Medicine by Alexandros G. Sfakianakis

Δευτέρα 30 Απριλίου 2018

Mitochondria, temperature, and the pace of life

Abstract

Life history strategies, physiological traits and behavior are thought to covary along a "pace of life" axis, with organisms at the fast end of this continuum having higher fecundity, shorter lifespan, and more rapid development, growth, and metabolic rates. Countergradient variation represents a special case of pace of life variation, in which high-latitude organisms occupy the fast end of the continuum relative to low-latitude conspecifics when compared at a common temperature. Here, we use Atlantic killifish (Fundulus heteroclitus) to explore the role of mitochondrial properties as a mechanism underlying countergradient variation, and thus variation in the pace of life. This species is found along the Atlantic coast of North America, through a steep latitudinal thermal gradient. The northern subspecies has faster development, more rapid growth, higher routine metabolic rate, and higher activity than the southern subspecies when compared at a common temperature. The northern subspecies also has greater mitochondrial respiratory capacity in the liver, although these differences are not evident in other tissues. The increased respiratory capacity of liver mitochondria in northern fish is associated with increases in the activity of multiple electron transport complexes, which largely reflects an increase in the amount of inner mitochondrial membrane per mitochondrion in the northern fish. There are also differences in the lipid composition of liver mitochondrial membranes, including differences in cardiolipin species, which could also influence respiratory capacity. These data suggest that variation in mitochondrial properties could, at least in part, underlie variation in the pace of life in Atlantic killifish.

Integrated ecological floating bed treating wastewater treatment plant effluents: effects of influent nitrogen forms and sediments

Abstract

In recent years, the treatment of wastewater treatment plant (WWTP) effluent has gained increasing attention. However, researches on the relationships between nitrogen forms and nitrogen removal efficiency are very limited. Based on the fact that the nitrogen forms in the WWTP effluent may vary as the season changes, the nitrogen removal efficiencies of an integrated ecological floating bed (IEFB) was studied under different influent nitrogen forms. In addition, the effects of sediments in the system were also quantified during the experiment. Results showed that the total nitrogen (TN) removal rates of the IEFB were 25.61 ± 5.72% and 60.03 ± 7.00%, respectively, when the main influent nitrogen forms are nitrate and ammonia. The sediments in the system also played vital roles in the removal processes: when the sediments were covered with a polyethylene membrane, the total nitrogen (TN) removal rate of the system dropped from 27.86 ± 5.53% to 14.78 ± 4.97%, and the total phosphorus (TP), from 58.77 ± 6.20% to 33.51 ± 25.52%.

Modeling the risk of water pollution by pesticides from imbalanced data

Abstract

The pollution of ground and surface waters with pesticides is a serious ecological issue that requires adequate treatment. Most of the existing water pollution models are mechanistic mathematical models. While they have made a significant contribution to understanding the transfer processes, they face the problem of validation because of their complexity, the user subjectivity in their parameterization, and the lack of empirical data for validation. In addition, the data describing water pollution with pesticides are, in most cases, very imbalanced. This is due to strict regulations for pesticide applications, which lead to only a few pollution events. In this study, we propose the use of data mining to build models for assessing the risk of water pollution by pesticides in field-drained outflow water. Unlike the mechanistic models, the models generated by data mining are based on easily obtainable empirical data, while the parameterization of the models is not influenced by the subjectivity of ecological modelers. We used empirical data from field trials at the La Jaillière experimental site in France and applied the random forests algorithm to build predictive models that predict "risky" and "not-risky" pesticide application events. To address the problems of the imbalanced classes in the data, cost-sensitive learning and different measures of predictive performance were used. Despite the high imbalance between risky and not-risky application events, we managed to build predictive models that make reliable predictions. The proposed modeling approach can be easily applied to other ecological modeling problems where we encounter empirical data with highly imbalanced classes.