Wearable, Machine Washable, Breathable Polyethylenimine/Sodium Alginate Layer-by-Layer-Coated Cotton-Based Multifunctional Triboelectric Nanogenerators.

Cotton-based textiles are ubiquitous in daily life and are prime candidates for application in wearable triboelectric nanogenerators. However, pristine cotton is vulnerable to bacterial attack, lacks antioxidant and ultraviolet (UV)-protective abilities, and shows lower triboelectric charge generation against tribonegative materials because it is present in the neutral region of the triboelectric series. To overcome such drawbacks, herein, a facile layer-by-layer method is proposed, involving the deposition of alternate layers of polyethylenimine (PEI) and sodium alginate (SA) on cotton.

Evaluation of the potential of Delta-aminolevulinic acid for simultaneous detection of bioburden and anti-microbial photodynamic therapy of MRSA infected wounds in Swiss albino mice.

Background: The dramatic increase of drug-resistant bacteria necessitates urgent development of platforms to simultaneously detect and inactivate bacteria causing wound infections, but are confronted with various challenges. Delta amino levulinic acid (ALA) induced protoporphyrin IX (PpIX) can be a promising modality for simultaneous bioburden diagnostics and therapeutics. Herein, we report utility of ALA induced protoporphyrin (PpIX) based simultaneous bioburden detection, photoinactivation and therapeutic outcome assessment in methicillin resistant Staphylococcus aureus (MRSA) infected wounds of mice.

In vitro photoinactivation effectiveness of a portable LED device aimed for intranasal photodisinfection and a photosensitizer formulation comprising methylene blue and potassium iodide against bacterial, fungal, and viral respiratory pathogens.

Antimicrobial photodynamic therapy (aPDT) can be a viable option for management of intranasal infections. However, there are light delivery, fluence, and photosensitizer-related challenges. We report in vitro effectiveness of an easily fabricated, low-cost, portable, LED device and a formulation comprising methylene blue (MB) and potassium iodide (KI) for photoinactivation of pathogens of the nasal cavity, namely, methicillin-resistant Staphylococcus aureus, antibiotic-resistant Klebsiella pneumoniae, multi-antibiotic-resistant Pseudomonas aeruginosa, Candida spp.

Evaluation of antimicrobial photodynamic action of a pluronic and pectin based film loaded with methylene blue against methicillin resistant.

Antimicrobial wound dressings play a crucial role in treatment of wound infections. However, existing commercial options fall short due to antibiotic resistance and the limited spectrum of activity of newly emerging antimicrobials against bacteria that are frequently encountered in wound infections. Antimicrobial photodynamic therapy (aPDT) is very promising alternative therapeutic approach against antibiotic resistant microbes such as methicillin resistant.

Pd Nanoparticle-Decorated Novel Ternary BiOCO-BiMoO-CuO Heterojunction for Enhanced Photo-electrocatalytic Ethanol Oxidation.

Recently, photo-electrooxidation of fuel using a noble metal-semiconductor junction has been one of the most promising approaches in fuel cell systems. Herein, we report the development of a Pd-supported BiMoO-BiOCO-CuO novel ternary heterojunction for ethanol oxidation in alkali in the presence and absence of visible light. Various spectroscopic and microscopic characterization techniques confirm strong coupling between palladium nanoparticles and BiMoO-BiOCO-CuO ternary heterojunction supports.

Pattern-based assessment of the influence of rainfall characteristics on urban stormwater quality.

Urbanisation increases pollutant generation within catchments and their transport to receiving waters. Changes to rainfall patterns, particularly in the age of climate change, make pollution mitigation a challenging task. Understanding how rainfall characteristics could influence the changes to stormwater pollutant runoff is important for designing effective mitigation strategies.

Automatic model calibration of combined hydrologic, hydraulic and stormwater quality models using approximate Bayesian computation.

A range of automatic model calibration techniques are used in water engineering practice. However, use of these techniques can be problematic due to the requirement of evaluating the likelihood function. This paper presents an innovative approach for overcoming this issue using a calibration framework developed based on Approximate Bayesian Computation (ABC) technique.

Pareto Optimality Explanation of the Glycolytic Alternatives in Nature.

The Entner-Doudoroff (ED) and Embden-Meyerhof-Parnas (EMP) glycolytic pathways are largely conserved across glycolytic species in nature. Is this a coincidence, convergent evolution or there exists a driving force towards either of the two pathway designs? We addressed this question by first employing a variant of the optStoic algorithm to exhaustively identify over 11,916 possible routes between glucose and pyruvate at different pre-determined stoichiometric yields of ATP. Subsequently, we analyzed the thermodynamic feasibility of all the pathways at physiological metabolite concentrations and quantified the protein cost of the feasible solutions.

Engineering of inherent fatty acid biosynthesis capacity to increase octanoic acid production.

Background: As a versatile platform chemical, construction of microbial catalysts for free octanoic acid production from biorenewable feedstocks is a promising alternative to existing petroleum-based methods. However, the bio-production strategy has been restricted by the low capacity of inherent fatty acid biosynthesis. In this study, a combination of integrated computational and experimental approach was performed to manipulate the existing metabolic network, with the objective of improving bio-octanoic acid production.

Multilevel engineering of the upstream module of aromatic amino acid biosynthesis in Saccharomyces cerevisiae for high production of polymer and drug precursors.

A multilevel approach was implemented in Saccharomyces cerevisiae to optimize the precursor module of the aromatic amino acid biosynthesis pathway, which is a rich resource for synthesizing a great variety of chemicals ranging from polymer precursor, to nutraceuticals and pain-relief drugs. To facilitate the discovery of novel targets to enhance the pathway flux, we incorporated the computational tool YEASTRACT for predicting novel transcriptional repressors and OptForce strain-design for identifying non-intuitive pathway interventions. The multilevel approach consisted of (i) relieving the pathway from strong transcriptional repression, (ii) removing competing pathways to ensure high carbon capture, and (iii) rewiring precursor pathways to increase the carbon funneling to the desired target.

Personalized Kinetic Models for Predictive Healthcare.

Metabolomics data are used to parameterize individual-specific kinetic models of metabolism to predict medically relevant parameters underpinning disease states or outcomes.

Designing overall stoichiometric conversions and intervening metabolic reactions.

Existing computational tools for de novo metabolic pathway assembly, either based on mixed integer linear programming techniques or graph-search applications, generally only find linear pathways connecting the source to the target metabolite. The overall stoichiometry of conversion along with alternate co-reactant (or co-product) combinations is not part of the pathway design. Therefore, global carbon and energy efficiency is in essence fixed with no opportunities to identify more efficient routes for recycling carbon flux closer to the thermodynamic limit.

Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models.

Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S.

Improving prediction fidelity of cellular metabolism with kinetic descriptions.

Several modeling frameworks for describing and redirecting cellular metabolism have been developed keeping pace with the rapid development in high-throughput data generation and advances in metabolic engineering techniques. The incorporation of kinetic information within stoichiometry-only modeling techniques offers potential advantages for improved phenotype prediction and consequently more precise computational strain design. In addition to substrate-level kinetic regulatory information, the integration of a number of additional layers of regulation at the transcription, translation, and post-translation levels is sought after by many research groups.

Advances in de novo strain design using integrated systems and synthetic biology tools.

Recent efforts in expanding the range of biofuel and biorenewable molecules using microbial production hosts have focused on the introduction of non-native pathways in model organisms and the bio-prospecting of non-model organisms with desirable features. Current challenges lie in the assembly and coordinated expression of the (non-)native pathways and the elimination of competing pathways and undesirable regulation. Several systems and synthetic biology approaches providing contrasting top-down and bottom-up strategies, respectively, have been developed.

Succinate Overproduction: A Case Study of Computational Strain Design Using a Comprehensive Escherichia coli Kinetic Model.

Computational strain-design prediction accuracy has been the focus for many recent efforts through the selective integration of kinetic information into metabolic models. In general, kinetic model prediction quality is determined by the range and scope of genetic and/or environmental perturbations used during parameterization. In this effort, we apply the k-OptForce procedure on a kinetic model of E.

Recent advances in the reconstruction of metabolic models and integration of omics data.

With the ever-accelerating pace of genome sequencing and annotation information generation, the development of computational pipelines for the rapid reconstruction of high-quality metabolic networks has received significant attention. Herein, we review the available biological databases and automated/semi-automated reconstruction tools. In addition, we describe available methodologies for the integration of high-throughput omics data to increase metabolic phenotype prediction accuracy.

k-OptForce: integrating kinetics with flux balance analysis for strain design.

Computational strain design protocols aim at the system-wide identification of intervention strategies for the enhanced production of biochemicals in microorganisms. Existing approaches relying solely on stoichiometry and rudimentary constraint-based regulation overlook the effects of metabolite concentrations and substrate-level enzyme regulation while identifying metabolic interventions. In this paper, we introduce k-OptForce, which integrates the available kinetic descriptions of metabolic steps with stoichiometric models to sharpen the prediction of intervention strategies for improving the bio-production of a chemical of interest.

Systems metabolic engineering design: fatty acid production as an emerging case study.

Increasing demand for petroleum has stimulated industry to develop sustainable production of chemicals and biofuels using microbial cell factories. Fatty acids of chain lengths from C6 to C16 are propitious intermediates for the catalytic synthesis of industrial chemicals and diesel-like biofuels. The abundance of genetic information available for Escherichia coli and specifically, fatty acid metabolism in E.

An integrated computational and experimental study for overproducing fatty acids in Escherichia coli.

Increasing demands for petroleum have stimulated sustainable ways to produce chemicals and biofuels. Specifically, fatty acids of varying chain lengths (C₆-C₁₆) naturally synthesized in many organisms are promising starting points for the catalytic production of industrial chemicals and diesel-like biofuels. However, bio-production of fatty acids from plants and other microbial production hosts relies heavily on manipulating tightly regulated fatty acid biosynthetic pathways.