Straw utilization for biofuel production: A consequential assessment of greenhouse gas emissions from bioethanol and biomethane provision with a focus on the time dependency of emissions

The shift from straw incorporation to biofuel production entails emissions from production, changes in soil organic carbon (SOC) and through the provision of (co‐)products and entailed displacement effects. This paper analyses changes in greenhouse gas (GHG) emissions arising from the shift from straw incorporation to biomethane and bioethanol production. The biomethane concept comprises comminution, anaerobic digestion and amine washing. It additionally provides an organic fertilizer. Bioethanol production comprises energetic use of lignin, steam explosion, enzymatic hydrolysis and co‐fermentation. Additionally, feed is provided. A detailed consequential GHG balance with in‐depth focus on the time dependency of emissions is conducted: (a) the change in the atmospheric load of emissions arising from the change in the temporal occurrence of emissions comparing two steady states (before the shift and once a new steady state has established); and (b) the annual change in overall emissions over time starting from the shift are assessed. The shift from straw incorporation to biomethane production results in net changes in GHG emissions of (a) ?979 (?436 to ?1,654) and (b) ?955 (?220 to ?1,623) kg CO₂‐eq. per t_{dry matter} straw converted to biomethane (minimum and maximum). The shift to bioethanol production results in net changes of (a) ?409 (?107 to ?610) and (b) ?361 (57 to ?603) kg CO₂‐eq. per t_{dry matter} straw converted to bioethanol. If the atmospheric load of emissions arising from different timing of emissions is neglected in case (a), the change in GHG emissions differs by up to 54%. Case (b) reveals carbon payback times of 0 (0–49) and 19 (1–100) years in case of biomethane and bioethanol production, respectively. These results demonstrate that the detailed inclusion of temporal aspects into GHG balances is required to get a comprehensive understanding of changes in GHG emissions induced by the introduction of advanced biofuels from agricultural residues.     

Development of a 3D Tissue‐Engineered Skeletal Muscle and Bone Co‐culture System

In vitro 3D tissue‐engineered (TE) structures have been shown to better represent in vivo tissue morphology and biochemical pathways than monolayer culture, and are less ethically questionable than animal models. However, to create systems with even greater relevance, multiple integrated tissue systems should be recreated in vitro. In the present study, the effects and conditions most suitable for the co‐culture of TE skeletal muscle and bone are investigated. High‐glucose Dulbecco's modified Eagle medium (HG‐DMEM) supplemented with 20% fetal bovine serum followed by HG‐DMEM with 2% horse serum is found to enable proliferation of both C2C12 muscle precursor cells and TE85 human osteosarcoma cells, fusion of C2C12s into myotubes, as well as an upregulation of RUNX2/CBFa1 in TE85s. Myotube formation is also evident within indirect contact monolayer cultures. Finally, in 3D co‐cultures, TE85 collagen/hydroxyapatite constructs have significantly greater expression of RUNX2/CBFa1 and osteocalcin/BGLAP in the presence of collagen‐based C2C12 skeletal muscle constructs; however, fusion within these constructs appears reduced. This work demonstrates the first report of the simultaneous co‐culture and differentiation of 3D TE skeletal muscle and bone, and represents a significant step toward a full in vitro 3D musculoskeletal junction model.     

Permeabilized Escherichia coli Whole Cells Containing Co‐Expressed Two Thermophilic Enzymes Facilitate the Synthesis of scyllo‐Inositol from myo‐Inositol

Scyllo‐inositol (SI), a stereoisomer of inositol, is regarded as a promising therapeutic agent for Alzheimer's disease. Here, an in vitro cofactor‐balance biotransformation for the production of SI from myo‐inositol (MI) by thermophilic myo‐inositol 2‐dehydrogenase (IDH) and scyllo‐inositol 2‐dehydrogenase (SIDH) is presented. These two enzymes (i.e., IDH and SIDH from Geobacillus kaustophilus) are co‐expressed in Escherichia coli BL21(DE3), and E. coli cells containing the two enzymes are permeabilized by heat treatment as whole‐cell catalysts to convert MI to SI. After condition optimizations about permeabilized temperature, reaction temperature, and initial MI concentration, about 82 g L^?1 of SI is produced from 250 g L^?1 of MI within 24 h without any cofactor supplementation. This final titer of SI produced is the highest to the authors’ limited knowledge. This study provides a promising method for the large‐scale industrial production of SI.     

Photosynthetic Reduction of Xylose to Xylitol Using Cyanobacteria

Photosynthetic generation of reducing power makes cyanobacteria an attractive host for biochemical reduction compared to cell‐free and heterotrophic systems, which require burning of additional resources for the supply of reducing equivalent. Here, using xylitol synthesis as an example, efficient uptake and reduction of xylose photoautotrophically in Synechococcus elongatus PCC7942 are demonstrated upon introduction of an effective xylose transporter from Escherichia coli (Ec‐XylE) and the NADPH‐dependent xylose reductase from Candida boidinii (Cb‐XR). Simultaneous activation of xylose uptake and matching of cofactor specificity enabled an average xylitol yield of 0.9 g g^?1 xylose and a maximum productivity of about 0.15 g L^?1 day^?1 OD^?1 with increased level of xylose supply. While long‐term cellular maintenance still appears challenging, high‐density conversion of xylose to xylitol using concentrated resting cell further pushes the titer of xylitol formation to 33 g L^?1 in six days with 85% of maximum theoretical yield. While the results show that the unknown dissipation of xylose can be minimized when coupled to a strong reaction outlet, it remains to be the major hurdle hampering the yield despite the reported inability of cyanobacteria to metabolize xylose.     

A Site‐Specific Integration Reporter System That Enables Rapid Evaluation of CRISPR/Cas9‐Mediated Genome Editing Strategies in CHO Cells

Targeted gene knockout and site‐specific integration (SSI) are powerful genome editing techniques to improve the development of industrially relevant Chinese hamster ovary (CHO) cell lines. However, past efforts to perform SSI in CHO cells are characterized by low efficiencies. Moreover, numerous strategies proposed to boost SSI efficiency in mammalian cell types have yet to be evaluated head to head or in combination to appreciably boost efficiencies in CHO. To enable systematic and rapid optimization of genome editing methods, the SSIGNAL (s ite‐s pecific i ntegration and g en ome al teration) reporter system is developed. This tool can analyze CRISPR (clustered regularly interspaced palindromic repeats)/Cas9 (CRISPR‐associated protein 9)‐mediated disruption activity alone or in conjunction with SSI efficiency. The reporter system uses green and red dual‐fluorescence signals to indicate genotype states within four days following transfection, facilitating rapid data acquisition via standard flow cytometry instrumentation. In addition to describing the design and development of the system, two of its applications are demonstrated by first comparing transfection conditions to maximize CRISPR/Cas9 activity and subsequently assessing the efficiency of several promising SSI strategies. Due to its sensitivity and versatility, the SSIGNAL reporter system may serve as a tool to advance genome editing technology.     

Plasmonic Biosensor Controlled by DNAzyme for On‐Site Genetic Detection of Pathogens

On‐site predetection of pathogens could significantly decrease of a disease outbreak or national loss in most of the countries. However, conventional detection techniques are limited in use for on‐site detection due to the necessity of specialized skill or equipment. Therefore, it is necessary to develop a new technique that can predetect pathogens in the field without special skills or equipment. Here, a DNAzyme strategy to control a plasmonic biosensor for rapid and simple visual detection of Salmonella choleraesuis is adopted. Multicomponent DNAzyme formed by target addition can cleave the linker effectively at 50 °C. Linker cleavage induces dispersion of two DNA‐immobilized gold nanoparticles and color change. Under optimized assay conditions, the target could be detected via visual discrimination sensitively and specifically. Moreover, the biosensor shows the possibility of practical use with contaminants and a 16S rRNA real target. As a result, the proposed plasmonic biosensor can visually detect S. choleraesuis without unstable enzymes, a specialized technique, or equipment. Therefore, these advantages could allow that this biosensor would be used for on‐site predetection to lower the risk of transmission of infectious diseases.     

Molecular Targets for the Testing of COVID‐19

The pandemic outbreaks of coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), spread all over the world in a short period of time. Efficient identification of the infection by SARS‐CoV‐2 has been one of the most important tasks to facilitate all the following counter measurements in dealing with the infectious disease. In Taiwan, a COVID‐19 Open Science Platform adheres to the spirit of open science: sharing sources, data, and methods to promote progress in academic research while corroborating findings from various disciplines has established in mid‐February 2020, for collaborative research in support of the development of detection methods, therapeutics, and a vaccine for COVID‐19. Research priorities include infection control, epidemiology, clinical characterization and management, detection methods (including viral RNA detection, viral antigen detection, and serum antibody detection), therapeutics (neutralizing antibody and small molecule drugs), vaccines, and SARS‐CoV‐2 pathogenesis. In addition, research on social ethics and the law are included to take full account of the impact of the COVID‐19 virus.     

Overexpression of ER Protein Processing and Apoptosis Regulator Genes in Human Embryonic Kidney 293 Cells Improves Gene Therapy Vectors Production

Gammaretroviral and lentiviral vectors (γ‐RV and LV) are among the most used vectors in gene therapy. Currently, human embryonic kidney (HEK) 293 cells, the manufacture platform of choice for these vectors, provide low transducing particle yields, challenging their therapeutic applications and commercialization. This work studies metabolic pathways, focusing on endoplasmic reticulum (ER) protein processing and anti‐apoptotic mechanisms, influencing vector productivity in HEK 293 cell substrates. To that end, four candidate genes—protein disulfide isomerase family A member 2 gene, heat shock protein family A (Hsp70) member 5 gene, X‐box binding protein 1 gene (ER protein processing), and B‐cell lymphoma 2 protein gene (anti‐apoptotic)—are individually stably expressed in the cells. How their overexpression level influence vector yields is analyzed by establishing cell populations with incremental genomic copies of each. γ‐RV volumetric productivity increases up to 97% when overexpressing ER protein processing genes. LV volumetric production increases 53% when overexpressing the anti‐apoptotic gene. Improvements are associated with higher cell specific productivities and dependent on gene overexpression level, highlighting the importance of fine‐tuning gene expression. Overall, this work discloses gene engineering targets enabling efficient gene therapy product manufacture showing that ER protein processing and anti‐apoptotic pathways are pivotal to producer cell performance.     

Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change

Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species' distributions, relies on correlations between species' localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species' current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species' current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species' range dynamics.     

Complementary strengths of spatially-explicit and multi-species distribution models

Species distribution models (SDMs) project the outcome of community assembly processes – dispersal, the abiotic environment and biotic interactions – onto geographic space. Recent advances in SDMs account for these processes by simultaneously modeling the species that comprise a community in a multivariate statistical framework or by incorporating residual spatial autocorrelation in SDMs. However, the effects of combining both multivariate and spatially-explicit model structures on the ecological inferences and the predictive abilities of a model are largely unknown. We used data on eastern hemlock Tsuga canadensis and five additional co-occurring overstory tree species in 35 569 forest stands across Michigan, USA to evaluate how the choice of model structure, including spatial and non-spatial forms of univariate and multivariate models, affects ecological inference about the processes that shape community composition as well as model predictive ability. Incorporating residual spatial autocorrelation via spatial random effects did not improve out-of-sample prediction for the six tree species, although in-sample model fit was higher in the spatial models. Spatial models attributed less variation in occurrence probability to environmental covariates than the non-spatial models for all six tree species, and estimated higher (more positive) residual co-occurrence values for most species pairs. The non-spatial multivariate model was better suited for evaluating habitat suitability and hypotheses about the processes that shape community composition. Environmental correlations and residual correlations among species pairs were positively related, perhaps indicating that residual correlations were due to shared responses to unmeasured environmental covariates. This work highlights the importance of choosing a non-spatial model formulation to address research questions about the species–environment relationship or residual co-occurrence patterns, and a spatial model formulation when within-sample prediction accuracy is the main goal.