Prey preference for Asian carp and soft plastic lure ingestion by largemouth bass

Invasive bighead (Hypophthalmichthys nobilis) and silver carp (Hypophthalmichthys molitrix) have become established throughout much of the Mississippi River basin. In many areas, these two species comprise a significant proportion of the fish biomass. Despite their prevalence and potential for negative environmental impacts, to date, there has been no assessment of vulnerability to predation of Asian carp compared to native species. We sought to examine largemouth bass (Micropterus salmoides) predation on juvenile bighead and silver carp in relation to common native prey species. Prey species selection experiments in 2-m pools showed number of prey captures was highest for bighead carp followed by gizzard shad with lower capture rates for bluegill, golden shiner, and silver carp. Observations of prey and predator behavior were quantified in a 720-L aquarium and variation in anti-predator behavior explained relative differences in vulnerability to predation. Differences in vulnerability to predation may explain the greater invasion success of silver carp. Similar or higher vulnerability to predation of Asian carp compared to common native prey suggests that they may serve as viable prey for native predators mitigating the potential negative impacts on the native prey community. Soft plastic fishing lures (SPLs) have recently gained attention as a potential source of pollution in aquatic systems. A number of anecdotal reports have suggested that discarded SPLs are being ingested by wild fish and causing health problems including mortality. Few studies have been conducted concerning the effects of SPLs on fish. We designed a laboratory study to determine the effects of ingestion of three different shapes and two different materials of SPLs on consumption by largemouth bass. No effects on consumption were observed except on the first day after SPL ingestion. In three trials with 30 fish, all largemouth bass were ultimately capable of expelling the lures from their bodies. Field data were also utilized to determine the occurrence of SPL ingestion by largemouth bass in the wild. In two Illinois lakes, occurrence rates of SPL ingestion were < 1%. Bass sampled with SPLs in their stomach did not have significantly different body condition from fish that had not ingested SPLs. We conclude that discarded SPLs do not pose a significant threat to the health of largemouth bass. Nevertheless, we encourage efforts to responsibly dispose of SPLs in order to prevent pollution and any possible undiscovered consequences of their presence in the environment

Awakening the sleeping giant of urban green in times of crisis—coverage, co-creation and practical guidelines for optimizing biodiversity-friendly and health-promoting residential greenery

As multiple crises deepen existing inequalities in urban societies within and between neighborhoods, strategically integrating nature-based solutions into the living environment can help reduce negative impacts and improve public health, social cohesion, and well-being. Compared to public green such as parks, semi-public residential greenery is rarely studied, is regularly overlooked by planners, and often receives step-motherly treatment from architects and housing companies. We approximated the area of residential greenery of modernist multi-story apartment complexes in Berlin, Germany. We surveyed residents’ suggestions for improving their living environments in vulnerable neighborhoods, report on co-creation experiences, and provide a practical guideline for optimizing health-promoting residential green spaces. The semi-public open space on the doorstep of two-thirds of Berlin’s population is highly fragmented and, in total, has a similar area as the public green spaces and a great potential for qualitative development. Just as the suitability of different nature-based solutions to be integrated into the residential greenery depends on building types, resident demands differ between neighborhoods. Residents called for more involvement in design, implementation, and maintenance, frequently proposing that biodiversity-friendly measures be included. As there is no universal solution even for neighborhoods sharing similar structural and socioeconomic parameters, we propose, and have tested, an optimization loop for health-promoting residential greening that involves exploring residents’ needs and co-creating local solutions for urban regeneration processes that can be initiated by different actors using bottom-up and/or top-down approaches in order to unlock this potential for healthy, livable and biodiversity friendly cities.Peer Reviewe

Awakening the sleeping giant of urban green in times of crisis—coverage, co-creation and practical guidelines for optimizing biodiversity-friendly and health-promoting residential greenery

As multiple crises deepen existing inequalities in urban societies within and between neighborhoods, strategically integrating nature-based solutions into the living environment can help reduce negative impacts and improve public health, social cohesion, and well-being. Compared to public green such as parks, semi-public residential greenery is rarely studied, is regularly overlooked by planners, and often receives step-motherly treatment from architects and housing companies. We approximated the area of residential greenery of modernist multi-story apartment complexes in Berlin, Germany. We surveyed residents’ suggestions for improving their living environments in vulnerable neighborhoods, report on co-creation experiences, and provide a practical guideline for optimizing health-promoting residential green spaces. The semi-public open space on the doorstep of two-thirds of Berlin’s population is highly fragmented and, in total, has a similar area as the public green spaces and a great potential for qualitative development. Just as the suitability of different nature-based solutions to be integrated into the residential greenery depends on building types, resident demands differ between neighborhoods. Residents called for more involvement in design, implementation, and maintenance, frequently proposing that biodiversity-friendly measures be included. As there is no universal solution even for neighborhoods sharing similar structural and socioeconomic parameters, we propose, and have tested, an optimization loop for health-promoting residential greening that involves exploring residents’ needs and co-creating local solutions for urban regeneration processes that can be initiated by different actors using bottom-up and/or top-down approaches in order to unlock this potential for healthy, livable and biodiversity friendly cities

Evaluation of rate law approximations in bottom-up kinetic models of metabolism.

BackgroundThe mechanistic description of enzyme kinetics in a dynamic model of metabolism requires specifying the numerical values of a large number of kinetic parameters. The parameterization challenge is often addressed through the use of simplifying approximations to form reaction rate laws with reduced numbers of parameters. Whether such simplified models can reproduce dynamic characteristics of the full system is an important question.ResultsIn this work, we compared the local transient response properties of dynamic models constructed using rate laws with varying levels of approximation. These approximate rate laws were: 1) a Michaelis-Menten rate law with measured enzyme parameters, 2) a Michaelis-Menten rate law with approximated parameters, using the convenience kinetics convention, 3) a thermodynamic rate law resulting from a metabolite saturation assumption, and 4) a pure chemical reaction mass action rate law that removes the role of the enzyme from the reaction kinetics. We utilized in vivo data for the human red blood cell to compare the effect of rate law choices against the backdrop of physiological flux and concentration differences. We found that the Michaelis-Menten rate law with measured enzyme parameters yields an excellent approximation of the full system dynamics, while other assumptions cause greater discrepancies in system dynamic behavior. However, iteratively replacing mechanistic rate laws with approximations resulted in a model that retains a high correlation with the true model behavior. Investigating this consistency, we determined that the order of magnitude differences among fluxes and concentrations in the network were greatly influential on the network dynamics. We further identified reaction features such as thermodynamic reversibility, high substrate concentration, and lack of allosteric regulation, which make certain reactions more suitable for rate law approximations.ConclusionsOverall, our work generally supports the use of approximate rate laws when building large scale kinetic models, due to the key role that physiologically meaningful flux and concentration ranges play in determining network dynamics. However, we also showed that detailed mechanistic models show a clear benefit in prediction accuracy when data is available. The work here should help to provide guidance to future kinetic modeling efforts on the choice of rate law and parameterization approaches

The interplay of intrinsic and extrinsic bounded noises in genetic networks

After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a genetic network. The influence of intrinsic and extrinsic noises on genetic networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: $(i)$ the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, $(ii)$ a model of enzymatic futile cycle and $(iii)$ a genetic toggle switch. In $(ii)$ and $(iii)$ we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possibile functional role of bounded noises

Accelerating the Gillespie τ-Leaping Method Using Graphics Processing Units

The Gillespie τ-Leaping Method is an approximate algorithm that is faster than the exact Direct Method (DM) due to the progression of the simulation with larger time steps. However, the procedure to compute the time leap τ is quite expensive. In this paper, we explore the acceleration of the τ-Leaping Method using Graphics Processing Unit (GPUs) for ultra-large networks ( reaction channels). We have developed data structures and algorithms that take advantage of the unique hardware architecture and available libraries. Our results show that we obtain a performance gain of over 60x when compared with the best conventional implementations

Confidence from uncertainty - A multi-target drug screening method from robust control theory

<p>Abstract</p> <p>Background</p> <p>Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.</p> <p>Results</p> <p>We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.</p> <p>Conclusions</p> <p>The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.</p

Multigene prognostic tests in breast cancer: past, present, future

There is growing consensus that multigene prognostic tests provide useful complementary information to tumor size and grade in estrogen receptor (ER)-positive breast cancers. The tests primarily rely on quantification of ER and proliferation-related genes and combine these into multivariate prediction models. Since ER-negative cancers tend to have higher proliferation rates, the prognostic value of current multigene tests in these cancers is limited. First-generation prognostic signatures (Oncotype DX, MammaPrint, Genomic Grade Index) are substantially more accurate to predict recurrence within the first 5 years than in later years. This has become a limitation with the availability of effective extended adjuvant endocrine therapies. Newer tests (Prosigna, EndoPredict, Breast Cancer Index) appear to possess better prognostic value for late recurrences while also remaining predictive of early relapse. Some clinical prediction problems are more difficult to solve than others: there are no clinically useful prognostic signatures for ER-negative cancers, and drug-specific treatment response predictors also remain elusive. Emerging areas of research involve the development of immune gene signatures that carry modest but significant prognostic value independent of proliferation and ER status and represent candidate predictive markers for immune-targeted therapies. Overall metrics of tumor heterogeneity and genome integrity (for example, homologue recombination deficiency score) are emerging as potential new predictive markers for platinum agents. The recent expansion of high-throughput technology platforms including low-cost sequencing of circulating and tumor-derived DNA and RNA and rapid reliable quantification of microRNA offers new opportunities to build extended prediction models across multiplatform data