Tailoring next-generation biofuels and their combustion in next-generation engines

Increasing energy costs, the dependence on foreign oil supplies, and environmental concerns have emphasized the need to produce sustainable renewable fuels and chemicals. The strategy for producing next-generation biofuels must include efficient processes for biomass conversion to liquid fuels and the fuels must be compatible with current and future engines. Unfortunately, biofuel development generally takes place without any consideration of combustion characteristics, and combustion scientists typically measure biofuels properties without any feedback to the production design. We seek to optimize the fuel/engine system by bringing combustion performance, specifically for advanced next-generation engines, into the development of novel biosynthetic fuel pathways. Here we report an innovative coupling of combustion chemistry, from fundamentals to engine measurements, to the optimization of fuel production using metabolic engineering. We have established the necessary connections among the fundamental chemistry, engine science, and synthetic biology for fuel production, building a powerful framework for co-development of engines and biofuels

Microbiome preterm birth DREAM challenge: Crowdsourcing machine learning approaches to advance preterm birth research

Every year, 11% of infants are born preterm with significant health consequences, with the vaginal microbiome a risk factor for preterm birth. We crowdsource models to predict (1) preterm birth (PTB; \u3c37 \u3eweeks) or (2) early preterm birth (ePTB; \u3c32 \u3eweeks) from 9 vaginal microbiome studies representing 3,578 samples from 1,268 pregnant individuals, aggregated from public raw data via phylogenetic harmonization. The predictive models are validated on two independent unpublished datasets representing 331 samples from 148 pregnant individuals. The top-performing models (among 148 and 121 submissions from 318 teams) achieve area under the receiver operator characteristic (AUROC) curve scores of 0.69 and 0.87 predicting PTB and ePTB, respectively. Alpha diversity, VALENCIA community state types, and composition are important features in the top-performing models, most of which are tree-based methods. This work is a model for translation of microbiome data into clinically relevant predictive models and to better understand preterm birth

Direct Visualization by Cryo-EM of the Mycobacterial Capsular Layer: A Labile Structure Containing ESX-1-Secreted Proteins

The cell envelope of mycobacteria, a group of Gram positive bacteria, is composed of a plasma membrane and a Gram-negative-like outer membrane containing mycolic acids. In addition, the surface of the mycobacteria is coated with an ill-characterized layer of extractable, non-covalently linked glycans, lipids and proteins, collectively known as the capsule, whose occurrence is a matter of debate. By using plunge freezing cryo-electron microscopy technique, we were able to show that pathogenic mycobacteria produce a thick capsule, only present when the cells were grown under unperturbed conditions and easily removed by mild detergents. This detergent-labile capsule layer contains arabinomannan, α-glucan and oligomannosyl-capped glycolipids. Further immunogenic and proteomic analyses revealed that Mycobacterium marinum capsule contains high amounts of proteins that are secreted via the ESX-1 pathway. Finally, cell infection experiments demonstrated the importance of the capsule for binding to cells and dampening of pro-inflammatory cytokine response. Together, these results show a direct visualization of the mycobacterial capsular layer as a labile structure that contains ESX-1-secreted proteins

The Human Nasal Microbiota and Staphylococcus aureus Carriage

BACKGROUND: Colonization of humans with Staphylococcus aureus is a critical prerequisite of subsequent clinical infection of the skin, blood, lung, heart and other deep tissues. S. aureus persistently or intermittently colonizes the nares of approximately 50% of healthy adults, whereas approximately 50% of the general population is rarely or never colonized by this pathogen. Because microbial consortia within the nasal cavity may be an important determinant of S. aureus colonization we determined the composition and dynamics of the nasal microbiota and correlated specific microorganisms with S. aureus colonization. METHODOLOGY/PRINCIPAL FINDINGS: Nasal specimens were collected longitudinally from five healthy adults and a cross-section of hospitalized patients (26 S. aureus carriers and 16 non-carriers). Culture-independent analysis of 16S rRNA sequences revealed that the nasal microbiota of healthy subjects consists primarily of members of the phylum Actinobacteria (e.g., Propionibacterium spp. and Corynebacterium spp.), with proportionally less representation of other phyla, including Firmicutes (e.g., Staphylococcus spp.) and Proteobacteria (e.g. Enterobacter spp). In contrast, inpatient nasal microbiotas were enriched in S. aureus or Staphylococcus epidermidis and diminished in several actinobacterial groups, most notably Propionibacterium acnes. Moreover, within the inpatient population S. aureus colonization was negatively correlated with the abundances of several microbial groups, including S. epidermidis (p = 0.004). CONCLUSIONS/SIGNIFICANCE: The nares environment is colonized by a temporally stable microbiota that is distinct from other regions of the integument. Negative association between S. aureus, S. epidermidis, and other groups suggests microbial competition during colonization of the nares, a finding that could be exploited to limit S. aureus colonization

DPHL: A DIA Pan-human Protein Mass Spectrometry Library for Robust Biomarker Discovery

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipeline and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to generate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Automatic reaction mechanism generation :

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, 2016.Cataloged from PDF version of thesis.Includes bibliographical references.Growing awareness of climate change and the risks associated with our society's dependence on fossil fuels has motivated global initiatives to develop economically viable, renewable energy sources. However, the transportation sector remains a major hurdle. Although electric vehicles are becoming more mainstream, the transportation sector is expected to continue relying heavily on combustion engines, particularly in the freight and airline industries. Therefore, research efforts to develop cleaner combustion must continue. This includes the development of more efficient combustion engines, identification of compatible alternative fuels, and the streamlining of existing petroleum resources. These dynamic systems have complex chemistry and are often difficult and expensive to probe experimentally, making detailed chemical kinetic modeling an attractive option for simulating and predicting macroscopic observables such as ignition delay or CO₂ concentrations. This thesis presents several methods and applications towards high fidelity predictive modeling using Reaction Mechanism Generator (RMG), an open source software package which automatically constructs kinetic mechanisms. Several sources contribute to model error during automatic mechanism generation, including incomplete or incorrect handling of chemistry, poor estimation of thermodynamic and kinetics parameters, and uncertainty propagation. First, an overview of RMG is presented along with algorithmic changes for handling incomplete or incorrect chemistry. Completeness of chemistry is often limited by CPU speed and memory in the combinational problem of generating reactions for large molecules. A method for filtering reactions is presented for efficiently and accurately building models for larger systems. An extensible species representation was also implemented based on chemical graph theory, allowing chemistry to be extended to lone pairs, charges, and variable valencies. Several chemistries are explored in this thesis through modeling three combustion related processes. Ketone and cyclic ether chemistry are explored in the study of diisoproyl ketone and cineole, biofuel candidates produced by fungi in the decomposition of cellulosic biomass. Detailed kinetic modeling in conjunction with engine experiments and metabolic engineering form a collaborative feedback loop that efficiently screens biofuel candidates for use in novel engine technologies. Next, the challenge of modeling constrained cyclic geometries is tackled in generating a combustion model of JP-10, a synthetic jet fuel used in propulsion technologies. The model is validated against experimental and literature data and succeeds in capturing key product distributions, including aromatic compounds, which are precursors to polyaromatic hydrocarbons (PAHs) and soot. Finally, oil-to-gas cracking processes under geological conditions are studied through modeling the low temperature pyrolysis of the heavy oil analog phenyldodecane in the presence of diethyldisulfide. This system is used to gather mechanistic insight on the observation that sulfur-rich kerogens have accelerated oil-to-gas decomposition, a topic relevant to petroleum reservoir modeling. The model shows that free radical timescales matter in low temperature systems where alkylaromatics are relatively stable. Local and global uncertainty propagation methods are used to analyze error in automatically generated kinetic models. A framework for local uncertainty analysis was implemented using Cantera as a backend. Global uncertainty analysis was implemented using adaptive Smolyak pscudospcctral approximations to efficiently compute and construct polynomial chaos expansions (PCE) to approximate the dependence of outputs on a subset of uncertain inputs. Both local and global methods provide similar qualitative insights towards identifying the most influential input parameters in a model. The analysis shows that correlated uncertainties based on kinetics rate rules and group additivity estimates of thermochemistry drastically reduce a model's degrees of freedom and can have a large impact on model outputs. These results highlight the necessity of uncertainty analysis in the mechanism generation workflow. This thesis demonstrates that predictive chemical kinetics can aid in the mechanistic understanding of complex chemical processes and contributes new methods for refining and building high fidelity models in the automatic mechanism generation workflow. These contributions are available to the kinetics community through the RMG software package.by Connie W. Gao.Ph. D

A detailed combined experimental and theoretical study on dimethyl ether/propane blended oxidation

In this paper, a binary fuel model for dimethyl ether (DME) and propane is developed, with a focus on engine-relevant conditions (10–50 atm and 550–2000 K). New rapid compression machine (RCM) data are obtained for the purpose of further validating the binary fuel model, identifying reactions important to low-temperature propane and DME oxidation, and understanding the ignition-promoting effect of DME on propane. It is found that the simulated RCM data for DME/propane mixtures is very sensitive to the rates of C₃H₈ + OH, which acts as a radical sink relative to DME oxidation, especially at high relative DME concentrations. New rate evaluations are conducted for the reactions of C₃H₈ + OH = products as well as the self-reaction of methoxymethyl peroxy (in competition with RO₂ = QOOH isomerization) of 2CH₃OCH₂O₂ = products. Accurate phenomenological rate constants, k(T, P), are computed by RRKM/ME methods (with energies obtained at the CCSD(T)-F12a/cc-pVTZ-F12 level of theory) for several radical intermediates relevant to DME. The model developed in this paper (120 species and 711 reactions) performs well against the experimental targets tested here and is suitable for use over a wide range of conditions. In addition, the reaction mechanism generator software RMG is used to explore cross-reactions between propane and DME radical intermediates. These cross-reactions did not have a significant effect on simulations of the conditions modeled in this paper, suggesting that kinetic models for high- and low-reactivity binary fuel mixtures may be assembled from addition of their corresponding submodels and a small molecule foundation model

Reaction Mechanism Generator: Automatic construction of chemical kinetic mechanisms

© 2016 The Authors. Published by Elsevier B.V. Reaction Mechanism Generator (RMG) constructs kinetic models composed of elementary chemical reaction steps using a general understanding of how molecules react. Species thermochemistry is estimated through Benson group additivity and reaction rate coefficients are estimated using a database of known rate rules and reaction templates. At its core, RMG relies on two fundamental data structures: graphs and trees. Graphs are used to represent chemical structures, and trees are used to represent thermodynamic and kinetic data. Models are generated using a rate-based algorithm which excludes species from the model based on reaction fluxes. RMG can generate reaction mechanisms for species involving carbon, hydrogen, oxygen, sulfur, and nitrogen. It also has capabilities for estimating transport and solvation properties, and it automatically computes pressure-dependent rate coefficients and identifies chemically-activated reaction paths. RMG is an object-oriented program written in Python, which provides a stable, robust programming architecture for developing an extensible and modular code base with a large suite of unit tests. Computationally intensive functions are cythonized for speed improvements. Program summary Program title: RMG Catalogue identifier: AEZW-v1-0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEZW-v1-0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: MIT/X11 License No. of lines in distributed program, including test data, etc.: 958681 No. of bytes in distributed program, including test data, etc.: 9495441 Distribution format: tar.gz Programming language: Python. Computer: Windows, Ubuntu, and Mac OS computers with relevant compilers. Operating system: Unix/Linux/Windows. RAM: 1 GB minimum, 16 GB or more for larger simulations Classification: 16.12. External routines: RDKit, Open Babel, DASSL, DASPK, DQED, NumPy, SciPy Nature of problem: Automatic generation of chemical kinetic mechanisms for molecules containing C, H, O, S, and N. Solution method: Rate-based algorithm adds most important species and reactions to a model, with rate constants derived from rate rules and other parameters estimated via group additivity methods. Additional comments: The RMG software package also includes CanTherm, a tool for computing the thermodynamic properties of chemical species and both high-pressure-limit and pressure-dependent rate coefficients for chemical reactions using results from quantum chemical calculations. CanTherm is compatible with a variety of ab initio quantum chemistry software programs, including but not limited to Gaussian, MOPAC, QChem, and MOLPRO. Running time: From 30 s for the simplest molecules, to up to several weeks, depending on the size of the molecule and the conditions of the reaction system chosen