Evaluating the environmental performance of producing soil and surfaces through industrial symbiosis

Industrial symbiosis (IS), where different entities collaborate in the management of energy, utilities, materials, or services, has been identified as an approach to improve resource efficiency and circularity in industry. This article assesses the environmental performance of an IS network with firms involved in waste management, soil, surfaces, paper, lumber, and energy. The aim is to highlight the environmental performance of an IS network and pay particular attention to the improved performance for products in the IS network. Life cycle assessment is used to compare the current IS network with a reference scenario and a potential future development. The results suggest that there are significant benefits from the IS network. Large reductions in greenhouse gas (GHG) emissions and abiotic resource depletion were identified. Furthermore, large reductions in local impacts, namely eutrophication and acidification impacts are illustrated. It was shown that all firms in the network benefit from the synergies involved, with a large share of the benefits stemming from the facilitated exchanges with the waste management company. The replacement of conventional products and energy streams with bio‐based counterparts from within the network is of significant importance. Finally, the results point to the importance of the facilitation of by‐product synergies, and the significant value this creates in the region, with large potential to improve the environmental performance of firms and their products.     

Variation of energy flow and greenhouse gas emissions in vineyards located in Natura 2000 sites

A combination of agro-environmental indicators and energy analysis of a production system, such as vineyards located in Natura 2000 sites, for which little is known, may be a useful tool to decide best farm management practices with low greenhouse gas emissions. In randomly selected vineyards at six sites of Natura 2000 network in Cyprus, we evaluated the energy flow and the greenhouse gas emissions. Hierarchical cluster analysis using production coefficients and a topographic variable (altitude) was used to detect clusters of the studied vineyards. Three groups were revealed. Group 1 (eight vineyards) with the lowest energy inputs, followed by Group 2 (three vineyards) with intermediate inputs and Group 3 (three vineyards) with the highest energy inputs. Altitude, fertilizers, labour, fuel and transportation had the greatest contribution on cluster formation. Non-parametric comparisons concerning 28 indicators showed that ten indicators (total energy inputs, renewable and non-renewable energy inputs, fruit energy outputs, shoot energy outputs, total energy outputs, fruit production, CO₂, CH₄, and N₂O) were significantly higher in high energy input vineyards (Group 3) and three (energy use for renewable inputs, energy productivity and energy fruit efficiency) in low energy input vineyards (Group 1). Five indicators (energy use for fuel, transportation and non-renewable inputs, and energy efficiency for (shoot + fruit) outputs and renewable energy inputs) were significantly lower in Group 3 and two (intensity and non-renewable energy consumption) in Group 1. Similarities and/or dissimilarities among vineyards in Natura 2000 sites were related to altitude, production coefficients and local farming practices. These mixed results stress the importance of taking both local management indicators and vineyard topography into consideration when developing future agri-environment schemes, and suggest that local–regional interactions may affect Natura 2000 sites services and functions. It is important that EU agricultural policies are complemented by national–regional interventions in order to regulate the fragile balance between Natura 2000 sites and agriculture, reducing non-renewable energy inputs as percent of total energy inputs.     

Microrheological investigations give insights into the microstructure and functionality of pectin gels

Many of the functional attributes of pectin, whether in the plant cell wall or in engineered food materials, are linked to its gelling properties and in particular to its ability to assemble in the presence of calcium. Pectin’s fine structure and local concentration relative to that of its cross-linking ion play a major role in determining resultant gel micro-structures, and consequently the mechanical and transport properties of pectin matrices. Recent studies have sought to probe the basic properties of such calcium-induced matrices, using a light scattering technique called diffusing wave spectroscopy (DWS). In addition to the low frequency mechanical behaviour, which provides information about the nature and density of cross-links, microrheological measurements carried out with DWS are able to determine the high frequency behaviour, which is closely linked to the response of the basic strands of the network. By using these microrheological measurements, two distinct regimes have been identified into which pectin gels appear to fall: one corresponding to the presence of semi-flexible networks, a generally accepted paradigm in biological gels, and another where flexible networks dominate. In order to explain the origin of these dramatically different networks, distinct assembly pathways have been proposed in which the relative importance of the free energy gained by association and the frictional barrier to polymeric re-arrangement during network formation can differ significantly. By manipulating the local environment in the plant cell wall it is possible that Nature makes full use of both of these network types for fulfilling different tasks; such as providing strain-hardening, maximizing local elastic properties or controlling macromolecular transport.     

Global change synergies and trade‐offs between renewable energy and biodiversity

Reliance on fossil fuels is causing unprecedented climate change and is accelerating environmental degradation and global biodiversity loss. Together, climate change and biodiversity loss, if not averted urgently, may inflict severe damage on ecosystem processes, functions and services that support the welfare of modern societies. Increasing renewable energy deployment and expanding the current protected area network represent key solutions to these challenges, but conflicts may arise over the use of limited land for energy production as opposed to biodiversity conservation. Here, we compare recently identified core areas for the expansion of the global protected area network with the renewable energy potential available from land‐based solar photovoltaic, wind energy and bioenergy (in the form of Miscanthus × giganteus). We show that these energy sources have very different biodiversity impacts and net energy contributions. The extent of risks and opportunities deriving from renewable energy development is highly dependent on the type of renewable source harvested, the restrictions imposed on energy harvest and the region considered, with Central America appearing at particularly high potential risk from renewable energy expansion. Without restrictions on power generation due to factors such as production and transport costs, we show that bioenergy production is a major potential threat to biodiversity, while the potential impact of wind and solar appears smaller than that of bioenergy. However, these differences become reduced when energy potential is restricted by external factors including local energy demand. Overall, we found that areas of opportunity for developing solar and wind energy with little harm to biodiversity could exist in several regions of the world, with the magnitude of potential impact being particularly dependent on restrictions imposed by local energy demand. The evidence provided here helps guide sustainable development of renewable energy and contributes to the targeting of global efforts in climate mitigation and biodiversity conservation.     

Salt-Bridge Energetics in Halophilic Proteins

Halophilic proteins have greater abundance of acidic over basic and very low bulky hydrophobic residues. Classical electrostatic stabilization was suggested as the key determinant for halophilic adaptation of protein. However, contribution of specific electrostatic interactions (i.e. salt-bridges) to overall stability of halophilic proteins is yet to be understood. To understand this, we use Adaptive-Poison-Boltzmann-Solver Methods along with our home-built automation to workout net as well as associated component energy terms such as desolvation energy, bridge energy and background energy for 275 salt-bridges from 20 extremely halophilic proteins. We then perform extensive statistical analysis on general and energetic attributes on these salt-bridges. On average, 8 salt-bridges per 150 residues protein were observed which is almost twice than earlier report. Overall contributions of salt-bridges are −3.0 kcal mol⁻¹. Majority (78%) of salt-bridges in our dataset are stable and conserved in nature. Although, average contributions of component energy terms are equal, their individual details vary greatly from one another indicating their sensitivity to local micro-environment. Notably, 35% of salt-bridges in our database are buried and stable. Greater desolvation penalty of these buried salt-bridges are counteracted by stable network salt-bridges apart from favorable equal contributions of bridge and background terms. Recruitment of extensive network salt-bridges (46%) with a net contribution of −5.0 kcal mol⁻¹ per salt-bridge, seems to be a halophilic design wherein favorable average contribution of background term (−10 kcal mol⁻¹) exceeds than that of bridge term (−7 kcal mol⁻¹). Interiors of proteins from halophiles are seen to possess relatively higher abundance of charge and polar side chains than that of mesophiles which seems to be satisfied by cooperative network salt-bridges. Overall, our theoretical analyses provide insight into halophilic signature in its specific electrostatic interactions which we hope would help in protein engineering and bioinformatics studies.     

Coupling field theory with mesoscopic dynamical simulations of multicomponent lipid bilayers

A method for simulating a two-component lipid bilayer membrane in the mesoscopic regime is presented. The membrane is modeled as an elastic network of bonded points; the spring constants of these bonds are parameterized by the microscopic bulk modulus estimated from earlier atomistic nonequilibrium molecular dynamics simulations for several bilayer mixtures of DMPC and cholesterol. The modulus depends on the composition of a point in the elastic membrane model. The dynamics of the composition field is governed by the Cahn-Hilliard equation where a free energy functional models the coupling between the composition and curvature fields. The strength of the bonds in the elastic network are then modulated noting local changes in the composition and using a fit to the nonequilibrium molecular dynamics simulation data. Estimates for the magnitude and sign of the coupling parameter in the free energy model are made treating the bending modulus as a function of composition. A procedure for assigning the remaining parameters in the free energy model is also outlined. It is found that the square of the mean curvature averaged over the entire simulation box is enhanced if the strength of the bonds in the elastic network are modulated in response to local changes in the composition field. We suggest that this simulation method could also be used to determine if phase coexistence affects the stress response of the membrane to uniform dilations in area. This response, measured in the mesoscopic regime, is already known to be conditioned or renormalized by thermal undulations.     

Preference of small molecules for local minimum conformations when binding to proteins

It is well known that small molecules (ligands) do not necessarily adopt their lowest potential energy conformations when binding to proteins. Analyses of protein-bound ligand crystal structures have reportedly shown that many of them do not even adopt the conformations at local minima of their potential energy surfaces (local minimum conformations). The results of these analyses raise a concern regarding the validity of virtual screening methods that use ligands in local minimum conformations. Here we report a normal-mode-analysis (NMA) study of 100 crystal structures of protein-bound ligands. Our data show that the energy minimization of a ligand alone does not automatically stop at a local minimum conformation if the minimum of the potential energy surface is shallow, thus leading to the folding of the ligand. Furthermore, our data show that all 100 ligand conformations in their protein-bound ligand crystal structures are nearly identical to their local minimum conformations obtained from NMA-monitored energy minimization, suggesting that ligands prefer to adopt local minimum conformations when binding to proteins. These results both support virtual screening methods that use ligands in local minimum conformations and caution about possible adverse effect of excessive energy minimization when generating a database of ligand conformations for virtual screening.     

Multi/infinite dimensional neural networks, multi/infinite dimensional logic theory

A mathematical model of an arbitrary multi-dimensional neural network is developed and a convergence theorem for an arbitrary multi-dimensional neural network represented by a fully symmetric tensor is stated and proved. The input and output signal states of a multi-dimensional neural network/logic gate are related through an energy function, defined over the fully symmetric tensor (representing the connection structure of a multi-dimensional neural network). The inputs and outputs are related such that the minimum/maximum energy states correspond to the output states of the logic gate/neural network realizing a logic function. Similarly, a logic circuit consisting of the interconnection of logic gates, represented by a block symmetric tensor, is associated with a quadratic/higher degree energy function. Infinite dimensional logic theory is discussed through the utilization of infinite dimension/order tensors.     

Electrostatic and steric interactions determine bacteriorhodopsin single-molecule biomechanics

Bacteriorhodopsin (bR) is a haloarchaeal membrane protein that converts the energy of single photons into large structural changes to directionally pump protons across purple membrane. This is achieved by a complex combination of local dynamic interactions controlling bR biomechanics at the submolecular level, producing efficient amplification of the retinal photoisomerization. Using single molecule force spectroscopy at different salt concentrations, we show that tryptophan (Trp) residues use steric specific interactions to create a rigid scaffold in bR extracellular region and are responsible for the main unfolding barriers. This scaffold, which encloses the retinal, controls bR local mechanical properties and anchors the protein into the membrane. Furthermore, the stable Trp-based network allows ion binding to two specific sites on the extracellular loops (BC and FG), which are involved in proton release and lateral transport. In contrast, the cytoplasmic side of bR is mainly governed by relatively weak nonspecific electrostatic interactions that provide the flexibility necessary for large cytoplasmic structural rearrangements during the photocycle. The presence of an extracellular Trp-based network tightly enclosing the retinal seems common to most haloarchaeal rhodopsins, and could be relevant to their exceptional efficiency.     

A comparative study of clustering methods for molecular data

The research aim is to use three clustering technologies for establishing molecular data model of large size sets by comparison between low energy samples (LES) and local molecular samples (LMS). Hierarchical cluster of multi-level tree distance relation, competitive learning network of similar inputs falling into the same cluster and topological SOM are used to analyze 6,242 LES and 5,000 LMS. Our experiments show that in SOM, there are 24 to 25 Davies-Boulding clustering index and color map cluster units in the LES more than 10 to 12 in the LMS, which is consistent with the results of hierarchical cluster and competitive learning network in the rough. The hierarchical cluster reflects the biggest inter-cluster distance about 30 for the LES is far larger than that of LMS about 10. The intra-cluster distance of LES about 15 is also far bigger than that of LMS about 3. In SOM, there are more cluster borders of high values (black) reflecting large distance and more clusters in the D-matrix and U-matrix of LES than that of LMS, due to the biggest standard deviation range from -8 to 10 of samples feature of the LES is bigger than that of LMS from -2.5 to 2.5.     

Inactivation of Metabolic Genes Causes Short- and Long-Range dys-Regulation in Escherichia coli Metabolic Network

The metabolic network in E. coli can be severely affected by the inactivation of metabolic genes that are required to catabolize a nutrient (D-galactose). We hypothesized that the resulting accumulation of small molecules can yield local as well as systemic effects on the metabolic network. Analysis of metabolomics data in wild-type and D-galactose non-utilizing mutants, galT, galU and galE, reveal the large metabolic differences between the wild-type and the mutants when the strains were grown in D-galactose. Network mapping suggested that the enzymatic defects affected the metabolic modules located both at short- and long-ranges from the D-galactose metabolic module. These modules suggested alterations in glutathione, energy, nucleotide and lipid metabolism and disturbed carbon to nitrogen ratio in mutant strains. The altered modules are required for normal cell growth for the wild-type strain, explaining why the cell growth is inhibited in the mutants in the presence of D-galactose. Identification of these distance-based dys-regulations would enhance the systems level understanding of metabolic networks of microorganisms having importance in biomedical and biotechnological research.     

The geographical variation of network structure is scale dependent: understanding the biotic specialization of host–parasitoid networks

Research on the structure of ecological networks suggests that a number of universal patterns exist. Historically, biotic specialization has been thought to increase towards the Equator. Yet, recent studies have challenged this view showing non‐conclusive results. Most studies analysing the geographical variation in biotic specialization focus, however, only on the local scale. Little is known about how the geographical variation of network structure depends on the spatial scale of observation (i.e. from local to regional spatial scales). This should be remedied, as network structure changes as the spatial scale of observation changes, and the magnitude and shape of these changes can elucidate the mechanisms behind the geographical variation in biotic specialization. Here we analyse four facets of biotic specialization in host–parasitoid networks along gradients of climatic constancy, classifying the networks according to their spatial extension (local or regional). Namely, we analyse network connectance, consumer diet overlap, consumer diet breadth, and resource vulnerability at both local and regional scales along the gradients of both current climatic constancy and historical climatic change. While at the regional scale none of the climatic variables are associated to biotic specialization, at the local scale, network connectance, consumer diet overlap, and resource vulnerability decrease with current climatic constancy, whereas consumer generalism increases (i.e. broader diet breadths in tropical areas). Similar patterns are observed along the gradient of historical climatic change. We provide an explanation based on different beta‐diversity for consumers and resources across the geographical gradients. Our results show that the geographical gradient of biotic specialization is not universal. It depends on both the facet of biotic specialization and the spatial scale of observation.     

Correlation Networks from Flows. The Case of Forced and Time-Dependent Advection-Diffusion Dynamics

Complex network theory provides an elegant and powerful framework to statistically investigate different types of systems such as society, brain or the structure of local and long-range dynamical interrelationships in the climate system. Network links in climate networks typically imply information, mass or energy exchange. However, the specific connection between oceanic or atmospheric flows and the climate network’s structure is still unclear. We propose a theoretical approach for verifying relations between the correlation matrix and the climate network measures, generalizing previous studies and overcoming the restriction to stationary flows. Our methods are developed for correlations of a scalar quantity (temperature, for example) which satisfies an advection-diffusion dynamics in the presence of forcing and dissipation. Our approach reveals that correlation networks are not sensitive to steady sources and sinks and the profound impact of the signal decay rate on the network topology. We illustrate our results with calculations of degree and clustering for a meandering flow resembling a geophysical ocean jet.     

Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study

The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD). However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI) of carefully selected moderate AD patients and normal controls (NCs). Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients.     

Local network regulation of orexin neurons in the lateral hypothalamus

Obesity and inadequate sleep are among the most common causes of health problems in modern society. Thus, the discovery that orexin (hypocretin) neurons play a pivotal role in sleep/wake regulation, energy balance, and consummatory behaviors has sparked immense interest in understanding the regulatory mechanisms of these neurons. The local network consisting of neurons and astrocytes within the lateral hypothalamus and perifornical area (LH/PFA), where orexin neurons reside, shapes the output of orexin neurons and the LH/PFA. Orexin neurons not only send projections to remote brain areas but also contribute to the local network where they release multiple neurotransmitters to modulate its activity. These neurotransmitters have opposing actions, whose balance is determined by the amount released and postsynaptic receptor desensitization. Modulation and negative feedback regulation of excitatory glutamatergic inputs as well as release of astrocyte-derived factors, such as lactate and ATP, can also affect the excitability of orexin neurons. Furthermore, distinct populations of LH/PFA neurons express neurotransmitters with known electrophysiological actions on orexin neurons, such as melanin-concentrating hormone, corticotropin-releasing factor, thyrotropin-releasing hormone, neurotensin, and GABA. These LH/PFA-specific mechanisms may be important for fine tuning the firing activity of orexin neurons to maintain optimal levels of prolonged output to sustain wakefulness and stimulate consummatory behaviors. Building on these exciting findings should shed further light onto the cellular mechanisms of energy balance and sleep-wake regulation.     

Optimal competitive hopfield network with stochastic dynamics for maximum cut problem

In this paper, introducing stochastic dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases which helps the OCHOM escape from local minima. The goal of the maximum cut problem, which is an NP-complete problem, is to partition the node set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The problem has many important applications including the design of VLSI circuits and design of communication networks. Recently, Galán-Marín et al. proposed the OCHOM, which can guarantee convergence to a global/local minimum of the energy function, and performs better than the other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic dynamics which helps the OCHOM escape from local minima, and it is applied to the maximum cut problem. A number of instances have been simulated to verify the proposed algorithm.     

A numerical model of cellular blebbing: A volume-conserving,fluid–structure interaction model of the entire cell

In animal cells, blebs are smooth, quasi-hemispherical protrusions of the plasma membrane that form when a section of the membrane detaches from the underlying actin cytoskeleton and is inflated by flowing cytosol. The mechanics behind this common cellular activity are not yet clear. As a first step in the development of a full computational framework, we present a numerical model of overall cell behavior based upon the interaction between a background Newtonian-fluid cytosol and elastic structures modeling the membrane and filaments. The detailed micromechanics of the cytoskeletal network are the subject of future work. Here, the myosin-driven contraction of the actin network is modeled through stressed elastic filaments. Quantitative models of cytoskeletal micromechanics and biochemistry require accurate estimates of local stress and flow conditions. The main contribution of this paper is the development of a computationally efficient fluid–structure interaction model based on operator splitting, to furnish this data. Cytosol volume conservation (as supported by experimental evidence) is enforced through an intermediate energy minimization step. Realistic bleb formation and retraction is observed from this model, offering an alternative formulation to positing complex continuum behavior of the cytoplasm (e.g. poroelastic model of Charras et al., 2008).     

Effects of area,environmental status and environmental variation on species richness per unit area in Mediterranean wetlands

Abstract. We propose a mechanistic model to relate α- and γ-diversity to area per se, moisture status and environmental variation (local and total), and explored the effects these abiotic variables have on species richness per unit area (α-diversity) for plant communities in a network of wetland habitats located in a Mediterranean mountainous region of central Spain. In this study, environmental status is measured as actual evapotranspiration (as an expression of energy), slope and soil wetness, and environmental variation refers to slope variation and soil wetness variation. Species richness per unit area was related to soil wetness, soil wetness variation, ground slope and ground slope variation. There were also positive correlations among moisture status and environmental variation variables. There is a joint effect of slope and soil wetness variation in explaining species richness per unit area of these wetland habitats, but area effects and energy are relatively unimportant. We conclude that species richness per unit area of wetland vegetation can be explained by moisture status and local environmental variation, and that habitat area may not have an important effect. Area could affect γ-diversity directly through random sampling and/or indirectly through increasing β-diversity, and energy may be important in areas with larger energy ranges. Complete surveys of environmental status, local and total environmental variation, and their associated species assemblages are needed to explain the processes that give rise to the rule that larger areas have larger species richness.     

Changes in Brain Network Efficiency and Working Memory Performance in Aging

Working memory is a complex psychological construct referring to the temporary storage and active processing of information. We used functional connectivity brain network metrics quantifying local and global efficiency of information transfer for predicting individual variability in working memory performance on an n-back task in both young (n = 14) and older (n = 15) adults. Individual differences in both local and global efficiency during the working memory task were significant predictors of working memory performance in addition to age (and an interaction between age and global efficiency). Decreases in local efficiency during the working memory task were associated with better working memory performance in both age cohorts. In contrast, increases in global efficiency were associated with much better working performance for young participants; however, increases in global efficiency were associated with a slight decrease in working memory performance for older participants. Individual differences in local and global efficiency during resting-state sessions were not significant predictors of working memory performance. Significant group whole-brain functional network decreases in local efficiency also were observed during the working memory task compared to rest, whereas no significant differences were observed in network global efficiency. These results are discussed in relation to recently developed models of age-related differences in working memory.     

CONTROL: software for the analysis of the control of metabolic networks

The program CONTROL is based on metabolic control theory anduses the method developed by Reder (1988). In this theory, twosets of parameters are defined in the vicinity of a steady-state:the elasticity coefficients which describe the local behaviourof the isolated enzymes, and the control coefficients whichexpress the response of the whole metabolic network to perturbationsat a given step. The theory shows that relationships exist betweenthe control coefficients (summation relationships or structuralrelationships) and also between the two types of coefficients(control and elasticity coefficients: connectivity relationships).The program CONTROL is divided into two parts (sub-menus). Thefirst one calculates all the control coefficients (flux andconcentrations) of a metabolic network from the elasticity coefficients.Using the second menu, the symbolic relationships are obtainedbetween the control coefficients (summation relationships) andbetween the control coefficients and the elasticity coefficients(connectivity relationships). These two sub-menus can be appliedindependently to any metabolic network (to date limited to 19steps and 19 metabolites).