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Neuronal differentiation is regulated by nerve growth factor (NGF) and other neurotrophins. We explored the impact of NGF on mitochondrial dynamics and metabolism through time-lapse imaging, metabolomics profiling, and computer modeling studies. We show that NGF may direct differentiation by stimulating fission, thereby causing selective mitochondrial network fragmentation and mitophagy, ultimately leading to increased mitochondrial quality and respiration. Then, we reconstructed the dynamic fusion–fission–mitophagy cycling of mitochondria in a computer model, integrating these processes into a single network mechanism. Both the computational model and the simulations are able to reproduce the proposed mechanism in terms of mitochondrial dynamics, levels of reactive oxygen species (ROS), mitophagy, and mitochondrial quality, thus providing a computational tool for the interpretation of the experimental data and for future studies aiming to detail further the action of NGF on mitochondrial processes. We also show that changes in these mitochondrial processes are intertwined with a metabolic function of NGF in differentiation: NGF directs a profound metabolic rearrangement involving glycolysis, TCA cycle, and the pentose phosphate pathway, altering the redox balance. This metabolic rewiring may ensure: (a) supply of both energy and building blocks for the anabolic processes needed for morphological reorganization, as well as (b) redox homeostasis.

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

This paper analyses the role of sector coupling towards 2050 in the energy system of Northern-central Europe when pursuing the green transition. Impacts of restricted onshore wind potential and transmission expansion are considered. Optimisation of the capacity development and operation of the energy system towards 2050 is performed with the energy system model Balmorel. Generation, storage, transmission expansion, district heating, carbon capture and storage, and synthetic gas units compete with each other. The results show how sector coupling leads to a change of paradigm: The electricity system moves from a system where generation adapts to inflexible demand, to a system where flexible demand adapts to variable generation. Sector coupling increases electricity demand, variable renewable energy, heat storage capacity, and electricity and district heating transmission expansion towards 2050. Non-restricted investments in onshore wind and electricity transmission reduce emissions and costs considerably (especially with high sector coupling) with savings of 78.7€2016/person/year. Investments in electric power-to-heat units are key to reduce costs and emissions in the heat sector. The scenarios with the highest sector coupling achieve the highest emission reduction by 2045: 76% greenhouse gases reduction with respect to 1990 levels, which highlights the value of sector coupling to achieve the green transition.

We here apply a control analysis and various types of stability analysis to an in silico model of innate immunity that addresses the management of inflammation by a therapeutic peptide. Motivation is the observation, both in silico and in experiments, that this therapy is not robust. Our modeling results demonstrate how (1) the biological phenomena of acute and chronic modes of inflammation may reflect an inherently complex bistability with an irrevertible flip between the two modes, (2) the chronic mode of the model has stable, sometimes unique, steady states, while its acute-mode steady states are stable but not unique, (3) as witnessed by TNF levels, acute inflammation is controlled by multiple processes, whereas its chronic-mode inflammation is only controlled by TNF synthesis and washout, (4) only when the antigen load is close to the acute mode's flipping point, many processes impact very strongly on cells and cytokines, (5) there is no antigen exposure level below which reduction of the antigen load alone initiates a flip back to the acute mode, and (6) adding healthy fibroblasts makes the transition from acute to chronic inflammation revertible, although (7) there is a window of antigen load where such a therapy cannot be effective. This suggests that triple therapies may be essential to overcome chronic inflammation. These may comprise (1) anti-immunoglobulin light chain peptides, (2) a temporarily reduced antigen load, and (3a) fibroblast repopulation or (3b) stem cell strategies.

Objective: Juvenile dermatomyositis (DM) is a heterogeneous systemic immune-mediated vasculopathy. This study was undertaken to 1) identify inflammation/endothelial dysfunction–related biomarker profiles reflecting disease severity at diagnosis, and 2) establish whether such biomarker profiles could be used for predicting the response to treatment in patients with juvenile DM. Methods: In total, 39 biomarkers related to activation of endothelial c

Contains fulltext : 220769.pdf (Publisher’s version ) (Open Access), OBJECTIVE: Juvenile dermatomyositis (DM) is a heterogeneous systemic immune-mediated vasculopathy. This study was undertaken to 1) identify inflammation/endothelial dysfunction-related biomarker profiles reflecting disease severity at diagnosis, and 2) establish whether such biomarker profiles could be used for predicting the response to treatment in patients with juvenile DM. METHODS: In total, 39 biomarkers related to activation of endothelial cells, endothelial dysfunction, and inflammation were measured using multiplex technology in serum samples from treatment-naive patients with juvenile DM from 2 independent cohorts (n = 30 and n = 29). Data were analyzed by unsupervised hierarchical clustering, nonparametric tests with correction for multiple comparisons, and Kaplan-Meier tests with Cox proportional hazards models for analysis of treatment duration. Myositis-specific antibodies (MSAs) were measured in the patients' serum using line blot assays. RESULTS: Severe vasculopathy in patients with juvenile DM was associated with low serum levels of intercellular adhesion molecule 1 (Spearman's rho [rs ] = 0.465, P = 0.0111) and high serum levels of endoglin (rs = -0.67, P < 0.0001). In the discovery cohort, unsupervised hierarchical clustering analysis of the biomarker profiles yielded 2 distinct patient clusters, of which the smaller cluster (cluster 1; n = 8) exhibited high serum levels of CXCL13, CCL19, galectin-9, CXCL10, tumor necrosis factor receptor type II (TNFRII), and galectin-1 (false discovery rate <0.0001), and this cluster had greater severity of muscle disease and global disease activity (each P < 0.05 versus cluster 2). In the validation cohort, correlations between the serum levels of galectin-9, CXCL10, TNFRII, and galectin-1 and the severity of global disease activity were confirmed (rs = 0.40-0.52, P < 0.05). Stratification of patients according to the 4 confirmed biomarkers identified a cluster of patients with severe symptoms (comprising 64.7%

By their definition, inadvertent exposure to endocrine disrupting compounds (EDCs) intervenes with the endocrine signalling system, even at low dose. On the one hand, some EDCs are used as important pharmaceutical drugs that one would not want to dismiss. On the other hand, these pharmaceutical drugs are having off-target effects and increasingly significant exposure to the general population with unwanted health implications. Flutamide, one of the top pharmaceutical products marketed all over the world for the treatment of prostate cancer, is also a pollutant. Its therapeutic action mainly depends on targeting the androgen receptors and inhibiting the androgen action that is essential for growth and survival of prostate tissue. Currently flutamide is of concern with respect to its categorization as an endocrine disruptor. In this work we have developed a physiologically based pharmacokinetic (PBPK) model of flutamide that could serve as a standard tool for its human risk assessment. First we built the model for rat (where many parameters have been measured). The rat PBPK model was extrapolated to human where the re-parameterization involved human-specific physiology, metabolic kinetics derived from in-vitro studies, and the partition coefficient same as the rat model. We have harmonized the model by integrating different sets of in-vitro, in-vivo and physiological data into a PBPK model. Then the model was used to simulate different exposure scenarios and the results were compared against the observed data. Both uncertainty and sensitivity analysis was done. Since this new whole-body PBPK model can predict flutamide concentrations not only in plasma but also in various organs, the model may have clinical applications in efficacy and safety assessment of flutamide. The model can also be used for reverse dosimetry in the context of interpreting the available biomonitoring data to estimate the degree to which the population is currently being exposed, and a tool fo

Contains fulltext : 220769.pdf (Publisher’s version ) (Open Access), OBJECTIVE: Juvenile dermatomyositis (DM) is a heterogeneous systemic immune-mediated vasculopathy. This study was undertaken to 1) identify inflammation/endothelial dysfunction-related biomarker profiles reflecting disease severity at diagnosis, and 2) establish whether such biomarker profiles could be used for predicting the response to treatment in patients with juvenile DM. METHODS: In total, 39 biomarkers related to activation of endothelial cells, endothelial dysfunction, and inflammation were measured using multiplex technology in serum samples from treatment-naive patients with juvenile DM from 2 independent cohorts (n = 30 and n = 29). Data were analyzed by unsupervised hierarchical clustering, nonparametric tests with correction for multiple comparisons, and Kaplan-Meier tests with Cox proportional hazards models for analysis of treatment duration. Myositis-specific antibodies (MSAs) were measured in the patients' serum using line blot assays. RESULTS: Severe vasculopathy in patients with juvenile DM was associated with low serum levels of intercellular adhesion molecule 1 (Spearman's rho [rs ] = 0.465, P = 0.0111) and high serum levels of endoglin (rs = -0.67, P < 0.0001). In the discovery cohort, unsupervised hierarchical clustering analysis of the biomarker profiles yielded 2 distinct patient clusters, of which the smaller cluster (cluster 1; n = 8) exhibited high serum levels of CXCL13, CCL19, galectin-9, CXCL10, tumor necrosis factor receptor type II (TNFRII), and galectin-1 (false discovery rate <0.0001), and this cluster had greater severity of muscle disease and global disease activity (each P < 0.05 versus cluster 2). In the validation cohort, correlations between the serum levels of galectin-9, CXCL10, TNFRII, and galectin-1 and the severity of global disease activity were confirmed (rs = 0.40-0.52, P < 0.05). Stratification of patients according to the 4 confirmed biomarkers identified a cluster of patients with severe symptoms (comprising 64.7%

By their definition, inadvertent exposure to endocrine disrupting compounds (EDCs) intervenes with the endocrine signalling system, even at low dose. On the one hand, some EDCs are used as important pharmaceutical drugs that one would not want to dismiss. On the other hand, these pharmaceutical drugs are having off-target effects and increasingly significant exposure to the general population with unwanted health implications. Flutamide, one of the top pharmaceutical products marketed all over the world for the treatment of prostate cancer, is also a pollutant. Its therapeutic action mainly depends on targeting the androgen receptors and inhibiting the androgen action that is essential for growth and survival of prostate tissue. Currently flutamide is of concern with respect to its categorization as an endocrine disruptor. In this work we have developed a physiologically based pharmacokinetic (PBPK) model of flutamide that could serve as a standard tool for its human risk assessment. First we built the model for rat (where many parameters have been measured). The rat PBPK model was extrapolated to human where the re-parameterization involved human-specific physiology, metabolic kinetics derived from in-vitro studies, and the partition coefficient same as the rat model. We have harmonized the model by integrating different sets of in-vitro, in-vivo and physiological data into a PBPK model. Then the model was used to simulate different exposure scenarios and the results were compared against the observed data. Both uncertainty and sensitivity analysis was done. Since this new whole-body PBPK model can predict flutamide concentrations not only in plasma but also in various organs, the model may have clinical applications in efficacy and safety assessment of flutamide. The model can also be used for reverse dosimetry in the context of interpreting the available biomonitoring data to estimate the degree to which the population is currently being exposed, and a tool for t

By their definition, inadvertent exposure to endocrine disrupting compounds (EDCs) intervenes with the endocrine signalling system, even at low dose. On the one hand, some EDCs are used as important pharmaceutical drugs that one would not want to dismiss. On the other hand, these pharmaceutical drugs are having off-target effects and increasingly significant exposure to the general population with unwanted health implications. Flutamide, one of the top pharmaceutical products marketed all over the world for the treatment of prostate cancer, is also a pollutant. Its therapeutic action mainly depends on targeting the androgen receptors and inhibiting the androgen action that is essential for growth and survival of prostate tissue. Currently flutamide is of concern with respect to its categorization as an endocrine disruptor. In this work we have developed a physiologically based pharmacokinetic (PBPK) model of flutamide that could serve as a standard tool for its human risk assessment. First we built the model for rat (where many parameters have been measured). The rat PBPK model was extrapolated to human where the re-parameterization involved human-specific physiology, metabolic kinetics derived from in-vitro studies, and the partition coefficient same as the rat model. We have harmonized the model by integrating different sets of in-vitro, in-vivo and physiological data into a PBPK model. Then the model was used to simulate different exposure scenarios and the results were compared against the observed data. Both uncertainty and sensitivity analysis was done. Since this new whole-body PBPK model can predict flutamide concentrations not only in plasma but also in various organs, the model may have clinical applications in efficacy and safety assessment of flutamide. The model can also be used for reverse dosimetry in the context of interpreting the available biomonitoring data to estimate the degree to which the population is currently being exposed, and a tool for t

type:Journal Article, The complex ammonium transport and assimilation network of E. coli involves the ammonium transporter AmtB, the regulatory proteins GlnK and GlnB, and the central N-assimilating enzymes together with their highly complex interactions. The engineering and modelling of such a complex network seem impossible because functioning depends critically on a gamut of data known at patchy accuracy. We developed a way out of this predicament, which employs: (i) a constrained optimization-based technology for the simultaneous fitting of models to heterogeneous experimental data sets gathered through diverse experimental set-ups, (ii) a ‘rubber band method’ to deal with different degrees of uncertainty, both in experimentally determined or estimated parameter values and in measured transient or steady-state variables (training data sets), (iii) integration of human expertise to decide on accuracies of both parameters and variables, (iv) massive computation employing a fast algorithm and a supercomputer, (v) an objective way of quantifying the plausibility of models, which makes it possible to decide which model is the best and how much better that model is than the others. We applied the new technology to the ammonium transport and assimilation network, integrating recent and older data of various accuracies, from different expert laboratories. The kinetic model objectively ranked best, has E. coli's AmtB as an active transporter of ammonia to be assimilated with GlnK minimizing the futile cycling that is an inevitable consequence of intracellular ammonium accumulation. It is 130 times better than a model with facilitated passive transport of ammonia., source:https://doi.org/10.1038/s41540-019-0091-6

By their definition, inadvertent exposure to endocrine disrupting compounds (EDCs) intervenes with the endocrine signalling system, even at low dose. On the one hand, some EDCs are used as important pharmaceutical drugs that one would not want to dismiss. On the other hand, these pharmaceutical drugs are having off-target effects and increasingly significant exposure to the general population with unwanted health implications. Flutamide, one of the top pharmaceutical products marketed all over the world for the treatment of prostate cancer, is also a pollutant. Its therapeutic action mainly depends on targeting the androgen receptors and inhibiting the androgen action that is essential for growth and survival of prostate tissue. Currently flutamide is of concern with respect to its categorization as an endocrine disruptor. In this work we have developed a physiologically based pharmacokinetic (PBPK) model of flutamide that could serve as a standard tool for its human risk assessment. First we built the model for rat (where many parameters have been measured). The rat PBPK model was extrapolated to human where the re-parameterization involved human-specific physiology, metabolic kinetics derived from in-vitro studies, and the partition coefficient same as the rat model. We have harmonized the model by integrating different sets of in-vitro, in-vivo and physiological data into a PBPK model. Then the model was used to simulate different exposure scenarios and the results were compared against the observed data. Both uncertainty and sensitivity analysis was done. Since this new whole-body PBPK model can predict flutamide concentrations not only in plasma but also in various organs, the model may have clinical applications in efficacy and safety assessment of flutamide. The model can also be used for reverse dosimetry in the context of interpreting the available biomonitoring data to estimate the degree to which the population is currently being exposed, and a tool fo

In order to comply with European emission goals, the role of accurate grid representation in energy system optimization is getting increasingly more important with larger shares of variable renewable energies. This paper compares the transmission system representation approaches using flow-based (FB) modelling with Power Transfer Distributing Factor (PTDF) to the Net Transfer Capacity (NTC) in energy system models for investment optimization. The open-source model Balmorel was used in order to investigate the impact on important modelling results. Overall, electricity prices flattened on a spatial level using FB modelling compared to NTC. Additionally, a change of investment decision in generation capacities is identified. Locations of newly installed lines and production sides are shifted furthermore slightly when representing the AC-system and its physics through FB modelling. However, a rough spatial representation and the subsequent clustering of the lines creates an uncertainty about the impact of overestimated line capacities and the benefits compared to NTC. Therefore, further studies are needed to accurately assess the impact of the transition from the current NTC to FB methodology in investment decision optimization.

This study conducts an integrated energy system assessment to evaluate pathways for using locally distributed sustainable biomass resources in the conversion to renewable gas and liquid biofuels in future integrated energy systems. A modelling framework with a detailed spatiotemporal representation is used, optimising the usage and transportation of local biomass resources for producing renewable gas and renewable liquid fuels through the OptiFlow model, along with the comprehensive power and district heating model Balmorel, integrating short-term dynamics in the long-term planning horizon. Results for a fossil-independent Danish energy system by 2050 show that production of bio-jet fuel in Denmark would impose high pressure on national biomass resources. Electrofuels, such as biomethanol, have economic viability and promising potentials. The results regarding renewable gas production show that anaerobic co-digestion of a mixed feedstock to produce biogas, which is further upgraded to biomethane using water scrubbing for CO2 removal, would be the preferred option. Biorefineries are located near larger cities to benefit from economy of scale and access to large district heating networks, as excess heat from biorefineries could supply up to 21% of the national district heating demand. On the contrary, biogas plants would be located in the countryside, as the costs of transporting manure are a determining factor. Therefore, our study provides a novel modelling approach, which enables optimisation of geographical distributed resources for renewable gas and liquid fuel production, while taking synergies across energy vectors in account.

The European Union is pushing to achieve a sustainable, competitive and secure energy supply in Europe. This has translated into significant long-term renewable energy targets towards 2050, and the ambition to improve the European grid. A large share of this development is expected to occur in the North Sea. This paper investigates which transmission architecture is the most beneficial to integrate large shares of renewable energy in the North Sea region, and the consequences of the planning horizon when planning such a system towards 2050 are analysed. This is achieved by performing investment optimisation of generation and transmission for different scenarios. It is found that: 1) an integrated offshore grid configuration planned over a long planning horizon leads to cost minimization; 2) the grid topology is not likely to influence the penetration of variable renewable energy, but it will affect the contribution of each variable renewable energy type and the system costs; and 3) not taking the future into account when developing the energy system is likely to lead to a more expensive system. These results remark the importance of long-term planning horizon for energy systems and grid expansion and calls for a political focus on planning and international cooperation.

This study quantifies the economic potential of cross-border transmission to a decarbonized future Northwestern European power system through the energy model Balmorel. A scenario with modelled optimal transmission capacity at lowest total system costs is compared to the scenario with given capacity level of existing and planned projects. Increased transmission investments decrease total system costs and regional price difference. It benefits particularly wind power deployment and thus, lowers CO2 emissions in the power and heat sector. The impacts are, nevertheless, distributed asymmetrically to northern and western stakeholders. Northern consumers receive higher power prices, but the revenues of wind and hydropower producers also increases. Meanwhile, western consumers receive lower power prices, but gas power producer revenues decrease.

World is changing at dizzying speed. The Internet is not only fascinating buy is also rapidly affecting our work and life. How do we prepare students for jobs that have not yet been created, for technologies that have not yet been invented? It has been estimated that our current skill sets would last only “the next decade or two”. Knowledge is no longer limited to set theories or single idea or linear thinking. What is required is the capacity to think across disciplines, connect ideas and “construct information”. The distinguishing fact from fiction is essential in our digital age and requires, “the capacity of young people to see the world through different perspectives, appreciate different ideas, and be open to different cultures”. Information for tomorrow has to be for transformation. Hence “learn, unlearn, and relearn” is the modern mantra of education. In countries like India, where illiteracy and lack of education are still haunting is it possible to achieve digital empowerment and inclusive growth? Is digital disruption cost-effective? How to overcome technophobia? These are some of the research questions that this paper tries to address based on theoretical and empirical data. This paper explores ways and means of digitally empowering marginalised communities living in socio-economic backwardness and poverty. Our finding is that digitalization per se is a complex programme and evolves with the perception and participation of the stakeholders. It suggests blending of technological and human approaches that strengthen the enabling and evaluatory mechanisms of digital empowerment. Keywords: Digitalization, empowerment, growth, India, information

The recognition that neurogenesis does not stop with adolescence has spun off research towards the reduction of brain disorders by enhancing brain regeneration. Adult neurogenesis is one of the tougher problems of developmental biology as it requires the generation of complex intracellular and pericellular anatomies, amidst the danger of neuroinflammation. We here review how a multitude of regulatory pathways optimized for early neurogenesis has to be revamped into a new choreography of time dependencies. Distinct pathways need to be regulated, ranging from neural growth factor induced differentiation to mitochondrial bioenergetics, reactive oxygen metabolism, and apoptosis. Requiring much Gibbs energy consumption, brain depends on aerobic energy metabolism, hence on mitochondrial activity. Mitochondrial fission and fusion, movement and perhaps even mitoptosis, thereby come into play. All these network processes are interlinked and involve a plethora of molecules. We recommend a deep thinking approach to adult neurobiology

There is a large amount of parametric uncertainties that might affect long-term energy planning, due to the inherent variability connected to the future. Most of these uncertainties are stochastic, i.e. they cannot be reduced, but can be better characterized. In an attempt to address this issue, studies often explore different alternative scenarios or perform local sensitivity analyses. While acknowledging their importance, it is evident that their traditional scope must be rethought, as those methods cannot consider interactions among parameters and hence might omit parameters that are highly influential. This study aims to explore the whole uncertainty range in order to identify the most critical parameters towards fossil-free energy systems with high integration of wind-based electricity. Denmark is used as a case study of a country with large wind resources, which are increasingly exploited. It pursues three steps: (1) selection of parameters and characterization of their uncertainties, (2) global sensitivity analyses through Morris sampling, and (3) uncertainty propagation and Monte Carlo runs using Latin Hypercube sampling. Offshore wind upscaling will depend on technological improvements related to capital costs or efficiencies as well as on the system integration constraints. Hence, increasing deployments of offshore wind would require policies that foster technological learning, while promoting the cost-efficient integration of an increase in participation in the power mix, such as grid transmission expansion. Therefore, methods that deal with the whole uncertainty space should, to a larger extent, be implemented when uncertainties are assessed in association with long-term planning of systems with high integration of fluctuating renewable energy.

Desulfitobacterium hafniense Y51 has been widely used in investigations of perchloroethylene (PCE) biodegradation, but limited information exists on its other physiological capabilities. We investigated how D. hafniense Y51 confronts the debilitating limitations of not having enough electron donor (lactate), or electron acceptor (fumarate) during cultivation in chemostats. The residual concentrations of the substrates supplied in excess were much lower than expected. Transcriptomics, proteomics and fluxomics were integrated to investigate how this phenomenon was regulated. Through diverse regulation at both transcriptional and translational levels, strain Y51 turned to fermenting the excess lactate and disproportionating the excess fumarate under fumarate- and lactate-limiting conditions respectively. Genes and proteins related to the utilization of a variety of alternative electron donors and acceptors absent from the medium were induced, apparently involving the Wood–Ljungdahl pathway. Through this metabolic flexibility, D. hafniense Y51 may be able to switch between different metabolic capabilities under limiting conditions.

Classical Thermodynamics restricts engineering. These restrictions are independent of mechanism and kinetics, and thereby inescapable. Forgetting these restrictions can lead to over-optimistic designs for making bio-plastics from waste, and to erroneous ideas on early or new Life on this or other planets. This can be rectified by putting the thermodynamics in place. Is every biochemical network design feasible, provided one puts classical thermodynamics in place? Or, are there other, ill-recognized, generic restrictions to bioengineering? For a while a Non-Equilibrium Thermodynamics (NET) has been trying to discover behaviors of dynamical systems away from equilibrium that are completely independent of kinetics and mechanism and thereby not engineerable. The principles discovered were of limited use to bioengineering however. We here show that processes away from equilibrium must indeed depend on kinetics and mechanism, but, importantly, not on all kinetic and mechanistic details: There are limitations to what the engineering of mechanisms and kinetics can achieve. It is of course better to recognize what is impossible before trying to engineer the impossible. Importantly, the new NET methodology also shows that system properties that are possible, can be engineered only in certain ways. The new NET methodology also enables to understand, and perhaps engineer towards, a performance that, by adjusting the network, remains optimal when conditions are changing. Using our in silico discovery tool, we show that this may indeed occur in the Archeon S. solfataricus. What we call ‘variomatic’ gear shifting is a way that some cells may use to self-engineer their ways to maximal growth rates in environments that lack robust resources, such as in environments with fluctuating oxygen levels. Population heterogeneity is another mechanism that can increase the robustness of a cell factory. We discuss a NET principle that suggests ways in which one can engineer the cells’ diver

Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

type:Conference Paper, The Third BMIRC International Symposium for Virtual Physiological Human, March 5-6, 2015, Iizuka, Japan

Desulfitobacterium hafniense Y51 has been widely used in investigations of perchloroethylene (PCE) biodegradation, but limited information exists on its other physiological capabilities. We investigated how D. hafniense Y51 confronts the debilitating limitations of not having enough electron donor (lactate), or electron acceptor (fumarate) during cultivation in chemostats. The residual concentrations of the substrates supplied in excess were much lower than expected. Transcriptomics, proteomics and fluxomics were integrated to investigate how this phenomenon was regulated. Through diverse regulation at both transcriptional and translational levels, strain Y51 turned to fermenting the excess lactate and disproportionating the excess fumarate under fumarate- and lactate-limiting conditions respectively. Genes and proteins related to the utilization of a variety of alternative electron donors and acceptors absent from the medium were induced, apparently involving the Wood–Ljungdahl pathway. Through this metabolic flexibility, D. hafniense Y51 may be able to switch between different metabolic capabilities under limiting conditions.

Classical Thermodynamics restricts engineering. These restrictions are independent of mechanism and kinetics, and thereby inescapable. Forgetting these restrictions can lead to over-optimistic designs for making bio-plastics from waste, and to erroneous ideas on early or new Life on this or other planets. This can be rectified by putting the thermodynamics in place. Is every biochemical network design feasible, provided one puts classical thermodynamics in place? Or, are there other, ill-recognized, generic restrictions to bioengineering? For a while a Non-Equilibrium Thermodynamics (NET) has been trying to discover behaviors of dynamical systems away from equilibrium that are completely independent of kinetics and mechanism and thereby not engineerable. The principles discovered were of limited use to bioengineering however. We here show that processes away from equilibrium must indeed depend on kinetics and mechanism, but, importantly, not on all kinetic and mechanistic details: There are limitations to what the engineering of mechanisms and kinetics can achieve. It is of course better to recognize what is impossible before trying to engineer the impossible. Importantly, the new NET methodology also shows that system properties that are possible, can be engineered only in certain ways. The new NET methodology also enables to understand, and perhaps engineer towards, a performance that, by adjusting the network, remains optimal when conditions are changing. Using our in silico discovery tool, we show that this may indeed occur in the Archeon S. solfataricus. What we call ‘variomatic’ gear shifting is a way that some cells may use to self-engineer their ways to maximal growth rates in environments that lack robust resources, such as in environments with fluctuating oxygen levels. Population heterogeneity is another mechanism that can increase the robustness of a cell factory. We discuss a NET principle that suggests ways in which one can engineer the cells’ diver

As the world progresses towards a cleaner energy future with more variable renewable energy sources, energy system models are required to deal with new challenges. This article describes design, development and applications of the open source energy system model Balmorel, which is a result of a long and fruitful cooperation between public and private institutions within energy system research and analysis. The purpose of the article is to explain the modelling approach, to highlight strengths and challenges of the chosen approach, to create awareness about the possible applications of Balmorel as well as to inspire to new model developments and encourage new users to join the community. Some of the key strengths of the model are the flexible handling of the time and space dimensions and the combination of operation and investment optimisation. Its open source character enables diverse, worldwide applications for exploratory energy scenarios as well as policy analysis as the applications outlined demonstrate. The existing functionality and structural suitability for extensions make it a useful tool for assessing challenges of the ongoing energy transitions. Numerous model extensions have been developed as different challenges to the energy transition have arisen. One of these includes the option of running the model with unit commitment. To meet new challenges, further development is needed and consequently the article outlines suggestions for future development, such as including transport of local biomass as part of the optimisation and speeding up the model.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 61-62)., We need better methods of dynamic parameter estimation because robots are becoming more complex and their interactions with the unmodeled world are becoming more frequent. These methods should be fast, reliable, and easily adaptable to various classes of robots. Here, we design and build a parameter estimation system in a robotic simulation and controls framework called Drake. The parameter estimation works by minimizing the error between a model and the observed data from a given trajectory. Using our estimator, we then compare the minimization of three types of errors, generated by the dynamics model, energetic model, and simulation model. We quantify the speed and performance of each algorithm on the Acrobot system., by Hans Susilo., M. Eng.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 61-62)., We need better methods of dynamic parameter estimation because robots are becoming more complex and their interactions with the unmodeled world are becoming more frequent. These methods should be fast, reliable, and easily adaptable to various classes of robots. Here, we design and build a parameter estimation system in a robotic simulation and controls framework called Drake. The parameter estimation works by minimizing the error between a model and the observed data from a given trajectory. Using our estimator, we then compare the minimization of three types of errors, generated by the dynamics model, energetic model, and simulation model. We quantify the speed and performance of each algorithm on the Acrobot system., by Hans Susilo., M. Eng.

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016., This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections., Cataloged from student-submitted PDF version of thesis., Includes bibliographical references (pages 61-62)., We need better methods of dynamic parameter estimation because robots are becoming more complex and their interactions with the unmodeled world are becoming more frequent. These methods should be fast, reliable, and easily adaptable to various classes of robots. Here, we design and build a parameter estimation system in a robotic simulation and controls framework called Drake. The parameter estimation works by minimizing the error between a model and the observed data from a given trajectory. Using our estimator, we then compare the minimization of three types of errors, generated by the dynamics model, energetic model, and simulation model. We quantify the speed and performance of each algorithm on the Acrobot system., by Hans Susilo., M. Eng.

Quantitative and detailed trade policy information and analysis are more necessary now than they have ever been. Questions have been asked about whether the gains from trade exceed the costs of trade. Concerns regarding the distributional consequences of trade reforms have also been expressed. Good policy needs to be backed by good analysis. There needs to be a rethinking of India’s foreign trade policy both in theory and in practice so as to make it more relevant and rewarding. Visibly India now focuses on exports as a thrust area, pursuing customers aboard. Make in India is the new (wake-up) call. What is in the upcoming foreign trade policy of India? This paper discusses the paradigm shifts in India’s foreign trade policy.

We present a systems biology view on pseudoenzymes that acknowledges that genes are not selfish: the genome is. With network function as the selectable unit, there has been an evolutionary bonus for recombination of functions of and within proteins. Many proteins house a functionality by which they 'read' the cell's state, and one by which they 'write' and thereby change that state. Should the writer domain lose its cognate function, a 'pseudoenzyme' or 'pseudosignaler' arises. GlnK involved in Escherichia coli ammonia assimilation may well be a pseudosignaler, associating 'reading' the nitrogen state of the cell to 'writing' the ammonium uptake activity. We identify functional pseudosignalers in the cyclin-dependent kinase complexes regulating cell-cycle progression. For the mitogen-activated protein kinase pathway, we illustrate how a 'dead' pseudosignaler could produce potentially selectable functionalities. Four billion years ago, bioenergetics may have shuffled 'electron-writers', producing various networks that all served the same function of anaerobic ATP synthesis and carbon assimilation from hydrogen and carbon dioxide, but at different ATP/acetate ratios. This would have enabled organisms to deal with variable challenges of energy need and substrate supply. The same principle might enable 'gear-shifting' in real time, by dynamically generating different pseudo-redox enzymes, reshuffling their coenzymes, and rerouting network fluxes. Non-stationary pH gradients in thermal vents together with similar such shuffling mechanisms may have produced a first selectable proton-motivated pyrophosphate synthase and subsequent ATP synthase. A combination of functionalities into enzymes, signalers, and the pseudoversions thereof may offer fitness in terms of plasticity, both in real time and in evolution.

ADP-ribosylation is a posttranslational modification that exists in monomeric and polymeric forms. Whereas the writers (e.g. ARTD1/PARP1) and erasers (e.g. PARG, ARH3) of poly-ADP-ribosylation (PARylation) are relatively well described, the enzymes involved in mono-ADP-ribosylation (MARylation) have been less well investigated. While erasers for the MARylation of glutamate/aspartate and arginine have been identified, the respective enzymes with specificity for serine were missing. Here we report that, in vitro, ARH3 specifically binds and demodifies proteins and peptides that are MARylated. Molecular modeling and site-directed mutagenesis of ARH3 revealed that numerous residues are critical for both the mono- and the poly-ADP-ribosylhydrolase activity of ARH3. Notably, a mass spectrometric approach showed that ARH3-deficient mouse embryonic fibroblasts are characterized by a specific increase in serine-ADP-ribosylation in vivo under untreated conditions as well as following hydrogen peroxide stress. Together, our results establish ARH3 as a serine mono-ADP-ribosylhydrolase and as an important regulator of the basal and stress-induced ADP-ribosylome.

Item does not contain fulltext, Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs, we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated "CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)," "CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)," "CNS high-grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1)," and "CNS high-grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR)," will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors.

The scaling relations and the star formation laws for molecular cloud complexes in the Milky Way is investigated. We compare their masses $M_{\rm gas}$, mass surface densities $\Sigma_{M_{\rm gas}}$, radii $R$, velocity dispersions $\sigma$, star formation rates $SFR$, and SFR densities $\Sigma_{\rm SFR}$ with those of structures ranging from cores, clumps, Giant Molecular Clouds (GMCs), to Molecular Cloud Complexes (MCCs), and to Galaxies, spanning 8 orders of magnitudes in size and 13 orders of magnitudes in mass. MCC are mostly large ($R>50$ pc), massive ($\sim 10^{6}$\,\msun) gravitationally unbound cloud structures. This results in the following universal relations: $\sigma\sim R^{0.5}$, $M_{\rm gas}\sim R^{2}$, $\Sigma_{\rm SFR}\sim \Sigma_{M_{\rm gas}}^{1.5}$, ${SFR}\sim {M_{\rm gas}}^{0.9}$, and ${SFR}\sim {\sigma}^{2.7}$. Variations in the slopes and the coefficients of these relations are found at individual scales signifying different physics acting at different scales. Additionally, there are breaks at the MCC scale in the $\sigma-R$ relation and between the starburst and the normal star-forming objects in the $SFR-M_{\rm gas}$ and $\Sigma_{\rm SFR}$-$\Sigma_{\rm M_{\rm gas}}$ relations. We propose to use the Schmidt-Kennicutt diagram to distinguish the starburst from the normal star-forming structures by applying a $\Sigma_{M_{\rm gas}}$ threshold of $\sim100$\,\msun pc$^{-2}$ and a $\Sigma_{\rm SFR}$ threshold of 1\,\msun yr$^{-1}$ kpc$^{-2}$. Mini-starburst complexes have enhanced $\Sigma_{\rm SFR}$ ($>$1\,\msun yr$^{-1}$ kpc$^{-2}$), probably caused by dynamic events such as radiation pressure, colliding flows, or spiral arm gravitational instability. Because of the dynamical evolution, gravitational boundedness does not play a significant role in characterizing the star formation activity of MCCs, especially the mini-starburst complexes., Comment: submitted on May 02 2016, accepted to Astrophysical Journal on Oct 09, 2016, comments are welcome

__Background:__ To date, women are most often diagnosed with bacterial vaginosis (BV) using microscopy based Nugent scoring or Amsel criteria. However, the accuracy is less than optimal. The aim of the present study was to confirm the identity of known BV-associated composition profiles and evaluate indicators for BV using three molecular methods. __Methods:__ Evaluation of indicators for BV was carried out by 16S rRNA amplicon sequencing of the V5-V7 region, a tailor-made 16S rRNA oligonucleotide-based microarray, and a PCR-based profiling technique termed IS-profiling, which is based on fragment variability of the 16S-23S rRNA intergenic spacer region. An inventory of vaginal bacterial species was obtained from 40 females attending a Dutch sexually transmitted infection outpatient clinic, of which 20 diagnosed with BV (Nugent score 7-10), and 20 BV negative (Nugent score 0-3). __Results:__ Analysis of the bacterial communities by 16S rRNA amplicon sequencing revealed two clusters in the BV negative women, dominated by either Lactobacillus iners or Lactobacillus crispatus and three distinct clusters in the BV positive women. In the former, there was a virtually complete, negative correlation between L. crispatus and L. iners. BV positive subjects showed cluster profiles that were relatively high in bacterial species diversity and dominated by anaerobic species, including Gardnerella vaginalis, and

The scaling relations and star formation laws for molecular cloud complexes (MCCs) in the Milky Way are investigated. MCCs are mostly large (R> 50 pc), massive (similar to 106 M circle dot) gravitationally unbound cloud structures. We compare their masses M-gas, mass surface densities Sigma(Mgas), radii R, velocity dispersions sigma, star formation rates (SFRs), and SFR densities Sigma(SFR) with those of structures ranging from cores, clumps, and giant molecular clouds, to MCCs, and galaxies, spanning eight orders of magnitudes in size and 13 orders of magnitudes in mass. This results in the following universal relations: sigma similar to R-0.5, M-gas similar to R-2, Sigma(SFR) similar to Sigma(1.5)(Mgas) , SFR similar to M-gas(0.9) , and SFR similar to sigma(2.7) Variations in the slopes and coefficients of these relations are found at individual scales, signifying different physics acting at different scales. Additionally, there are breaks at the MCC scale in the sigma-R relation and between starburst and normal star-forming objects in the SFR-M-gas and Sigma(SFR)-Sigma(Mgas) gas relations. Therefore, we propose to use the Schmidt-Kennicutt diagram to distinguish starburst from normal star-forming structures by applying a SMgas threshold of similar to 100M circle dot pc (2) and a Sigma(SFR) threshold of 1M circle dot yr (1) kpc (2). Mini-starburst complexes are gravitationally unbound MCCs that have enhanced Sigma(SFR) (> 1M circle dot yr(-1) kpc(-2)), probably caused by dynamic events such as radiation pressure, colliding flows, or spiral arm gravitational instability. Because of dynamical evolution, gravitational boundedness does not play a significant role in regulating the star formation activity of MCCs, especially the mini-starburst complexes, which leads to the dynamical formation of massive stars and clusters. We emphasize the importance of understanding mini-starbursts in investigating the physics of starburst galaxies.

Item does not contain fulltext, Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs, we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated "CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)," "CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)," "CNS high-grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1)," and "CNS high-grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR)," will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors.

Item does not contain fulltext, Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein, we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well-defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs, we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated "CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)," "CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)," "CNS high-grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1)," and "CNS high-grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR)," will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors.

The scaling relations and star formation laws for molecular cloud complexes (MCCs) in the Milky Way are investigated. MCCs are mostly large (R> 50 pc), massive (similar to 106 M circle dot) gravitationally unbound cloud structures. We compare their masses M-gas, mass surface densities Sigma(Mgas), radii R, velocity dispersions sigma, star formation rates (SFRs), and SFR densities Sigma(SFR) with those of structures ranging from cores, clumps, and giant molecular clouds, to MCCs, and galaxies, spanning eight orders of magnitudes in size and 13 orders of magnitudes in mass. This results in the following universal relations: sigma similar to R-0.5, M-gas similar to R-2, Sigma(SFR) similar to Sigma(1.5)(Mgas) , SFR similar to M-gas(0.9) , and SFR similar to sigma(2.7) Variations in the slopes and coefficients of these relations are found at individual scales, signifying different physics acting at different scales. Additionally, there are breaks at the MCC scale in the sigma-R relation and between starburst and normal star-forming objects in the SFR-M-gas and Sigma(SFR)-Sigma(Mgas) gas relations. Therefore, we propose to use the Schmidt-Kennicutt diagram to distinguish starburst from normal star-forming structures by applying a SMgas threshold of similar to 100M circle dot pc (2) and a Sigma(SFR) threshold of 1M circle dot yr (1) kpc (2). Mini-starburst complexes are gravitationally unbound MCCs that have enhanced Sigma(SFR) (> 1M circle dot yr(-1) kpc(-2)), probably caused by dynamic events such as radiation pressure, colliding flows, or spiral arm gravitational instability. Because of dynamical evolution, gravitational boundedness does not play a significant role in regulating the star formation activity of MCCs, especially the mini-starburst complexes, which leads to the dynamical formation of massive stars and clusters. We emphasize the importance of understanding mini-starbursts in investigating the physics of starburst galaxies.