A semimechanistic model predicting the growth and production of the bioenergy crop Miscanthus×giganteus: description, parameterization and validation

Biomass based bioenergy is promoted as a major sustainable energy source which can simultaneously decrease net greenhouse gas emissions. Miscanthus × giganteus ( M. × giganteus ), a C₄ perennial grass with high nitrogen, water, and light use efficiencies, is regarded as a promising energy crop for biomass production. Mathematical models which can accurately predict M. × giganteus biomass production potential under different conditions are critical to evaluate the feasibility of its production in different environments. Although previous models based on light-conversion efficiency have been shown to provide good predictions of yield, they cannot easily be used in assessing the value of physiological trait improvement or ecosystem processes. Here, we described in detail the physical and physiological processes of a previously published generic mechanistic eco-physiological model, WIMOVAC, adapted and parameterized for M. × giganteus . Parameterized for one location in England, the model was able to realistically predict daily field diurnal photosynthesis and seasonal biomass at a range of other sites from European studies. The model provides a framework that will allow incorporation of further mechanistic information as it is developed for this new crop.     

Cloning and plant‐based production of antibody MC10E7 for a lateral flow immunoassay to detect [4‐arginine]microcystin in freshwater

Antibody MC10E7 is one of a small number of monoclonal antibodies that bind specifically to [Arg4]‐microcystins, and it can be used to survey natural water sources and food samples for algal toxin contamination. However, the development of sensitive immunoassays in different test formats, particularly user‐friendly tests for on‐site analysis, requires a sensitive but also cost‐effective antibody. The original version of MC10E7 was derived from a murine hybridoma, but we determined the sequence of the variable regions using the peptide mass‐assisted cloning strategy and expressed a scFv (single‐chain variable fragment) format of this antibody in yeast and a chimeric full‐size version in leaves of Nicotiana tabacum and Nicotiana benthamiana to facilitate inexpensive and scalable production. The specific antigen‐binding activity of the purified antibody was verified by surface plasmon resonance spectroscopy and ELISA, confirming the same binding specificity as its hybridoma‐derived counterpart. The plant‐derived antibody was used to design a lateral flow immunoassay (dipstick) for the sensitive detection of [Arg4]‐microcystins at concentrations of 100–300 ng/L in freshwater samples collected at different sites. Plant‐based production will likely reduce the cost of the antibody, currently the most expensive component of the dipstick immunoassay, and will allow the development of further antibody‐based analytical devices and water purification adsorbents for the efficient removal of toxic contaminants.     

Bioenergy crop models: descriptions,data requirements,and future challenges

Field studies that address the production of lignocellulosic biomass as a source of renewable energy provide critical data for the development of bioenergy crop models. A literature survey revealed that 14 models have been used for simulating bioenergy crops including herbaceous and woody bioenergy crops, and for crassulacean acid metabolism (CAM) crops. These models simulate field‐scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro‐BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane at regional scales (Agro‐IBIS and LPJmL). Although it lacks the complexity of other bioenergy crop models, the environmental productivity index (EPI) is the only model used to estimate biomass production of CAM (Agave and Opuntia) plants. Except for the EPI model, all models include representations of leaf area dynamics, phenology, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few models simulate soil water, nutrient, and carbon cycle dynamics, making them especially useful for assessing the environmental consequences (e.g., erosion and nutrient losses) associated with the large‐scale deployment of bioenergy crops. The rapid increase in use of models for energy crop simulation is encouraging; however, detailed information on the influence of climate, soils, and crop management practices on biomass production is scarce. Thus considerable work remains regarding the parameterization and validation of process‐based models for bioenergy crops; generation and distribution of high‐quality field data for model development and validation; and implementation of an integrated framework for efficient, high‐resolution simulations of biomass production for use in planning sustainable bioenergy systems.     

CPhos: A program to calculate and visualize evolutionarily conserved functional phosphorylation sites

Profiling using high‐throughput MS has discovered an overwhelming number of novel protein phosphorylation sites (“phosphosites”). However, the functional relevance of these sites is not always clear. In light of recent studies on the evolutionary mechanism of phosphorylation, we have developed CPhos, a Java program that can assess the conservation of phosphosites among species using an information theory‐based approach. The degree of conservation established using CPhos can be used to assess the functional significance of phosphosites. CPhos has a user friendly graphical user interface and is available both as a web service and as a standalone Java application to assist phosphoproteomic researchers in analyzing and prioritizing lists of phosphosites for further experimental validation. CPhos can be accessed or downloaded at http://helixweb.nih.gov/CPhos/ .     

BETYdb: a yield,trait, and ecosystem service database applied to second‐generation bioenergy feedstock production

Increasing demand for sustainable energy has led to research and development on the cultivation of diverse plant species for biomass production. To support the research and development required to domesticate and cultivate crops for bioenergy, we developed the Biofuel Ecophysiological Traits and Yields database (BETYdb). BETYdb is a centralized open‐access repository that facilitates organization, discovery, and exchange of information about plant traits, crop yields, and ecosystem functions. BETYdb provides user interfaces to simplify storage and discovery as well as programming interfaces that support automated and reproducible scientific workflows. Presently, BETYdb contains over forty thousand observations of plant traits, biomass yields, and ecosystem dynamics collected from the published articles and ongoing field studies. Over half of these records represent fewer than ten genera that have been intensively evaluated for biomass production, while the other half represent over two thousand plant species reflecting research on new crops, unmanaged ecosystems, and land use transitions associated with bioenergy. BETYdb has been accessed over twenty‐five thousand times and is used in the fields of bioenergy and ecosystem ecology to quantify yield potential and ecosystem functioning of crops and unmanaged systems under present and future climates. Here, we summarize the database contents and illustrate its applications. We show its utility in a new analysis that confirms that Miscanthus is twice as productive as switchgrass over a much wider range of environmental and management conditions than covered in previous analyses. We compare traits related to carbon uptake and water use of these species with each other and with two coppice shrubs, poplar and willow. These examples, along with a growing body of published research that used BETYdb, illustrate the scope of research supported through this open‐access database.     

Enabling high‐throughput biology with flexible open‐source automation

Our understanding of complex living systems is limited by our capacity to perform experiments in high throughput. While robotic systems have automated many traditional hand‐pipetting protocols, software limitations have precluded more advanced maneuvers required to manipulate, maintain, and monitor hundreds of experiments in parallel. Here, we present Pyhamilton, an open‐source Python platform that can execute complex pipetting patterns required for custom high‐throughput experiments such as the simulation of metapopulation dynamics. With an integrated plate reader, we maintain nearly 500 remotely monitored bacterial cultures in log‐phase growth for days without user intervention by taking regular density measurements to adjust the robotic method in real‐time. Using these capabilities, we systematically optimize bioreactor protein production by monitoring the fluorescent protein expression and growth rates of a hundred different continuous culture conditions in triplicate to comprehensively sample the carbon, nitrogen, and phosphorus fitness landscape. Our results demonstrate that flexible software can empower existing hardware to enable new types and scales of experiments, empowering areas from biomanufacturing to fundamental biology.     

User−Producer Interaction in Housing Energy Innovations

Von Hippel and colleagues have highlighted the crucial role of users in innovation in different industries and types of products. They describe the innovation process in terms of the distinct domains of knowledge that producers and users possess. Producers have knowledge about technical solutions and users about their needs, the context of use, and their own capabilities as users. Both sets of knowledge are characterized by "stickiness": They move relatively freely within their own domain but are difficult to transfer outside of it.
In the case of radical innovations for sustainable consumption, the problem of "sticky information" is compounded. Both producers and consumers need to reach out of their conventional competencies and search for new solutions. "Societal actors," such as government bodies or environmental experts, can show the way to such solutions, but this new knowledge needs to be integrated with the "sticky" knowledge about everyday practices in production and consumption.
In the present article we attempt to conceptualize the role and interaction of user and producer knowledge with the knowledge of environmental experts in housing energy innovations. We do so by applying the user−producer interaction framework to a case study on the introduction of low-energy housing concepts in Finland. On the basis of this analysis, we draw conclusions on the potential and limitations of today's practices in the field. For example, we suggest that user involvement can help to enhance the acceptance of low-energy solutions but that the methods for involving users need to be adapted to the particular circumstances in each industry.     

Siderophore production in Azotobacter vinelandii in response to Fe‐, Mo‐ and V‐limitation

Azotobacter vinelandii is a terrestrial diazotroph well studied for its siderophore production capacity and its role as a model nitrogen fixer. In addition to Fe, A. vinelandii siderophores are used for the acquisition of the nitrogenase co‐factors Mo and V. However, regulation of siderophore production by Mo‐ and V‐limitation has been difficult to confirm and knowledge of the full suite of siderophores synthesized by this organism has only recently become available. Using this new information, we conducted an extensive study of siderophore production in N₂‐fixing A. vinelandii under a variety of trace metal conditions. Our results show that under Fe‐limitation the production of all siderophores increases, while under Mo‐limitation only catechol siderophore production is increased, with the strongest response seen in protochelin. We also find that the newly discovered A. vinelandii siderophore vibrioferrin is almost completely repressed under Mo‐ and V‐limitation. An examination of the potential nitrogen ‘cost’ of siderophore production reveals that investments in siderophore N can represent as much as 35% of fixed N, with substantial differences between cultures using the Mo‐ as opposed to the less efficient V‐nitrogenase.     

Modelling the carbon cycle of Miscanthus plantations: existing models and the potential for their improvement

The lignocellulosic perennial grass Miscanthus has received considerable attention as a potential bioenergy crop over the last 25 years, but few commercial plantations exist globally. This is partly due to the uncertainty associated with claims that land‐use change (LUC) to Miscanthus will result in both commercially viable yields and net increases in carbon (C) storage. To simulate what the effects may be after LUC to Miscanthus, six process‐based models have been parameterized for Miscanthus and here we review how these models operate. This review provides an overview of the key Miscanthus soil organic matter models and then highlights what measurers can do to accelerate model development. Each model (WIMOVAC, BioCro, Agro‐IBIS, DAYCENT, DNDC and ECOSSE) is capable of simulating biomass production and soil C dynamics based on specific site characteristics. Understanding the design of these models is important in model selection as well as being important for field researchers to collect the most relevant data to improve model performance. The rapid increase in models parameterized for Miscanthus is promising, but refinements and improvements are still required to ensure that model predictions are reliable and can be applied to spatial scales relevant for policy. Specific improvements, needed to ensure the models are applicable for a range of environmental conditions, come under two categories: (i) increased data generation and (ii) development of frameworks and databases to allow simulations of ranging scales. Research into nonfood bioenergy crops such as Miscanthus is relatively recent and this review highlights that there are still a number of knowledge gaps regarding Miscanthus specifically. For example, the low input requirements of Miscanthus make it particularly attractive as a bioenergy crop, but it is essential that we increase our understanding of the crop's nutrient remobilization and ability to host N‐fixing organisms to derive the most accurate simulations.     

Modellbank Biotechnologie – Ein Softwarepaket zur Modellauswahl,Optimierung und Simulation biotechnologischer Prozesse

The MODELBASE BIOTECHNOLOGY has been developed for gathering and processing data obtained from biochemical experiments. It covers the analysis of growth kinetics and production kinetics as well and may be used for calibration curve determination too. The implementation of models defined by the user is supported. Both, differential equations and their integrated forms are accepted. In addition to parameter estimation a statistical analysis can be carried on request. Two and three dimensional graphical representation facilitates the interpretation of results. Fields of application are biochemistry, biotechnology, microbiology, engineering, pharmacy, equipment design, education.     

A Simple,Low-Cost Conductive Composite Material for 3D Printing of Electronic Sensors

3D printing technology can produce complex objects directly from computer aided digital designs. The technology has traditionally been used by large companies to produce fit and form concept prototypes (‘rapid prototyping’) before production. In recent years however there has been a move to adopt the technology as full-scale manufacturing solution. The advent of low-cost, desktop 3D printers such as the RepRap and emoH@baF has meant a wider user base are now able to have access to desktop manufacturing platforms enabling them to produce highly customised products for personal use and sale. This uptake in usage has been coupled with a demand for printing technology and materials able to print functional elements such as electronic sensors. Here we present formulation of a simple conductive thermoplastic composite we term ‘carbomorph’ and demonstrate how it can be used in an unmodified low-cost 3D printer to print electronic sensors able to sense mechanical flexing and capacitance changes. We show how this capability can be used to produce custom sensing devices and user interface devices along with printed objects with embedded sensing capability. This advance in low-cost 3D printing with offer a new paradigm in the 3D printing field with printed sensors and electronics embedded inside 3D printed objects in a single build process without requiring complex or expensive materials incorporating additives such as carbon nanotubes.     

Life Cycle Assessment of Biomass‐based Combined Heat and Power Plants

Norway, like many countries, has realized the need to extensively plan its renewable energy future sooner rather than later. Combined heat and power (CHP) through gasification of forest residues is one technology that is expected to aid Norway in achieving a desired doubling of bioenergy production by 2020. To assess the environmental impacts to determine the most suitable CHP size, we performed a unit process‐based attributional life cycle assessment (LCA), in which we compared three scales of CHP over ten environmental impact categories—micro (0.1 megawatts electricity [MWe]), small (1 MWe), and medium (50 MWe) scale. The functional units used were 1 megajoule (MJ) of electricity and 1 MJ of district heating delivered to the end user (two functional units), and therefore, the environmental impacts from distribution of electricity and hot water to the consumer were also considered. This study focuses on a regional perspective situated in middle‐Norway's Nord‐ and Sør‐Trøndelag counties. Overall, the unit‐based environmental impacts between the scales of CHP were quite mixed and within the same magnitude. The results indicated that energy distribution from CHP plant to end user creates from less than 1% to nearly 90% of the total system impacts, depending on impact category and energy product. Also, an optimal small‐scale CHP plant may be the best environmental option. The CHP systems had a global warming potential ranging from 2.4 to 2.8 grams of carbon dioxide equivalent per megajoule of thermal (g CO₂‐eq/MJ_th) district heating and from 8.8 to 10.5 grams carbon dioxide equivalent per megajoule of electricity (g CO₂‐eq/MJ_el) to the end user.     

Simple and reliable detection of slime production of <Emphasis Type="Italic">Candida</Emphasis><Emphasis Type="Italic">spp</Emphasis>. directly from blood culture bottles: Comparison of visual tube method and transmission electron microscopy

Early detection of slime production may be useful for clinical decision because of its suggestive property for potential pathogenic capacity of a Candida strain especially in patients with a prosthetic device. In this study we aimed to compare the visual tube method (VTM) with transmission electron microscopy (TEM) in order to confirm the reliability of the former method. In order to demonstrate the reproducibility of the tube method and to determine the correct timing for the test, Candida isolates directly obtained from blood culture (DBC) bottles and their two subsequent subcultures were used. The results of this study showed that VTM is a simple and reliable method which can be used in every clinical mycology laboratory, provided that the test is applied on DBC isolates; as the ability of slime production is decreased or lost even after the first subculturing. We suggest that this simple method can be used and may have some contributions to the ongoing studies on the controversial issue concerning removal of biomaterials in candidemic patients.     

StructureSelector: A web‐based software to select and visualize the optimal number of clusters using multiple methods

Inferences of population genetic structure are of great importance to the fields of ecology and evolutionary biology. The program structure has been widely used to infer population genetic structure. However, previous studies demonstrated that uneven sampling often leads to wrong inferences on hierarchical structure. The most widely used ΔK method tends to identify the uppermost hierarchy of population structure. Recently, four alternative statistics (medmedk , medmeak , maxmedk and maxmeak ) were proposed, which appear to be more accurate than the previously used methods for both even and uneven sampling data. However, the lack of easy‐to‐use software limits the use of these appealing new estimators. Here, we developed a web‐based user‐friendly software structureselector to calculate the four appealing alternative statistics together with the commonly used Ln Pr(X|K) and ΔK statistics. structureselector accepts the result files of structure , admixture or faststructure as input files. It reports the “best” K for each estimator, and the results are available as HTML or tab separated tables. The program can also generate graphical representations for specific K, which can be easily downloaded from the server. The software is freely available at http://lmme.qdio.ac.cn/StructureSelector/ .     

CODON—Software to manual curation of prokaryotic genomes

Genome annotation conceptually consists of inferring and assigning biological information to gene products. Over the years, numerous pipelines and computational tools have been developed aiming to automate this task and assist researchers in gaining knowledge about target genes of study. However, even with these technological advances, manual annotation or manual curation is necessary, where the information attributed to the gene products is verified and enriched. Despite being called the gold standard process for depositing data in a biological database, the task of manual curation requires significant time and effort from researchers who sometimes have to parse through numerous products in various public databases. To assist with this problem, we present CODON, a tool for manual curation of genomic data, capable of performing the prediction and annotation process. This software makes use of a finite state machine in the prediction process and automatically annotates products based on information obtained from the Uniprot database. CODON is equipped with a simple and intuitive graphic interface that assists on manual curation, enabling the user to decide about the analysis based on information as to identity, length of the alignment, and name of the organism in which the product obtained a match. Further, visual analysis of all matches found in the database is possible, impacting significantly in the curation task considering that the user has at his disposal all the information available for a given product. An analysis performed on eleven organisms was used to test the efficiency of this tool by comparing the results of prediction and annotation through CODON to ones from the NCBI and RAST platforms.     

The ImageJ ecosystem: Open‐source software for image visualization,processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.     

Metabolic engineering of Saccharomyces cerevisiae for production of fatty acid–derived hydrocarbons

Fatty acid–derived hydrocarbons attract increasing attention as biofuels due to their immiscibility with water, high‐energy content, low freezing point, and high compatibility with existing refineries and end‐user infrastructures. Yeast Saccharomyces cerevisiae has advantages for production of fatty acid–derived hydrocarbons as its native routes toward fatty acid synthesis involve only a few reactions that allow more efficient conversion of carbon substrates. Here we describe major biosynthetic pathways of fatty acid–derived hydrocarbons in yeast, and summarize key metabolic engineering strategies, including enhancing precursor supply, eliminating competing pathways, and expressing heterologous pathways. With recent advances in yeast production of fatty acid–derived hydrocarbons, our review identifies key research challenges and opportunities for future optimization, and concludes with perspectives and outlooks for further research directions.