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Mercury (Hg2+) and silver (Ag+) ions possess the harmful effects on public health and environment that makes it essential to develop the sensing techniques with great sensitivity for the ions. Metal ions commonly coexist in the different biological and environmental systems. Hence, it is an urgent demand to design a simple method for the simultaneous detection of metal ions, peculiarly in the case of coexisting Hg2+ and Ag+. This study introduces a low-cost paper-based aptasensor to monitor Hg2+ and Ag+, simultaneously. The strategy of the sensing array is according to the conformational changes of Hg2+- and Ag+-specific aptamers and their release from the GO surface after the injection of the target sample on the sensing platform. Through monitoring the fluorescence recovery changes against the concentrations of the ions, Hg2+ and Ag+ can be determined as low as 1.33 and 1.01 pM. The paper-based aptasensor can simultaneously detect the ions within about 10 min. The aptasensor is applied prosperously to monitor Hg2+ and Ag+ in human serum, water, and milk. The designed aptasensor with the main advantages of simplicity and feasibility holds the supreme potential to develop a cost-effective sensing method for environmental monitoring, food control, and human diagnostics. Image 1 • An aptasensor was developed for the simultaneous detection of Hg2+ and Ag+. • Hg2+ and Ag+ were detected with the LODs of 1.33 and 1.01 pM, respectively. • The target detection was done within about 10 min. • The simple aptasensor was applied in the real samples. [ABSTRACT FROM AUTHOR]

A fluorescent nanoscale titanium-based metal-organic framework materials (MOF), Ti 6 (μ 3 -O) 6 (μ 2 -OH) 6 (BTCA) 2 (DMF) 2 (ZSTU-3) was synthesized, with blue fluorescence and delicate conjugated structures indicated its potential as a fluorescence sensor. In this work, we reported a rare example of Ti-MOF for highly sensitive and selective for fluorescence sensing of Fe3+ ion and nitro-aromatic compounds. The limit of detection (LOD) for Fe3+ ion reaches 70.7 ​nM and the K sv value is 2.36 ​× ​105 ​M−1, which is among one of the highest LOD values reported by MOFs as far as we known. For the detection of nitro-aromatic compounds, especially 2,4,6-trinitrophenol (PA), the LOD is calculated to be 15.90 ​μM, and the K sv value is 4.253 ​× ​104 ​M−1. In addition, we also performed a rapid detection of Fe3+ ion and nitro-aromatic compounds with fluorescent test paper by naked-eyes. The results demonstrate the superiority of Ti-MOF for detecting Fe3+ ion and nitro-aromatic compounds, which may facilitate the detection application in environmental and biological systems. A nanoscale titanium-based MOF was synthesized and exhibited highly sensitive sensing properties towards Fe3+ ion and nitro-aromatic molecules. Image 1 • A rare example of nanoscale luminescent titanium metal-organic framework ZSTU-3 nanoscale material realized the nanomolar detection of Fe3+ with the detection limit of only 70.1 ​nM. •The ZSTU-3 material is a rare example of Ti-MOFs for highly efficient sensing of nitroaromatic explosives, and realized excellent sensing properties of 2,4,6-trinitrophenol. •The detection of Fe3+ and nitroaromatic explosives was carried out in fluorescent test paper which can be used for rapid detection of these substances by nake eyes. [ABSTRACT FROM AUTHOR]

Fe3+ is the fourth most important transition metal ion in biological systems. Also, nitro-aromatics is a class of compounds which is highly explosive and toxic. Therefore, selective detection of Fe3+ and nitro-aromatics is of great significance for human health and environmental protection. In this work, we reported a rare example of Ti-MOF material (ZSTU-1) for highly sensitive and selective luminescent sensing of nitroaromatic explosives and nanomolar level of detection of Fe3+. To the best of our konwledge, ZSTU-1 is the first report of Ti-MOFs for highly efficient sensing of nitroaromatic explosives, and realized excellent sensing properties for PA (2,4,6-trinitrophenol) in both liquid and vapor phase. In liquid, the detection limit for PA can reach 12.3 μM (2.8 ppm) and the Ksv is calculated to be 3.586 × 104 M−1. The most important is that there have been very few MOF-based sensors reach the nanomolar level of detection Fe3+ with the detection limit calculated to be 63.8 nM (approximately 3.6 ppb) and the Ksv value of ZSTU-1 nanorod is 2.69 × 106 M−1. In addition, the detection of Fe3+ and nitroaromatic explosives was carried out in fluorescent test paper which could be used for rapid detection of these substances by naked eyes. These results demonstrate the advantages of titanium-based MOFs for the detection of Fe3+ and nitroaromatic explosives, and can contribute to develop a high performance and visible to the naked eyes fluorescent sensor for detection of specific substances that are vital in biological systems. Nanoscale titanium metal-organic framework materials realized excellent sensing properties for 2,4,6-trinitrophenol in liquid and vapor phase. Image 1 • The ZSTU-1 nanorod material exhibits a multiresponsive fluorescence sensing behavior. • It exhibited the one of the best nanomolar sensing performances for Fe3+. • It is the first report of Ti-MOFs realized excellent sensing properties of nitroaromatic explosives. • The detection of Fe3+ and nitroaromatic explosives was carried out in fluorescent test paper. [ABSTRACT FROM AUTHOR]

In this paper we study the regularity of weak solutions to an elliptic-parabolic system modeling natural network formation. The system is singular and involves cubic nonlinearity. Our investigation reveals that weak solutions are Hölder continuous when the space dimension N is 2. This is achieved via an inequality associated with the Stummel-Kato class of functions and refinement of a lemma originally due to S. Campanato and C.B. Morrey ([5] , p. 86). [ABSTRACT FROM AUTHOR]

The extensive application of amorphous silica nanoparticles (aSiNPs) in recent years has resulted in unavoidable human exposure in daily life, thus raising widespread concerns regarding the safety of aSiNPs on human health. The particle size is one of the important characteristics of nanomaterials that could influence their toxicity. For the reason that particles with smaller sizes possess larger surface area, which may lead to higher surface activity and biological reactivity. However, due to the complexity of experimental conditions and biological systems, the relationship between the particle size and the toxic effect of aSiNPs remains unclear. Therefore, this systematic review aims to investigate how particle size influences the toxic effect of aSiNPs in vivo and to analyze the relevant experimental factors affecting the size-dependent toxicity of aSiNPs in vivo. We found that 83.8% of 35 papers included in the present review came to the conclusion that smaller-sized aSiNPs exhibited stronger toxicity, though a few papers (6 papers) put forward different opinions. The reasons for smaller aSiNPs manifested greater toxicity were summarized. In addition, certain important experimental factors could influence the size-dependent effects and in vivo toxicity of aSiNPs, such as the synthesis method of aSiNPs, disperse medium of aSiNPs, administration route of aSiNPs, species or strain of experimental animals, sex of experimental animals, aggregation/agglomeration and protein corona of aSiNPs. [Display omitted] • The article size is an important factor which impacts the in vivo toxicity of aSiNPs. • 83.8% included papers showed that smaller-sized aSiNPs exhibited stronger toxicity. • Reasons for smaller aSiNPs did or did not exhibit stronger toxicity were explored. • Some experimental factors would influence the size-dependent toxicity of aSiNPs. [ABSTRACT FROM AUTHOR]

Pulsed field gradient (PFG) NMR is a noninvasive tool to study anomalous diffusion, which exists widely in many systems such as in polymer or biological systems, in porous material, in single file structures and in fractal geometries. In a real system, the diffusion could be a restricted or a tortuous anomalous diffusion, rather than a free diffusion as the domains for fast and slow transport could coexist. Though there are signal attenuation expressions for free anomalous diffusion in literature, the signal attenuation formalisms for restricted anomalous diffusion is very limited, except for a restricted time-fractional diffusion within a plate reported recently. To better understand the PFG restricted fractional diffusion, in this paper, the PFG signal attenuation expressions were derived for three typical structures (plate, sphere, and cylinder) based on two models: fractal derivative model and fractional derivative model. These signal attenuation expressions include two parts, the time part T n ( t ) and the space part X n ( r ). Unlike normal diffusion, the time part T n ( t ) in time-fractional diffusion can be either a Mittag-Leffler function from the fractional derivative model or a stretched exponential function from the fractal derivative model. However, provided the restricted normal diffusion and the restricted time-fractional diffusion are in an identical structure, they will have the same space part X n ( r ) as both diffusions have the same space derivative parameter β equaling 2, therefore, they should have similar diffractive patterns. The restricted general fractional diffusion within a plate is also investigated, which indicates that at a long time limit, the diffusion type is insignificant to the diffractive pattern that depends only on the structure and the gradient pulses. The expressions describing the time-dependent behaviors of apparent diffusion coefficient D f , app for restricted anomalous diffusion are also proposed in this paper. Both the short and long time-dependent behaviors of D f , app are distinct from that of normal diffusion. The general expressions for PFG restricted curvilinear diffusion of tube model were derived in a conventional way and its result agree with that obtained from the fractional derivative model with α equaling 1/2. Additionally, continuous-time random walk simulation was performed to give good support to the theoretical results. These theoretical results reported here will be valuable for researchers in analyzing PFG anomalous diffusion. [ABSTRACT FROM AUTHOR]

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1–11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. 13C-metabolic flux analysis (13C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice. Image 1 • Metabolic fluxes provide quantitative insights into flow of carbon, energy and electrons. • Flux balance analysis (FBA) evaluates capabilities of metabolic network models. • 13C-Metabolic flux analysis (13C-MFA) estimates fluxes with high precision. • New methods for 13C-MFA can measure fluxes in complex, non-standard biological systems. • Predictive kinetic models of metabolism are constructed from large-scale 13C-MFA data sets. [ABSTRACT FROM AUTHOR]

Arsenic (As) is a toxic metalloid exist ubiquitously in environment. Epidemiological studies and laboratory animal studies have verified that As damages multiple organs or tissues in the body and is associated with a variety of diseases. Changes in metabolites usually indicate disturbances in metabolic pathways and specific metabolites are considered as biomarkers of diseases or drugs/toxins or environmental effects. Metabolomics is the quantitative measurement of the dynamic multi-parameter metabolic responses of biological systems due to pathophysiological or genetic changes. Current years, some metabolomic studies on the hazardous effect of environmental As on humans have been reported. In this paper, we first overviewed the metabolomics studies of environmental As exposure in humans since 2011, emphasizing on the data mining process of metabolic characteristics related to the hazardous effects of environmental As on humans. Then, the relationship between metabolic characteristics and the toxic mechanism of environmental As exposure in humans were discussed, and finally, the prospects of metabolomics studies on populations exposed to environmental As were put forward. Our paper may shed light on the study of mechanisms, prevention and individualized treatment of As poisoning. [Display omitted] • Metabolomics was used in study of the hazardous effect of environmental As on humans. • The data mining of metabolic characteristics adopts many statistical strategies. • Methionine and one carbon metabolism are associated with As biotransformation. • Metabolic changes are related to toxic mechanisms, such as oxidative stress, apoptosis, etc. • There are limitations and perspectives in experimental design, samples, methodology and technology. [ABSTRACT FROM AUTHOR]

Abstract Reactive nitrogen (Nr) is both a principal factor regulating growth in biological systems and a significant pollutant for the environment. The objective of this paper is to reflect on the challenges of the nutrient management related to and priorities needed to clarify how Nr is employed appropriately whilst striving to mitigate emissions at the same time. To create orientation, valid data are needed reflecting the real-farming emissions (RFE) from individual farms to assess what is needed to balance partly contradicting goals, and to bridge the gap between current and envisaged levels. Furthermore, knowledge is required of which tools and measures are at the farmer's disposal to mitigate Nr emissions. Finally and of utmost importance, the farm management is in need of action knowledge, i.e. knowing how to select the most appropriate and cost-effective tools and measures for the specific conditions on each farm, while taking the possible impacts of their implementation on the balance between Nr-related productivity and threats into account. Dealing with such a complex issue requires a systemic approach, considering the farm system not only as a one-compartment model (1st scale) with quantifiable Nr inputs and outputs at the farm gate but as divisible into sub-systems (2nd scale). The Nr-flow through the sub-systems: feed, livestock, manure and utilised land area, represents an inner-farm Nr-cycle. Each sub-system can be further sub-divided into sub-sub-systems (3rd scale) such as feeding groups within livestock, which could be differentiated in individual animals (4th scale). This approach enables to determine where (and to what extent) nutrients may be allocated more effectively and more cost-efficiently. The allocation of Nr resources between sub-units within the respective sub-systems determines the efficiency in the use of Nr and thus the proportion of Nr contributing either to an increase in productivity or to Nr-related environmental pollution. Quantifying the N-flows through sub-systems of a farm on the 2nd scale is the starting point for benchmarking; providing orientation for the regulation of processes both inside and outside the farm system. It creates target figures for the farm management while identifying the gap between the current ranking level of the farm and its potential rank. Improving the recycling of Nr throughout the whole farm system and increasing the efficiency in the use of Nr on the 3rd and 4th scale are seen as major opportunities for the farm management to balance the trade-offs without comprising productivity. It is concluded that the lack of benchmark RFE values in relation to the amount of food and feed produced can be seen as one of the main barriers in the fight to mitigate environmental Nr emissions from agricultural processes. If benchmarking has not been established, the farm management lacks orientation regarding the target figures it should aim at. Without target figures it is not possible to formulate concise working hypotheses regarding the most effective and cost-efficient use of means that are to the farmer's disposal as well as strategies for an improved allocation of resources in a farm specific context. Highlights • Use of Nr is facing trade-offs which have to be balanced within the farm systems. • Understanding Nr losses and solving possible conflicts require a context-dependent approach. • Quantification of Nr output/input ratios of sub-systems is essential for the nutrient management. • Appropriate allocation of Nr resources require clear target figures based on benchmarking. • Mitigation of Nr emissions while considering trade-offs is best achieved by increasing efficiency. [ABSTRACT FROM AUTHOR]

Highlights • The nonlinear damping of the limb-like (LLS) or X-shaped structure is studied. • The damping is a nonlinear function of vibration displacement and varies at different frequencies. • The damping is high at resonance (large-displacement) but low at other frequencies. • The structural parameters can be tuned to achieve a desired passive and nonlinear damping. • The study demonstrates an innovative and passive solution for desired nonlinear damping design in engineering. Abstract Natural biological systems can always demonstrate amazing properties or performance subject to external excitation. The nonlinear damping characteristics of a passive bio-inspired limb-like structure (LLS) or X-shaped structure is studied in this paper with theoretical modelling and experimental validation. It is revealed that, (1) the linear horizontally-placed damper and joint friction can both produce equivalent nonlinear damping in the vertical direction; (2) the equivalent damping characteristic is shown to be a nonlinear function of the vibration displacement and varies at different frequency; (3) the equivalent damping can be beneficially varying with vibration displacement and frequencies, i.e., automatically high at around resonance frequency (for large-displacement vibration) but low at other frequencies (for low-displacement vibration), showing an ideal passive and nonlinear damping property; (4) the structural rod length, asymmetric rod length ratio, layer number, and assembly angle can all affect the equivalent nonlinear damping and thus can be used to tune the damping characteristic to the ideal case conveniently in practice. The results of this study demonstrate an innovative and passive solution for designing desired nonlinear damping characteristics in various engineering practices by employing the LLS. [ABSTRACT FROM AUTHOR]

Abstract: A great many computational algorithms developed over the past half-century have been motivated or suggested by biological systems or processes, the most well-known being the artificial neural networks. These algorithms are commonly grouped together under the terms soft or natural computing. A property shared by most natural computing algorithms is that they allow exploration of, or learning from, data. This property has proved extremely valuable in the solution of many diverse problems in science and engineering. The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research. The application of some of the main algorithms is illustrated using case studies. The paper also attempts to give some indication as to which of the algorithms emerging now from the machine learning community are likely to be important for mechanical systems research in the future. [Copyright &y& Elsevier]

Energy efficiency in irrigation distribution networks can be estimated by following the method proposed by Abadia et al (2008) [Biosystems Engineering, 101, 21–27], in which energy efficiency is quantified in terms of global energy efficiency (GEE), a parameter that is a compound of the pumping energy efficiency (PEE) and the supply energy efficiency (SEE). PEE is related to pump functioning, while SEE is related to the design and management of the distribution system. Calculation of SEE by this method is limited to irrigation networks that have a single water intake at an elevation below that of the irrigated area and a single supplied sector. Results from the method are confusing for networks with more than one water supply point or irrigation sectors supplied by intakes located at elevations higher than that of the irrigated area. This paper proposes a modification of the earlier method that is appropriate to any type of layout and configuration of the irrigation network, with one or several water intake points and supplied sectors. The new model is applied to four different types of collective pressurised irrigation network, and the results are compared with those of the previous model. The influence of different measures taken to improve the SEE and determine the variables that affect this efficiency was analysed. The results show that adequate SEE values can be obtained with the proposed model for all the situations analysed, and that it is possible to quantify the improvements in SEE with the model. Highlights • Knowing the energy efficiency of a network can improve design and energy management. • Previous Supply Energy Efficiency (SEE) definition unfit for some network designs. • New method proposed and applied to four distribution networks. • New method is coherent and appropriate for all networks designs. • New SEE definition allows for identification of badly designed or managed networks. [ABSTRACT FROM AUTHOR]

A new type of sprayer developed for orchard is being adapted for vine spraying. The technology uses vertical air sleeves, generating a two-dimensional horizontal air jet, to distribute spray droplets within the canopy. Three different configurations were tested to determine the combination which provides the most appropriate spray distribution within the canopy along with the highest coverage on artificial cylindrical targets. One configuration (a) used a single air jet perpendicular to the row. Two other configurations used a pair of sleeves, one sleeve on each side of the row. In configuration (b), airflow rate was distributed evenly between the two sleeves. In configuration (c), the distribution of air was varied along with the angle of attack, providing an interaction between the two airflows. Coverage on 22mm cylindrical targets covered with water-sensitive papers was evaluated across the vine rows. These papers were processed using image analysis. Results showed that coverage was maximised using configuration (c). The use of a pair of sleeves, in configurations (b) and (c), provided a more uniform distribution of droplets locally. [Copyright &y& Elsevier]

Solving the chemical master equation is an indispensable tool in understanding the behavior of biological and chemical systems. In particular, it is increasingly recognized that commonly used ODE models are not able to capture the stochastic nature of many cellular processes. Solving the chemical master equation directly, however, suffers from the curse of dimensionality. That is, both memory and computational effort scale exponentially in the number of species. In this paper we propose a dynamical low-rank approach that enables the simulation of large biological networks. The approach is guided by partitioning the network into biological relevant subsets and thus avoids the use of single species basis functions that are known to give inaccurate results for biological systems. We use the proposed method to gain insight into the nature of asynchronous vs. synchronous updating in Boolean models and successfully simulate a 41 species apoptosis model on a standard desktop workstation. • First dynamical low-rank approximation (DLRA) for the Boolean chemical master equation. • Drastic reduction in both memory and computational cost. • Proposal of an alternative to single orbital basis functions based on the structure of the problem. [ABSTRACT FROM AUTHOR]

In this paper, spreading properties of a competition-diffusion system of two equations are studied. This system models the invasion of an empty favorable habitat, by two competing species, each obeying a logistic growth equation, such that any coexistence state is unstable. If the two species are initially absent from the right half-line x > 0 , and the slowest one dominates the fastest one on x < 0 , then the latter will invade the right space at its Fisher-KPP speed, and will be replaced by or will invade the former, depending on the parameters, at a slower speed. Thus, the system forms a propagating terrace, linking an unstable state to two consecutive stable states. [ABSTRACT FROM AUTHOR]

The Patlak-Keller-Segel-Navier-Stokes system describes the biological chemotaxis phenomenon in the fluid environment. It is a coupled nonlinear system with unknowns being the cell density, the concentration of chemoattractants, the fluid velocity and the pressure, and it satisfies an energy dissipation law, preserves the bound/positivity and mass of the cell density. We develop in this paper a class of scalar auxiliary variable (SAV) schemes with relaxation which preserve these properties unconditionally at the discrete level, and only require solving decoupled linear systems with constant coefficients at each time step. We present ample numerical results to validate these schemes, simulate the chemotactic non-aggregation and aggregation with a saturation concentration, as well as investigate the blow-up phenomenon. [ABSTRACT FROM AUTHOR]

This paper is devoted to studying obstacle avoiding patterns and cohesiveness of fish school. First, we introduce a model of stochastic differential equations (SDEs) for describing the process of fish school's obstacle avoidance. Second, on the basis of the model we find obstacle avoiding patterns. Our observations show that there are clear four obstacle avoiding patterns, namely, Rebound, Pullback, Pass and Reunion, and Separation. Furthermore, the emerging patterns change when parameters change. Finally, we present a scientific definition for fish school's cohesiveness that will be an internal property characterizing the strength of fish schooling. There are then evidences that the school cohesiveness can be measured through obstacle avoiding patterns. [ABSTRACT FROM AUTHOR]

Metabolic reaction data is commonly modelled using a complex network approach, whereby nodes represent the chemical species present within the organism of interest, and connections are formed between those nodes participating in the same chemical reaction. Unfortunately, such an approach provides an inadequate description of the metabolic process in general, as a typical chemical reaction will involve more than two nodes, thus risking oversimplification of the system of interest in a potentially significant way. In this paper, we employ a complex hypernetwork formalism to investigate the robustness of bacterial metabolic hypernetworks by extending the concept of a percolation process to hypernetworks. Importantly, this provides a novel method for determining the robustness of these systems and thus for quantifying their resilience to random attacks/errors. Moreover, we performed a site percolation analysis on a large cohort of bacterial metabolic networks and found that hypernetworks that evolved in more variable environments displayed increased levels of robustness and topological complexity. [ABSTRACT FROM AUTHOR]

• Statistical methods are appropriate for modelling fine-tuning. • Fine-tuning is detected in functional proteins, cellular networks etc. • Constants and initial conditions of nature are deliberately tuned. • Statistical analysis of fine-tuning model some of the categories of design. • Fine-tuning and design deserve attention in the scientific community. Fine-tuning has received much attention in physics, and it states that the fundamental constants of physics are finely tuned to precise values for a rich chemistry and life permittance. It has not yet been applied in a broad manner to molecular biology. However, in this paper we argue that biological systems present fine-tuning at different levels, e.g. functional proteins, complex biochemical machines in living cells, and cellular networks. This paper describes molecular fine-tuning, how it can be used in biology, and how it challenges conventional Darwinian thinking. We also discuss the statistical methods underpinning fine-tuning and present a framework for such analysis. [ABSTRACT FROM AUTHOR]

• PCF-based sensors have been investigated for biomedical applications. • Various novel PCF-based structures have been also investigated. • Various novel nanomaterials used with PCF have been tested. • Several diseases diagnosis can be also perform with PCF. • Sensing performance of PCF-based sensors are high. Nowadays, the usage of photonic crystal fibers (PCF) has become a very quick and prevalent innovation in sensor implementation. PCFs have unique features and capabilities due to their geometric design, which makes them ideal choice for sensing applications. In this study, various simulations of biomedical sensors are analyzed. A biosensor is a type of an analytical device that analyses a biological system using either chemical or biological components. By filling the solutions into PCF shows good results in diagnosing disease, malignancies, hemoglobin concentrations and antibodies. PCF type biosensors are widely used in biological research, clinical trials, medication development and other applications. This research proposes a survey for different shapes of PCFs in biomedical sensors. This research paper main objective is to describe the numerous applications of biomedical sensors like glucose, blood, protein, and DNA sensors. The sensitivity and confinement loss are compared using this different-shaped PCF with different background materials. [ABSTRACT FROM AUTHOR]

In this paper, the effects of wheel slip estimation and compensation of trajectory tracking for orchard applications were investigated. A slippage estimator was developed and adapted into a car-like robot model. Steering and velocity commands were generated using a model-based control approach. The whole system was implemented and tested on an autonomous orchard vehicle that has steerable front wheels and actuated rear wheels. A high accuracy positioning system was used to estimate the longitudinal and lateral slip velocities while the vehicle is moving. A laser scanning range finder was placed at the front centre of the vehicle, which was used to detect rows of trees in the orchard. Procedures were first tested in a non-flat but open space, which was covered with snow. Then it was tested on an experimental orchard where the surface was covered with heavy mud and the vehicle was expected to follow trajectories that span multiple rows in the orchard. The vehicle detected individual trees as well as rows of trees to track the centre of each row and manoeuvred from one row to the next. The experimental results showed that trajectory tracking performance of the vehicle was enhanced via integrating a slippage estimator into the system model. Furthermore, using the slippage estimation in the system model increased the accuracy, repeatability and performance of the control system. [ABSTRACT FROM AUTHOR]

“A Mathematical Theory of Communication”, was published in 1948 by Claude Shannon to establish a framework that is now known as information theory. In recent decades, information theory has gained much attention in the area of systems biology. The aim of this paper is to provide a systematic review of those contributions that have applied information theory in inferring or understanding of biological systems. Based on the type of system components and the interactions between them, we classify the biological systems into 4 main classes: gene regulatory, metabolic, protein–protein interaction and signaling networks. In the first part of this review, we attempt to introduce most of the existing studies on two types of biological networks, including gene regulatory and metabolic networks, which are founded on the concepts of information theory. [ABSTRACT FROM AUTHOR]

Cannabis sativa is a recreational drug commonly consumed in Europe and is getting popularity for both recreational and therapeutic use. In some individuals, the use of cannabis leads to psychotic disorders. This systematic review summarizes the current evidence linking genetic polymorphisms and inter-individual susceptibility to psychosis induced by cannabis. Studies published from 2005 to 2020 were identified through Medline using PubMed, Web of Science and Scopus database and searches were conducted according to PRISMA guidelines. Initial search was performed with terms: "cannabis induced psychosis" AND "genetics". From the initial group of 108 papers, 18 studies met our inclusion criteria. Many of the findings revealed associations with genetic polymorphisms modulations of genes involved directly (COMT, DRD2 and DAT) or indirectly (AKT1) to dopamine pathways. The most consistent finding was with COMT rs4680, where the presence of the Val allele was associated with a higher risk for cannabis-induced psychosis. This higher susceptibility was also reported for AKT1 (rs2494732) with the CC genotype. Of note, the only genome-wide association study identified a significant signal close to the cholinergic receptor muscarinic 3 represented by rs115455482 and rs74722579 predisposing to cannabis-induced hallucinations and remarkably no dopaminergic target was found. Actual evidence supports the role of dopamine in cannabis induced psychosis. However, most of genetic polymorphism studies have as a starting point the pre-existing dopaminergic theoretical basis for psychosis. This alerts to the importance of more broad genetic studies. Integrate genetic results into biological systems may enhance our knowledge of cannabis induced psychosis and could help in the prevention and treatment of these patients. [Display omitted] • Considering risk for cannabis induced psychosis, most consistent genetic associations described are related with dopaminergic pathways. • COMT and AKT1 are the two most studied genes. • Studies evaluating the genetic influence on cannabis-induced psychosis in healthy individuals are still scarce. • Great heterogeneity in the definition of phenotypes and endophenotypes. • The variety of measuring instruments and experimental design makes it difficult to pull together all studies in a suitable systematic review. [ABSTRACT FROM AUTHOR]

Spin-label electron spin resonance (ESR) spectroscopy is a valuable means to study molecular mobility and interactions in biological systems. This paper deals with conventional, continuous wave ESR of nitroxide spin-labels at 9-GHz providing an introduction to the basic principles of the technique and applications to self-assembled lipid aggregates and proteins. Emphasis is given to segmental lipid chain order and rotational dynamics of lipid structures, environmental polarity of membranes and proteins, structure and conformational dynamics of proteins. [ABSTRACT FROM AUTHOR]

Abstract: Calcium is one of the most important intracellular messengers, which occurs in the cytosol and the endoplasmic reticulum of animal cells. While most calcium dynamics models either do not account properly for the fact that the endoplasmic reticulum constitutes a microstructure of the cell or are infeasible by resolving the fine structure very explicitly, Goel et al. [15] derived an effective macroscopic model by formal homogenization. In this paper, this approach is made rigorous using periodic homogenization techniques to upscale the nonlinear coupled system of reaction–diffusion equations and, moreover, the appropriate scaling of the interfacial exchange term is taken into consideration. [Copyright &y& Elsevier]

A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure. [ABSTRACT FROM AUTHOR]

This research note is in reaction to a recent paper on weed recognition using image analysis (Peng & Jun, 2011). Here, the correct use of moment invariants in a weed recognition system is presented. [ABSTRACT FROM AUTHOR]

Abstract: The assumption of being able to perform infinite precision measurements does not only lead to undecidability, but it also introduces artifacts in the mathematical models that do not correspond to observable behaviours of systems under study. When bounded spatial regions are involved, such issues can be avoided if arbitrarily small sets of points are not definable in the mathematical setting. ϵ-semantics were introduced in this spirit. In this paper we investigate the use of ϵ-semantics deeper, in the context of reachability analysis of hybrid automata. In particular, we focus on two ϵ-semantics and reason about their computability. We then try our approach on biological model analysis to give evidence about the effectiveness of the methodology. [Copyright &y& Elsevier]

Abstract: The structures of many helical protein filaments can be derived from electron micrographs of their suspensions in thin films of vitrified aqueous solutions. The most successful and generally-applicable approach treats short segments of these filaments as independent “single particles”, yielding near-atomic resolution for rigid and well-ordered filaments. The single-particle approach can also accommodate filament deformations, yielding sub-nanometer resolution for more flexible filaments. However, in the case of thin and flexible filaments, such as some amyloid-β (Aβ) fibrils, the single-particle approach may fail because helical segments can be curved or otherwise distorted and their alignment can be inaccurate due to low contrast in the micrographs. We developed new software called Frealix that allows the use of arbitrarily short filament segments during alignment to approximate even high curvatures. All segments in a filament are aligned simultaneously with constraints that ensure that they connect to each other in space to form a continuous helical structure. In this paper, we describe the algorithm and benchmark it against datasets of Aβ(1–40) fibrils and tobacco mosaic virus (TMV), both analyzed in earlier work. In the case of TMV, our algorithm achieves similar results to single-particle analysis. In the case of Aβ(1–40) fibrils, we match the previously-obtained resolution but we are also able to obtain reliable alignments and ∼8-Å reconstructions from curved filaments. Our algorithm also offers a detailed characterization of filament deformations in three dimensions and enables a critical evaluation of the worm-like chain model for biological filaments. [Copyright &y& Elsevier]

• Biological networks have the ability to maintain their performance in the face of internal and external disturbances. • Considering the mathematical model of biological networks, a comprehensive picture of the system's stability is given through the stability sensitivity evaluation of each edge against disturbance. • The most influential edges on a biological network are stochasticlly specified through control-theory basics. • Very small values of standard deviations prove that the stability sensitivity is independent of the magnitude of disturbance. Network modeling is an effective tool for understanding the properties of complex systems. Networks are widely used to help us gain insight into biological systems. In this way, the cell, gene, and protein are denoted as nodes, and the connection elements are regarded as links or edges. In this paper, a novel stochastic strategy is developed for identifying the most influential edges on the stability of biological networks. Regarding the principles of networks and control-theory basics like Jacobian and eigenvalue sensitivity-based analysis, a new criterion is proposed, called "random sensitivity index matrix" (RSIM). RSIM evaluates the eigenvalue sensitivity of all edges in a network in the presents of stochastic disturbances based on the Monte Carlo algorithm. Through the values of RSIM elements, the sensitive edges are identifiable. In addition, the contribution of each edge in network instability has been compared through different percentages of disturbances. Different percentages of disturbances did not change the results. The performance of the proposed method was verified by simulation results for Lac (lactose) operon and MAPK (Mitogen-activated protein kinases) as two sample biological networks. [ABSTRACT FROM AUTHOR]

Abstract: Self-sustaining autocatalytic chemical networks represent a necessary, though not sufficient condition for the emergence of early living systems. These networks have been formalised and investigated within the framework of RAF theory, which has led to a number of insights and results concerning the likelihood of such networks forming. In this paper, we extend this analysis by focussing on how small autocatalytic networks are likely to be when they first emerge. First we show that simulations are unlikely to settle this question, by establishing that the problem of finding a smallest RAF within a catalytic reaction system is NP-hard. However, irreducible RAFs (irrRAFs) can be constructed in polynomial time, and we show it is possible to determine in polynomial time whether a bounded size set of these irrRAFs contain the smallest RAFs within a system. Moreover, we derive rigorous bounds on the sizes of small RAFs and use simulations to sample irrRAFs under the binary polymer model. We then apply mathematical arguments to prove a new result suggested by those simulations: at the transition catalysis level at which RAFs first form in this model, small RAFs are unlikely to be present. We also investigate further the relationship between RAFs and another formal approach to self-sustaining and closed chemical networks, namely chemical organisation theory (COT). [Copyright &y& Elsevier]

Abstract: Control engineering and analysis of biological systems have become increasingly important for systems and synthetic biology. Unfortunately, no widely accepted control framework is currently available for these systems, especially at the cell and molecular levels. This is partially due to the lack of appropriate mathematical models to describe the unique dynamics of biological systems, and the lack of implementation techniques, such as ultra-fast and ultra-small devices and corresponding control algorithms. This paper proposes a control framework for biological systems subject to dynamics that exhibit adaptive behavior under evolutionary pressures. The control framework was formulated based on evolutionary game based modeling, which integrates both the internal dynamics and the population dynamics. In the proposed control framework, the adaptive behavior was characterized as an internal dynamic, and the external environment was regarded as an external control input. The proposed open-interface control framework can be integrated with additional control algorithms for control of biological systems. To demonstrate the effectiveness of the proposed framework, an optimal control strategy was developed and validated for drug delivery using the pathogen Giardia lamblia as a test case. In principle, the proposed control framework can be applied to any biological system exhibiting adaptive behavior under evolutionary pressures. [Copyright &y& Elsevier]

Abstract: The study of protein damage by oxidative processes and its influence on protein activity is central to understanding the deleterious effects of oxidative stress on biological systems. This paper will focus on the study of enzyme inactivation by oxidative modifications, utilizing α-amylase from Bacillus species. (BAA) as a model protein. Oxidative stress was induced using metal catalyzed oxidation (MCO). The enzymatic activity of BAA was correlated with the oxidative damage induced to the protein. Off-line nano-HPLC–MALDI-TOF/TOF-MS was used to characterize the oxidative modifications occurring to the protein. Additionally, semi-quantitative analysis was employed in order to evaluate the significance of the various oxidative modifications. BAA oxidation was found to be deleterious to its enzymatic activity. A total of 10 amino acid residues were found to have an oxidation degree above 50%, out of which eight were methionine and tryptophan. Residues in the proximity of key structural elements were found to be particularly susceptible to oxidation. The oxidative process was found to be governed by the nature of the amino acid residues side chain and, to a lesser extent, their location within the three dimensional structure of the protein. [Copyright &y& Elsevier]

Abstract: The phenomenon of backward bifurcation in disease transmission models, where a stable endemic equilibrium co-exists with a stable disease-free equilibrium when the associated reproduction number is less than unity, has been observed in a number of disease transmission models. The epidemiological consequence of backward bifurcation is that the classical requirement of the reproduction number being less than unity becomes only a necessary, but not sufficient, for disease elimination (hence, the presence of this phenomenon in the transmission dynamics of a disease makes its effective control in the community difficult). This paper addresses the problem of finding the causes of backward bifurcation in some standard deterministic models for the spread of some emerging and re-emerging diseases (it contains a brief review of some common causes, as well as some new causes, of backward bifurcation in some standard disease transmission models). It is shown that, in addition to the usual causes (such as the use of imperfect vaccine and exogenous re-infection in TB disease), a number of other biological or epidemiological mechanisms, such as vaccine-derived immunity waning at a slower rate than natural immunity, disease-induced mortality in vector-borne diseases and differential susceptibility in risk-structured models, could also cause backward bifurcation in disease transmission models. [Copyright &y& Elsevier]

Abstract: In the forthcoming future, various means of wireless communication, such as cellular, Wi-Fi, WiMAX, and DSRC, will be available to mobile users and applications. With the development of wireless communication and mobile devices, more and more users and applications will be accommodated in mobile environment. Since mobile users and applications compete for the limited wireless resources whose communication quality dynamically change, we need an adaptive mechanism for mobile users and applications to share the available network resources while satisfying each applicationʼs QoS requirements. In this paper, we propose an adaptive resource allocation mechanism where each node autonomously determines wireless network resources to assign to each of networked applications running on it. For this purpose, we adopt an attractor composition model, which is based on an autonomous and adaptive behavior of biological systems. Through numerical analysis, we confirmed that our mechanism could adaptively and stably allocate wireless network resources to applications, while considering their QoS requirements and fairly sharing network resources with other nodes. It also is shown that our mechanism superiors to a mechanism where a node determines resource allocation by solving an optimization problem. [Copyright &y& Elsevier]

Abstract: Biological systems are typically modelled by nonlinear differential equations. In an effort to produce high fidelity representations of the underlying phenomena, these models are usually of high dimension and involve multiple temporal and spatial scales. However, this complexity and associated stiffness makes numerical simulation difficult and mathematical analysis impossible. In order to understand the functionality of these systems, these models are usually approximated by lower dimensional descriptions. These can be analysed and simulated more easily, and the reduced description also simplifies the parameter space of the model. This model reduction inevitably introduces error: the accuracy of the conclusions one makes about the system, based on reduced models, depends heavily on the error introduced in the reduction process. In this paper we propose a method to calculate the error associated with a model reduction algorithm, using ideas from dynamical systems. We first define an error system, whose output is the error between observables of the original and reduced systems. We then use convex optimisation techniques in order to find approximations to the error as a function of the initial conditions. In particular, we use the Sum of Squares decomposition of polynomials in order to compute an upper bound on the worst-case error between the original and reduced systems. We give biological examples to illustrate the theory, which leads us to a discussion about how these techniques can be used to model-reduce large, structured models typical of systems biology. [Copyright &y& Elsevier]

Abstract: A fundamental landmark in the emergence and maintenance of the first proto-biological systems must have been the formation of a “closed” metabolic organization, and this paper describes a stochastic analysis of a simple model of a system that is closed to efficient causation. Although it shows an absorbing barrier corresponding to the trivial solution that implies collapse and extinction, for certain values of the kinetic parameters it can also show a “coexistence state” in which there are non-null populations of its intermediates, which corresponds approximately to a non-trivial deterministic stable steady state. Depending on the initial conditions, fluctuations can drive the system either to the self-maintaining regime or to extinction, with different probabilities. Different lines of equal probability have been obtained and compared with the deterministic results, and the average time for reaching these states (characteristic time) has been estimated. The system shows strong dependence on volume size, and there is a critical volume below which it collapses very rapidly. The characteristic time is also affected by the volume, with faster responses for lower system volumes. All these results are discussed in the context of the origin of living organization. [Copyright &y& Elsevier]

Abstract: Metabolomics refer to the global analysis of small molecule metabolites in a biological system, and can be a powerful tool to elucidate and optimize cellular processes, particularly when integrated into a systems biology framework. Determining the endometabolome in cultured animal cells is especially challenging, due to the conflicting demands for rapid quenching of metabolism and retention of membrane integrity, while cells are separated from the complex medium. The challenge is magnified in virus infected cells due to increased membrane fragility. This paper describes an effective methodology for quantitative intracellular metabolite analysis of the baculovirus–insect cell expression system, an important platform for the production of heterologous proteins and baculovirus-based biopesticides. These two applications were represented by Spodoptera frugiperda (Sf9) and Helicoverpa zea (HzAM1) cells infected with recombinant Autographa californica and wild-type Helicoverpa armigera nucleopolyhedroviruses (AcMNPV and HaSNPV), respectively. Specifically, an ice-cold quenching solution comprising 1.1% w/v NaCl and 0.2% w/v Pluronic® F-68 (NaCl+P) was found to be efficacious in preserving cell viability and minimizing cell leakage during quenching and centrifugation-based washing procedures (prior to extraction using cold 50% v/v acetonitrile). Good recoveries of intracellular adenosine triphosphate, total adenosine phosphates and amino acids were obtained after just one wash step, for both uninfected and infected insect cells. The ability to implement wash steps is critical, as insect cell media are metabolites-rich, while infected insect cells are much more fragile than their uninfected counterparts. Hence, a promising methodology has been developed to facilitate endometabolomic analysis of insect cell–baculovirus systems for bioprocess optimization. [Copyright &y& Elsevier]

Abstract: Protein–protein interactions (PPIs) play an important role in biological processes. Although much effort has been devoted to the identification of novel PPIs by integrating experimental biological knowledge, there are still many difficulties because of lacking enough protein structural and functional information. It is highly desired to develop methods based only on amino acid sequences for predicting PPIs. However, sequence-based predictors are often struggling with the high-dimensionality causing over-fitting and high computational complexity problems, as well as the redundancy of sequential feature vectors. In this paper, a novel computational approach based on compressed sensing theory is proposed to predict yeast Saccharomyces cerevisiae PPIs from primary sequence and has achieved promising results. The key advantage of the proposed compressed sensing algorithm is that it can compress the original high-dimensional protein sequential feature vector into a much lower but more condensed space taking the sparsity property of the original signal into account. What makes compressed sensing much more attractive in protein sequence analysis is its compressed signal can be reconstructed from far fewer measurements than what is usually considered necessary in traditional Nyquist sampling theory. Experimental results demonstrate that proposed compressed sensing method is powerful for analyzing noisy biological data and reducing redundancy in feature vectors. The proposed method represents a new strategy of dealing with high-dimensional protein discrete model and has great potentiality to be extended to deal with many other complicated biological systems. [Copyright &y& Elsevier]

Abstract: We present in this paper a new kernel-independent fast multipole method (FMM), named as FKI-FMM, for pairwise particle interactions with translation-invariant kernel functions. FKI-FMM creates, using numerical techniques, sufficiently accurate and compressive representations of a given kernel function over multi-scale interaction regions in the form of a truncated Fourier series. It provides also economic operators for the multipole-to-multipole, multipole-to-local, and local-to-local translations that are typical and essential in the FMM algorithms. The multipole-to-local translation operator, in particular, is readily diagonal and does not dominate in arithmetic operations. FKI-FMM provides an alternative and competitive option, among other kernel-independent FMM algorithms, for an efficient application of the FMM, especially for applications where the kernel function consists of multi-physics and multi-scale components as those arising in recent studies of biological systems. We present the complexity analysis and demonstrate with experimental results the FKI-FMM performance in accuracy and efficiency. [Copyright &y& Elsevier]

Abstract: Individual based models (IBMs) and Agent based models (ABMs) have become widely used tools to understand complex biological systems. However, general methods of parameter inference for IBMs are not available. In this paper we show that it is possible to address this problem with a traditional likelihood-based approach, using an example of an IBM developed to describe the spread of chytridiomycosis in a population of frogs as a case study. We show that if the IBM satisfies certain criteria we can find the likelihood (or posterior) analytically, and use standard computational techniques, such as MCMC, for parameter inference. [Copyright &y& Elsevier]

Abstract: Scientific theories seek to provide simple explanations for significant empirical regularities based on fundamental physical and mechanistic constraints. Biological theories have rarely reached a level of generality and predictive power comparable to physical theories. This discrepancy is explained through a combination of frozen accidents, environmental heterogeneity, and widespread non-linearities observed in adaptive processes. At the same time, model building has proven to be very successful when it comes to explaining and predicting the behavior of particular biological systems. In this respect biology resembles alternative model-rich frameworks, such as economics and engineering. In this paper we explore the prospects for general theories in biology, and suggest that these take inspiration not only from physics, but also from the information sciences. Future theoretical biology is likely to represent a hybrid of parsimonious reasoning and algorithmic or rule-based explanation. An open question is whether these new frameworks will remain transparent to human reason. In this context, we discuss the role of machine learning in the early stages of scientific discovery. We argue that evolutionary history is not only a source of uncertainty, but also provides the basis, through conserved traits, for very general explanations for biological regularities, and the prospect of unified theories of life. [Copyright &y& Elsevier]

Abstract: Existing inference methods for estimating the strength of balancing selection in multi-locus genotypes rely on the assumption that there are no epistatic interactions between loci. Complex systems in which balancing selection is prevalent, such as sets of human immune system genes, are known to contain components that interact epistatically. Therefore, current methods may not produce reliable inference on the strength of selection at these loci. In this paper, we address this problem by presenting statistical methods that can account for epistatic interactions in making inference about balancing selection. A theoretical result due to is used to build a multi-locus Wright-Fisher model of balancing selection, allowing for epistatic interactions among loci. Antagonistic and synergistic types of interactions are examined. The joint posterior distribution of the selection and mutation parameters is sampled by Markov chain Monte Carlo methods, and the plausibility of models is assessed via Bayes factors. As a component of the inference process, an algorithm to generate multi-locus allele frequencies under balancing selection models with epistasis is also presented. Recent evidence on interactions among a set of human immune system genes is introduced as a motivating biological system for the epistatic model, and data on these genes are used to demonstrate the methods. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a simple method to analyze the similarity of biological sequences. By taking the average contents of biological sequences and their information entropies as the variables, the fuzzy method is used to cluster them. From the results of application, it finds that the method is relatively easy and rapid. Unlike other methods such as the graphical representation methods, which is usually very complex to compute some invariants of matric derived from graphical representation, our method pays more attention to the information of biological sequences themselves. Especially with the help of the software (SPSS), it seems to be very convenient. Therefore, it may be used to study the new biological sequences such as their evolution relationship and structures. [ABSTRACT FROM AUTHOR]

The paper presents results of research on an inverse kinematics algorithm that has been used in a functional model of a cucumber-harvesting robot consisting of a redundant P6R manipulator. Within a first generic approach, the inverse kinematics problem was reformulated as a non-linear programming problem and solved with a Genetic Algorithm (GA). Although solutions were easily obtained, the considerable calculation time needed to solve the problem prevented on-line implementation. To circumvent this problem, a second, less generic, approach was developed which consisted of a mixed numerical-analytic solution of the inverse kinematics problem exploiting the particular structure of the P6R manipulator. Using the latter approach, calculation time was considerably reduced. During the early stages of the cucumber-harvesting project, this inverse kinematics algorithm was used off-line to evaluate the ability of the robot to harvest cucumbers using 3D-information obtained from a cucumber crop in a real greenhouse. Thereafter, the algorithm was employed successfully in a functional model of the cucumber harvester to determine if cucumbers were hanging within the reachable workspace of the robot and to determine a collision-free harvest posture to be used for motion control of the manipulator during harvesting. The inverse kinematics algorithm is presented and demonstrated with some illustrative examples of cucumber harvesting, both off-line during the design phase as well as on-line during a field test. [Copyright &y& Elsevier]

Abstract: Membrane systems, also called P systems, are biologically inspired theoretical models of distributed and parallel computing. This paper presents a new class of tissue-like P systems with cell separation, a feature which allows the generation of new workspace. We study the efficiency of the class of P systems and draw a conclusion that only tractable problems can be efficiently solved by using cell separation and communication rules with the length of at most 1. We further present an efficient (uniform) solution to SAT by using cell separation and communication rules with length at most 6. We conclude that a borderline between efficiency and non-efficiency exists in terms of the length of communication rules (assuming ). We discuss future research topics and open problems. [Copyright &y& Elsevier]

Performance results are given of a new type of greenhouse, which combines reflection of near infrared radiation (NIR) with electrical power generation using hybrid photovoltaic cell/thermal collector modules. Besides the generation of electrical and thermal energy, the reflection of the NIR will result in improved climate conditions in the greenhouse. In a previous paper (Sonneveld, P. J., Swinkels, G. L. A. M., Bot, G. P. A., & Flamand, G. (2010). Feasibility study for combining cooling and high grade energy production in a solar greenhouse. Biosystems Engineering, 105, 51–58) a design and feasibility study of this electricity-producing greenhouse was presented. After the description of the construction of this greenhouse, the peak power for Dutch climate circumstances is determined based on the amount of electrical and thermal energy (hot water) produced. The typical yearly yield of this greenhouse system is determined as a total electrical energy of 20kWhm−2 and a thermal energy of 160kWhm−2. Improvements are possible in the spectral range of the NIR film and in the focusing unit of the system. In future the improved electricity-producing greenhouse system could generate 31kWhm−2 of electrical energy and 270kWhm−2 of thermal energy, so it could operate independent of fossil fuels. [Copyright &y& Elsevier]

Abstract: The constant rate birth–death process is used as a stochastic model for many biological systems, for example phylogenies or disease transmission. As the biological data are usually not fully available, it is crucial to understand the effect of incomplete sampling. In this paper, we analyze the constant rate birth–death process with incomplete sampling. We derive the density of the bifurcation events for trees on n leaves which evolved under this birth–death-sampling process. This density is used for calculating prior distributions in Bayesian inference programs and for efficiently simulating trees. We show that the birth–death-sampling process can be interpreted as a birth–death process with reduced rates and complete sampling. This shows that joint inference of birth rate, death rate and sampling probability is not possible. The birth–death-sampling process is compared to the sampling-based population genetics model, the coalescent. It is shown that despite many similarities between these two models, the distribution of bifurcation times remains different even in the case of very large population sizes. We illustrate these findings on an Hepatitis C virus dataset from Egypt. We show that the transmission times estimates are significantly different—the widely used Gamma statistic even changes its sign from negative to positive when switching from the coalescent to the birth–death process. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a mathematical expression of closure to efficient causation in terms of λ-calculus; we argue that this opens up the perspective of developing principled computer simulations of systems closed to efficient causation in an appropriate programming language. An important implication of our formulation is that, by exhibiting an expression in λ-calculus, which is a paradigmatic formalism for computability and programming, we show that there are no conceptual or principled problems in realizing a computer simulation or model of closure to efficient causation. We conclude with a brief discussion of the question whether closure to efficient causation captures all relevant properties of living systems. We suggest that it might not be the case, and that more complex definitions could indeed create crucial some obstacles to computability. [Copyright &y& Elsevier]