Integral techno-economic comparison and greenhouse gas balances of different production routes of aromatics from biomass with CO₂ capture

The techno-economic performance and CO2 equivalent (CO2eq) reduction potential of bio-based aromatic production cases with and without CO2 capture and storage (CCS) have been evaluated and compared to those of fossil-based aromatic production. The bio-cases include tail gas reactive pyrolysis (TGRP), catalytic pyrolysis (CP), hydrothermal liquefaction (HTL), gasification-methanol-aromatics (GMA), and Diels-Alder of furan/furfural combined with catalytic pyrolysis of lignin (FFCA). The crude oil-based naphtha catalytic reforming (NACR) routes have GHG emissions of 43.4 and 43.9 t CO2eq/t aromatics with and without CCS (NACR-CCS), respectively. Except for HTL, all the biomass cases with CCS show negative emissions between −6.1 and −1.1 t CO2eq/t aromatics with avoidance costs ranging from 27.7 to 93.3 $/t CO2eq. Under favorable conditions, GMA with CCS (GMA-CCS) has the lowest emissions (−14.6 t CO2eq/t aromatics), while CP with CCS (CP-CCS) shows the lowest avoidance cost (12.3$/t CO2eq). All biomass based aromatics production techniques are currently at the laboratory or demonstration stages, except for CP, which has pilot plants. The results indicate that bio-based aromatics production, with their reasonable avoidance costs and low, or potentially negative, greenhouse gas (GHG) emissions, are an attractive option to compensate for the expected aromatic production shortages in the coming decades

Precision drug repurposing via convergent eQTL-based molecules and pathway targeting independent disease-associated polymorphisms

Repurposing existing drugs for new therapeutic indications can improve success rates and streamline development. Use of large-scale biomedical data repositories, including eQTL regulatory relationships and genome-wide disease risk associations, offers opportunities to propose novel indications for drugs targeting common or convergent molecular candidates associated to two or more diseases. This proposed novel computational approach scales across 262 complex diseases, building a multi-partite hierarchical network integrating (i) GWAS-derived SNP-to-disease associations, (ii) eQTL-derived SNP-to-eGene associations incorporating both cis-and trans-relationships from 19 tissues, (iii) protein target-to-drug, and (iv) drug-to-disease indications with (iv) Gene Ontology-based information theoretic semantic (ITS) similarity calculated between protein target functions. Our hypothesis is that if two diseases are associated to a common or functionally similar eGene -and a drug targeting that eGene/protein in one disease exists - the second disease becomes a potential repurposing indication. To explore this, all possible pairs of independently segregating GWAS-derived SNPs were generated, and a statistical network of similarity within each SNP-SNP pair was calculated according to scale-free overrepresentation of convergent biological processes activity in regulated eGenes (ITSeGENE-eGENE) and scale-free overrepresentation of common eGene targets between the two SNPs (ITSSNP-SNP). Significance of ITSSNP-SNP was conservatively estimated using empirical scale-free permutation resampling keeping the node-degree constant for each molecule in each permutation. We identified 26 new drug repurposing indication candidates spanning 89 GWAS diseases, including a potential repurposing of the calcium-channel blocker Verapamil from coronary disease to gout. Predictions from our approach are compared to known drug indications using DrugBank as a gold standard (odds ratio=13.1, p-value=2.49x10(-8)). Because of specific disease-SNPs associations to candidate drug targets, the proposed method provides evidence for future precision drug repositioning to a patient's specific polymorphisms.University of Arizona Health Sciences CB2; BIO5 Institute; UA Cancer Center; NIH [U01AI122275]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context

Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics

Magnetic Activities of M-type Stars Based on LAMOST DR5 and Kepler and K2 Missions

We performed a statistical study of magnetic activities of M-type stars by combining the spectra of LAMOST DR5 with light curves from the Kepler and K2 missions. We mainly want to study the relationship between chromospheric activity and flares, and their relations of magnetic activity and rotation period. We have obtained the maximum catalog of 516,688 M-type stellar spectra of 480,912 M stars from LAMOST DR5 and calculated their equivalent widths of chromospheric activity indicators (Hα, Hβ, Hγ, Hδ, Ca II H&K, and He I D3). Using the Hα indicator, 40,464 spectra of 38,417 M stars show chromospheric activity, and 1791 of these 5499 M-type stars with repeated observations have Hα variability. We used an automatic detection plus visual inspection method to detect 17,432 flares on 8964 M-type stars from the catalog by cross-matching LAMOST DR5 and the Kepler and K2 databases. We used the Lomb–Scargle method to calculate their rotation periods. We find that the flare frequency is consistent with the ratio of activities of these chromospheric activity indicators as a function of spectral type in M0–M3. We find the equivalent widths of Hα and Ca II H have a significant statistical correlation with the flare amplitude in M-type stars. We confirm that the stellar flare is affected by both the stellar magnetic activity and the rotation period. Finally, using the Hα equivalent width equal to 0.75 Å and using the rotation period equal to 10 days as the threshold for the M-type stellar flare time frequency are almost equivalent

16S Next-generation sequencing and quantitative PCR reveal the distribution of potential pathogens in the Liaohe Estuary

The existence of potentially pathogenic bacteria seriously threatens aquatic animals and human health. Estuaries are closely related to human activities, and the detection of pathogens is important for aquaculture and public health. However, monitoring only indicator microorganisms and pathogens is not enough to accurately and comprehensively estimate water pollution. Here, the diversity of potentially pathogenic bacteria in water samples from the Liaohe estuary was profiled using 16S next-generation sequencing (16S NGS) and quantitative polymerase chain reaction (qPCR) analysis. The results showed that the dominant genera of environmental pathogens were Pseudomonas, Vibrio, Mycobacterium, Acinetobacter, Exiguobacterium, Sphingomonas, and Legionella, and the abundance of enteric pathogens was significantly less than the environmental pathogens, mainly, Citrobacter, Enterococcus, Escherichia-Shigella, Enterobacter, Bacteroides. The qPCR results showed that the 16S rRNA genes of Vibrio were the most abundant, with concentrations between 7.06 and 9.48 lg copies/L, followed by oaa gene, fliC gene, trh gene, and uidA gene, and the temperature and salinity were the main factors affecting its abundance. Variance partitioning analysis (VPA) analysis of spatial factors on the potential pathogen’s distribution (19.6% vs 5.3%) was greater than environmental factors. In addition, the co-occurrence analysis of potential pathogens in the estuary revealed significant co-occurrence among the opportunistic pathogens Testosteronemonas, Brevimonas vesicularis, and Pseudomonas putida. Our findings provide an essential reference for monitoring and occurrence of potentially pathogenic bacteria in estuaries

Integral techno-economic comparison and greenhouse gas balances of different production routes of aromatics from biomass with CO2 capture

The techno-economic performance and CO2 equivalent (CO2eq) reduction potential of bio-based aromatic production cases with and without CO2 capture and storage (CCS) have been evaluated and compared to those of fossil-based aromatic production. The bio-cases include tail gas reactive pyrolysis (TGRP), catalytic pyrolysis (CP), hydrothermal liquefaction (HTL), gasification-methanol-aromatics (GMA), and Diels-Alder of furan/furfural combined with catalytic pyrolysis of lignin (FFCA). The crude oil-based naphtha catalytic reforming (NACR) routes have GHG emissions of 43.4 and 43.9 t CO2eq/t aromatics with and without CCS (NACR-CCS), respectively. Except for HTL, all the biomass cases with CCS show negative emissions between −6.1 and −1.1 t CO2eq/t aromatics with avoidance costs ranging from 27.7 to 93.3 $/t CO2eq. Under favorable conditions, GMA with CCS (GMA-CCS) has the lowest emissions (−14.6 t CO2eq/t aromatics), while CP with CCS (CP-CCS) shows the lowest avoidance cost (12.3$/t CO2eq). All biomass based aromatics production techniques are currently at the laboratory or demonstration stages, except for CP, which has pilot plants. The results indicate that bio-based aromatics production, with their reasonable avoidance costs and low, or potentially negative, greenhouse gas (GHG) emissions, are an attractive option to compensate for the expected aromatic production shortages in the coming decades

Progress in Research on Bacteriophage Receptor-Binding Proteins and Host Range Extension

Phages are one of the most diverse and widely distributed organisms on the earth today, and have great advantages in eliminating bacteria, especially multi-drug resistant bacteria that are widespread at present. At present, a variety of bacteriophage products have been well applied in the fields of breeding for food raw materials, farmed animals, foodborne pathogenic bacteria detection, and instant food sterilization. However, the narrow host range and complex interaction mechanism between bacteriophage and host bacteria greatly limit their development as new bacteriostatic agents. The specific binding between bacteriophages and host bacteria depends on the receptor binding proteins on the surface of bacteriophages. Studying the structure, function and mechanism of action of various bacteriophage receptor binding proteins will help in understanding the interaction process between bacteriophage and bacteria. On this basis, phage receptor binding protein modification by genetic engineering and induced culture methods to obtain high-quality broad-spectrum phages has become an important content of phage research. This paper summarizes the current research on different structures of phage receptor binding proteins and various host spectrum expansion schemes in an effort to provide theoretical support for the development and application of bacteriophages in the food industry