171 research outputs found
Metagenomic Taxonomy-Guided Database-Searching Strategy for Improving Metaproteomic Analysis
No full textMetaproteomics provides a direct measure of the functional information by investigating all proteins expressed by a microbiota. However, due to the complexity and heterogeneity of microbial communities, it is very hard to construct a sequence database suitable for a metaproteomic study. Using a public database, researchers might not be able to identify proteins from poorly characterized microbial species, while a sequencing-based metagenomic database may not provide adequate coverage for all potentially expressed protein sequences. To address this challenge, we propose a metagenomic taxonomy-guided database-search strategy (MT), in which a merged database is employed, consisting of both taxonomy-guided reference protein sequences from public databases and proteins from metagenome assembly. By applying our MT strategy to a mock microbial mixture, about two times as many peptides were detected as with the metagenomic database only. According to the evaluation of the reliability of taxonomic attribution, the rate of misassignments was comparable to that obtained using an a priori matched database. We also evaluated the MT strategy with a human gut microbial sample, and we found 1.7 times as many peptides as using a standard metagenomic database. In conclusion, our MT strategy allows the construction of databases able to provide high sensitivity and precision in peptide identification in metaproteomic studies, enabling the detection of proteins from poorly characterized species within the microbiota
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From Theory to Practice: Multi-Disciplinary Approaches to Lithium-ion Battery Monitoring and Functional Material Discovery
No full textThis thesis delivers a thorough analysis of lithium-ion battery monitoring technologiesand the discovery of new functional materials. It starts with an exploration using Transient Grating Spectroscopy (TGS) to implement a non-destructive technique for assessing
the state of lithiation in battery cells. This approach yields precise observations of the
internal behaviors of battery cells under diverse conditions, particularly with a focus on
lithium electrodeposition. Through the study of surface acoustic waves (SAW), which
are highly sensitive to microstructural changes on electrode surfaces during nucleation
and growth, the potential of TGS for in-situ monitoring of electrochemical interfaces and
identifying defects in battery electrodes is emphasized.
The research progresses to examine the freeze-thaw dynamics of lithium-ion battery
graphite electrodes at extreme temperatures to evaluate battery durability and performance under conditions similar to those on lunar missions. Analyses using the time of
flight and damping properties of Bulk Acoustic Waves (BAW), coupled with electrochemical assessments, provide insights into the mechanical responses of batteries to significant
temperature variations, essential for space missions and other harsh environments.
Furthermore, the thesis investigates the role of AI in materials science, particularly
how large language models can autonomously generate a database of magnetocaloric
effect (MCE) materials from published sources. This AI-enhanced method identifies
promising MCE materials across various temperatures, confirmed through density functional theory simulations and automated structural determinations. The integration of
these discoveries into a self-updating database emphasizes the transformative role of AI in
advancing material discovery, and in the future, it could prove invaluable for developing
battery components such as cathodes, anodes, and electrolyte
From Theory to Practice: Multi-Disciplinary Approaches to Lithium-ion Battery Monitoring and Functional Material Discovery
No full textA Contrastive Study of the English Constructions V + O and V + PP + O and the Differences in Translation
No full textMapping quantitative trait loci in line cross with repeat records
No full textAbstract Background Phenotypes with repeat records from one individual or multiple individuals were often encountered in practices of mapping QTL in linecross. The current genetic mapping method for a trait with repeat records is adopted by simply replacing the phenotype by the average value of the repeat records. This simple treatment has not sufficiently utilized the information from the replication and ignored the impacts of the permanent environmental effects on the accuracy of the estimated QTL. Results We propose to map QTL by using the repeatability model to directly analyze the repeat records rather than simply analyze the mean phenotype, improving the efficiency of QTL detecting because of adequately utilizing the information from data and allowing for the permanent environmental effects. A maximum likelihood method implemented via the expectation-maximization (EM) algorithm is applied to perform the parameter estimation of the repeatability model. The superiority of the mapping method based on the repeatability model over simple analysis using the mean phenotype was demonstrated by a series of simulations. Conclusion Our results suggest that the proposed method can serve as a powerful alternative to existing methods. By mean of the repeatability model, utilizing the repeat records on individual may improve the efficiency of QTL detecting in line cross.</p
Non-covalent surface modification of boron nitride nanotubes for enhanced catalysis
No full textBoron nitride nanotubes were functionalized by microperoxidase-11 in aqueous media, showing improved catalytic performance due to a strong electron coupling 10 between the active centre of microperoxidase-11 and boron nitride nanotubes. One main application challenge of enzymes as biocatalysts is molecular aggregation in the aqueous solution. This issue is addressed by immobilization of enzymes on solid supports which 15 can enhance enzyme stability and facilitate separation, and recovery for reuse while maintaining catalytic activity and selectivity. The protein-nanoparticle interactions play a key role in bio-nanotechnology and emerge with the development of nanoparticle-protein “corona”. Bio-molecular coronas provide a 20 unique biological identity of nanosized materials.1, 2 As a structural analogue to carbon nanotubes (CNTs), Boron nitride nanotubes have boron and nitrogen atoms distributed equally in hexagonal rings and exhibit excellent mechanical strength, unique physical properties, and chemical stability at high-temperatures. 25 The chemical inertness of BN materials suits to work in hazardous environments, making them an optimal candidate in practical applications in biological and medical field.3,
Nitrogen utilization analysis reveals the synergetic effect of arginine and urea in promoting fucoxanthin biosynthesis in the mixotrophic marine diatom Phaeodactylum tricornutum
No full textFucoxanthin is a new dietary ingredient applied in healthy foods with specific benefits of body weight loss and liver fat reduction. The marine diatom Phaeodactylum tricornutum is a highly suitable species for fucoxanthin production. In the present study, aiming to promote fucoxanthin biosynthesis in mixotrophic P. tricornutum , NaNO 3 , tryptone, and urea were evaluated as nitrogen sources with 0.10 mol L −1 of glycerol as the organic carbon source for mixotrophic growth in shake flasks. Compared to NaNO 3 , the mixture of tryptone and urea (referred to as T+U, 1:1, mol N:mol N) as organic nitrogen sources could induce a higher biomass and fucoxanthin production. Through nitrogen utilization analysis, leucine, arginine, lysine, and phenylalanine in the T+U medium were identified as the amino acids that primarily support cell growth. Among those amino acids, arginine causes the highest rate of nitrogen utilization and cell growth promotion. After 12 days of cultivation, the highest biomass concentration (3.18 g L −1 ), fucoxanthin content (12.17 mg g −1 ), and productivity (2.68 mg L −1 day −1 ) were achieved using 25 mmol N L −1 of arginine and 5 mmol N L −1 of urea as nitrogen sources, indicating that arginine and urea performed synergistically on enhancing biomass and pigment production. This study provides new insights into the promotion of fucoxanthin biosynthesis by nitrogen utilization analysis and verifies the synergetic effect of arginine and urea on facilitating the development of a promising strategy for efficient enhancement of fucoxanthin production through mixotrophic cultivation of P. tricornutum
Mapping genome-wide QTL of ratio traits with Bayesian shrinkage analysis for its component traits
No full textYang R: Forward LASSO analysis for high-order interactions in genome-wide association study. Brief Bioinform 2013. Jun 17. [Epub ahead of print
No full textAbstract Previous genome-wide association study (GWAS) focused on low-order interactions between pairwise single-nucleotide polymorphisms (SNPs) with significant main effects. Little is known how high-order interactions effect, especially one among the SNPs without main effects regulates quantitative traits. Within the frameworks of linear model and generalized linear model, the LASSO with coordinate descent step can be used to simultaneously analyze thousands and thousands of SNPs for normal and discrete traits. With consideration of high-order interactions among SNPs, a huge number of genetic effects make the LASSO failing to work under the presented condition of computation. Forward LASSO analysis is, therefore, proposed to shrink most of genetic effects to be zeros stage by stage. Simulation demonstrates that our proposed method could be used instead of the LASSO method for full model in mapping high-order interactions. Application of forward LASSO method is provided to GWAS for carcass traits and meat quality traits in beef cattle
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