilibrium model known as the voter model, and I have also studied the effect of cooperation on the ecosystem resilience. The results of my work suggest a novel picture on the relation between complexity, cooperation and resilience, challenging previous results in the literature

Dimensionality reduction of networked systems with separable coupling-dynamics: theory and applications

Complex dynamical systems are prevalent in various domains, but their analysis and prediction are hindered by their high dimensionality and nonlinearity. Dimensionality reduction techniques can simplify the system dynamics by reducing the number of variables, but most existing methods do not account for networked systems with separable coupling-dynamics, where the interaction between nodes can be decomposed into a function of the node state and a function of the neighbor state. Here, we present a novel dimensionality reduction framework that can effectively capture the global dynamics of these networks by projecting them onto a low-dimensional system. We derive the reduced system's equation and stability conditions, and propose an error metric to quantify the reduction accuracy. We demonstrate our framework on two examples of networked systems with separable coupling-dynamics: a modified susceptible-infected-susceptible model with direct infection and a modified Michaelis-Menten model with activation and inhibition. We conduct numerical experiments on synthetic and empirical networks to validate and evaluate our framework, and find a good agreement between the original and reduced systems. We also investigate the effects of different network structures and parameters on the system dynamics and the reduction error. Our framework offers a general and powerful tool for studying complex dynamical networks with separable coupling-dynamics.Comment: 15 pages, 5 figure

Critical slowing down associated with critical transition and risk of collapse in cryptocurrency

The year 2017 saw the rise and fall of the crypto-currency market, followed by high variability in the price of all crypto-currencies. In this work, we study the abrupt transition in crypto-currency residuals, which is associated with the critical transition (the phenomenon of critical slowing down) or the stochastic transition phenomena. We find that, regardless of the specific crypto-currency or rolling window size, the autocorrelation always fluctuates around a high value, while the standard deviation increases monotonically. Therefore, while the autocorrelation does not display signals of critical slowing down, the standard deviation can be used to anticipate critical or stochastic transitions. In particular, we have detected two sudden jumps in the standard deviation, in the second quarter of 2017 and at the beginning of 2018, which could have served as early warning signals of two majors price collapses that have happened in the following periods. We finally propose a mean-field phenomenological model for the price of crypto-currency to show how the use of the standard deviation of the residuals is a better leading indicator of the collapse in price than the time series' autocorrelation. Our findings represent a first step towards a better diagnostic of the risk of critical transition in the price and/or volume of crypto-currencies.Comment: 14 pages, 5 figures, 1 tabl

Resilience, Complexity and Cooperation in Socio-ecological System

Advances in experimental technologies, both in the laboratory and in the field, are generating an increasing volume of ecologically and sociologically relevant data, spanning a wide range of scales, revealing recurrent emergence of patterns in these systems. This "data explosion"' is both a challenge (inventing new tools for their analysis) and an opportunity (identifying rules driving the functioning of complex systems). However, data alone do not necessarily lead to an understanding of the systems of interest. At present, we are in a situation where in front of a rich (but common to many systems) phenomenology we have innumerable models for very specific cases that call for a general vision. This challenge is very fascinating for physicists, that have in their veins the search for general principles of apparently different phenomena. In particular, a very important property that seems to be shared by most of the socio-ecological systems is their ability to respond to perturbations, i.e. the system resilience. Cell biology, ecology, environmental science, and food security are just some of the many areas of investigation on the mechanisms increasing the system resilience. Nevertheless, not all socio-ecological systems display high resilience. In food security, the intensification of international food trade and local shocks in food production led to global food crises, and for example Suweis develops a framework to investigate the coupled global food-population dynamics and finds that the global food system is losing resilience (increasingly unstable and susceptible to conditions of crisis); In ecology, the concept of resilience has evolved considerably since Holling's (1973) seminal paper to describe the persistence of natural systems in the face of changes in ecosystem variables due to natural or anthropogenic causes. It has been suggested that in many ecosystems we are facing a lost of resilience and consequent loss of biodiversity. Therefore an important challenge is to understand what are the main drivers ruling the resilience of ecological communities, so that proper ecosystem management strategy can be developed. From data is emerging that one of the key feature of socio-ecological system resilience may lie in the architecture of the interaction networks. The topology of the interaction network may actually represent the "parameter" that system somehow self-tunes so that the system's responses to stimuli is optimized with respect to some feature (e.g. stability). In inanimate matter, spins or particles always have their mutual interactions turned on (with an intensity decaying with their relative distance) and the network describing their interaction is dense, with most of the connections present. In contrast, if we consider for instance an ecosystem, species interact selectively even if they coexist at short distances, and the species interaction network is sparse, that is, most of the interactions are turned off. At the same time, the interactions that are turned on form non-random evolving structures that are the result of some optimization process under adaptive/evolution pressure. Thanks to massive databases now easy available, characteristics similar to those just mentioned for ecological networks, have been observed also in gene-interaction network, in neuronal networks and even in social networks. These networks are very different and yet share a crucial aspect: they all have undergone biological/social evolution that has driven their incremental complexity. One particular long-standing question regards the relationship between stability (resilience) and complexity in ecological system. Many of the population dynamics modeling frameworks proposed in the literature cannot elude the celebrated May's theorem. This theorem, recently refined by Allesina and Tang states that the stability of the system depends on the product [SC], where [S] is the number of species and [C] is the fraction of non-zero pairwise interactions between species. This result leads to the so-called stability and complexity paradox debate: a system in order to be stable cannot be too large ([S] large) or too connected (large [C]). The paradox lies in the fact that empirically, ecosystems with a large number of species seem to be very stable. Moreover, recently it has been suggested that because of this stability paradox, in microbial ecosystems competition may play a much important roles than what expected until now. In fact in these models, competition has a stabilizing role in ecosystem dynamics, contrarily to cooperation that decreases the ecosystem resilience. During my Ph.D. I have used a physicists approach based on complex networks and statistical physics, to study the resilience in Socio-Ecological systems, how it is related to the system complexity and what is the role of cooperation in the ecosystem dynamics. I have used a comprehensive approach that includes data mining, theoretical modeling (both computational and analytical) and statistical analyses. In particular, I have investigated the efficiency of a recently proposed framework to study the resilience of complex interacting systems, what the role of cooperation and competition in the universal patterns theoretically predicted by the model, and its validation with data. I have then focused on the long-standing open question of the relation between complexity and resilience in ecosystems, by specifically focusing on how the architecture of interaction networks may confer to living systems their ability to promptly react to to perturbations (e.g. increase resilience). To do that we have developed a stochastic population dynamics model, generalizing an interacting non-equilibrium model known as the voter model, and I have also studied the effect of cooperation on the ecosystem resilience. The results of my work suggest a novel picture on the relation between complexity, cooperation and resilience, challenging previous results in the literature.Non

Role of reactive oxygen species in pluripotent stem cells cardiac differentiation and survival

Pluripotent stem cells (PSCs) derived cardiomyocytes provide an invaluable cell source for numerous important applications. PSC-cardiomyocytes may be transplanted into injured hearts to repair the tissue damage caused by myocardial infarction. Patients-specific induced PSCs (iPSCs) derived cardiomyocytes can serve as platforms for personalized cardiotoxicity test and drug screening. Further, in vitro cardiac differentiation process may be employed as a model for studying heart development. To achieve efficient and consistent cardiac differentiation, a wide range of biochemical (e.g., growth factors and small molecules) and physical stimuli (e.g., mechanical stress and electrical stimulation) have been explored with various degrees of success. Reactive oxygen species (ROS) are known to be critical in cell signaling, mainly via their modifications of protein activities. ROS have been shown to be indispensable for cardiac differentiation. In this dissertation, we seek to utilize ROS as a potential new tool in the control of cardiac differentiation, maturation and removal of undifferentiated cells. Specifically, we modulated ROS by changing glucose level and/or applying antioxidants. Our results showed that differentiation of mouse embryonic stem cells (mESCs) in high glucose medium without any antioxidants resulted in high level of cellular ROS, and also increased cardiac differentiation efficiency from about 15% to over 40%, along with increased expression of cardiac markers such as NKX2.5 and MESP1. More interestingly, when mESCs were differentiated in the presence of commonly used thiol-containing antioxidants such as beta-mercaptoethanol (BME) or monothiol glycerol (MTG), the resulting cardiomyocytes showed lower TNNI3/TNNI1 expression ratio, lower beating frequency and slower contraction velocity, suggesting delayed cardiac maturation. In contrast, Trolox, a vitamin E derivative and a non-thiol antioxidant, did not have these effects despite its strong antioxidant efficacy. These results suggest that redox regulation of cardiac differentiation is not only dictated by overall cellular ROS, but fine-controlled on a subcellular level. Further, ROS may also be utilized to promote desired cellular apoptosis. When co-treated with GSK3 inhibitors and antioxidants such as N-acetyl cysteine (NAC), human iPSCs underwent rapid and extensive apoptosis. This unique effect might be leveraged to remove iPSCs from their differentiated descendants to minimize the future risk of tumorigenesis.Biomedical Engineerin