Analyzing mechanisms in Co(i) redox catalysis using a pattern recognition platform.
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ABSTRACT:
SUBMITTER: Tang T
PROVIDER: S-EPMC8179645 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundFew studies have systematically evaluated birth defect co-occurrence patterns, perhaps, in part, due to the lack of software designed to implement large-scale, complex analytic methods.MethodsWe created an R-based platform, "co-occurring defect analysis" (CODA), designed to implement analyses of birth defect co-occurrence patterns in birth defect registries. CODA uses an established algorithm for calculating the observed-to-expected ratio of a given birth defect combination, accounting for the known tendency of birth defects to co-occur nonspecifically. To demonstrate CODA's feasibility, we evaluated the computational time needed to assess 2- to 5-way combinations of major birth defects in the Texas Birth Defects Registry (TBDR) (1999-2014). We report on two examples of pairwise patterns, defects co-occurring with trisomy 21 or with non-syndromic spina bifida, to demonstrate proof-of-concept.ResultsWe evaluated combinations of 175 major birth defects among 206,784 infants in the TBDR. CODA performed efficiently in the data set, analyzing 1.5 million 5-way combinations in 18 hr. As anticipated, we identified large observed-to-expected ratios for the birth defects that co-occur with trisomy 21 or spina bifida.ConclusionsCODA is available for application to birth defect data sets and can be used to better understand co-occurrence patterns. Co-occurrence patterns elucidated by using CODA may be helpful for identifying new birth defect associations and may provide etiological insights regarding potentially shared pathogenic mechanisms. CODA may also have wider applications, such as assessing patterns of additional types of co-occurrence patterns in other large data sets (e.g., medical records).
| S-EPMC7814855 | biostudies-literature
Project description:Macroalgae have high potential to be an efficient, and sustainable feedstock for the production of biofuels and other more valuable chemicals. Attempts have been made to enable the co-fermentation of alginate and mannitol by Saccharomyces cerevisiae to unlock the full potential of this marine biomass. However, the efficient use of the sugars derived from macroalgae depends on the equilibrium of cofactors derived from the alginate and mannitol catabolic pathways. There are a number of strong metabolic limitations that have to be tackled before this bioconversion can be carried out efficiently by engineered yeast cells. An analysis of the redox balance during ethanol fermentation from alginate and mannitol by Saccharomyces cerevisiae using metabolic engineering tools was carried out. To represent the strain designed for conversion of macroalgae carbohydrates to ethanol, a context-specific model was derived from the available yeast genome-scale metabolic reconstructions. Flux balance analysis and dynamic simulations were used to determine the flux distributions. The model indicates that ethanol production is determined by the activity of 4-deoxy-l-erythro-5-hexoseulose uronate (DEHU) reductase (DehR) and its preferences for NADH or NADPH which influences strongly the flow of cellular resources. Different scenarios were explored to determine the equilibrium between NAD(H) and NADP(H) that will lead to increased ethanol yields on mannitol and DEHU under anaerobic conditions. When rates of mannitol dehydrogenase and DehRNADH tend to be close to a ratio in the range 1-1.6, high growth rates and ethanol yields were predicted. The analysis shows a number of metabolic limitations that are not easily identified through experimental procedures such as quantifying the impact of the cofactor preference by DEHU reductase in the system, the low flux into the alginate catabolic pathway, and a detailed analysis of the redox balance. These results show that production of ethanol and other chemicals can be optimized if a redox balance is achieved. A possible methodology to achieve this balance is presented. This paper shows how metabolic engineering tools are essential to comprehend and overcome this limitation.
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Project description:BackgroundPattern recognition algorithms are useful in bioimage informatics applications such as quantifying cellular and subcellular objects, annotating gene expressions, and classifying phenotypes. To provide effective and efficient image classification and annotation for the ever-increasing microscopic images, it is desirable to have tools that can combine and compare various algorithms, and build customizable solution for different biological problems. However, current tools often offer a limited solution in generating user-friendly and extensible tools for annotating higher dimensional images that correspond to multiple complicated categories.ResultsWe develop the BIOimage Classification and Annotation Tool (BIOCAT). It is able to apply pattern recognition algorithms to two- and three-dimensional biological image sets as well as regions of interest (ROIs) in individual images for automatic classification and annotation. We also propose a 3D anisotropic wavelet feature extractor for extracting textural features from 3D images with xy-z resolution disparity. The extractor is one of the about 20 built-in algorithms of feature extractors, selectors and classifiers in BIOCAT. The algorithms are modularized so that they can be "chained" in a customizable way to form adaptive solution for various problems, and the plugin-based extensibility gives the tool an open architecture to incorporate future algorithms. We have applied BIOCAT to classification and annotation of images and ROIs of different properties with applications in cell biology and neuroscience.ConclusionsBIOCAT provides a user-friendly, portable platform for pattern recognition based biological image classification of two- and three- dimensional images and ROIs. We show, via diverse case studies, that different algorithms and their combinations have different suitability for various problems. The customizability of BIOCAT is thus expected to be useful for providing effective and efficient solutions for a variety of biological problems involving image classification and annotation. We also demonstrate the effectiveness of 3D anisotropic wavelet in classifying both 3D image sets and ROIs.
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Project description:Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward the realization of energy-efficient machine learning hardware.
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Project description:Biofilms are complex aggregates formed by microorganisms such as bacteria, fungi and algae, which grow at the interfaces between water and natural or artificial materials. They are actively involved in processes of sorption and desorption of metal ions in water and reflect the environmental conditions in the recent past. Therefore, biofilms can be used as bioindicators of water quality. The goal of this study was to determine whether the biofilms, developed in different aquatic systems, could be successfully discriminated using data on their elemental compositions. Biofilms were grown on natural or polycarbonate materials in flowing water, standing water and seawater bodies. Using an unsupervised technique such as principal component analysis (PCA) and several supervised methods like classification and regression trees (CART), discriminant partial least squares regression (DPLS) and uninformative variable elimination-DPLS (UVE-DPLS), we could confirm the uniqueness of sea biofilms and make a distinction between flowing water and standing water biofilms. The CART, DPLS and UVE-DPLS discriminant models were validated with an independent test set selected either by the Kennard and Stone method or the duplex algorithm. The best model was obtained from CART with 100% correct classification rate for the test set designed by the Kennard and Stone algorithm. With CART, one variable describing the Mg content in the biofilm water phase was found to be important for the discrimination of flowing water and standing water biofilms.
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Project description:The generalization of related asymmetric processes in organocatalyzed reactions is an ongoing challenge due to subtle, noncovalent interactions that drive selectivity. The lack of transferability is often met with a largely empirical approach to optimizing catalyst structure and reaction conditions. This has led to the development of diverse structural catalyst motifs and inspired unique design principles in this field. Bifunctional hydrogen bond donor (HBD) catalysis exemplifies this in which a broad collection of enantioselective transformations has been successfully developed. Herein, we describe the use of data science methods to connect catalyst and substrate structural features of an array of reported enantioselective bifunctional HBD catalysis through an iterative statistical modeling process. The computational parameters used to build the correlations are mechanism-specific based on the proposed transition states, which allows for analysis into the noncovalent interactions responsible for asymmetric induction. The resulting statistical models also allow for extrapolation to out-of-sample examples to provide a prediction platform that can be used for future applications of bifunctional hydrogen bond donor catalysis. Finally, this multireaction workflow presents an opportunity to build statistical models unifying various modes of activation relevant to asymmetric organocatalysis.
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Project description:Using a simple copper catalyst, the alkylation of nitroalkanes with ?-bromocarbonyls is now possible. This method provides a general, functional group tolerant route to ?-nitrocarbonyl compounds, including nitro amides, esters, ketones, and aldehydes. The highly sterically dense, functional group rich products from these reactions can be readily elaborated into a range of complex nitrogen-containing molecules, including highly substituted ?-amino acids.
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Project description:Lignin is a recalcitrant and underexploited natural feedstock for aromatic commodity chemicals, and its degradation generally requires the use of high temperatures and harsh reaction conditions. Herein we present an ambient temperature one-pot process for the controlled oxidation and depolymerization of this potent resource. Harnessing the potential of electrocatalytic oxidation in conjugation with our photocatalytic cleavage methodology, we have developed an operationally simple procedure for selective fragmentation of ?-O-4 bonds with excellent mass recovery, which provides a unique opportunity to expand the existing lignin usage from energy source to commodity chemicals and synthetic building block source.
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Project description:The mechanism of the recently reported catalyzed asymmetric hydrogenation of enyne 1 catalyzed by the Co-(R,R)-QuinoxP* complex was studied by DFT. Conceivable pathways for the Co(I)-Co(III) mechanism were computed together with a Co(0)-Co(II) catalytic cycle. It is commonly assumed that the exact nature of the chemical transformations taking place along the actually operating catalytic pathway determine the sense and level of enantioselection of the catalytic reaction. In this work, two chemically different mechanisms reproduced the experimentally observed perfect stereoselection of the same handedness. Moreover, the relative stabilities of the transition states of the stereo induction stages were controlled via exactly the same weak disperse interactions between the catalyst and the substrate.
| S-EPMC10057697 | biostudies-literature
Project description:BackgroundThere have been many recent breakthroughs in processing and analyzing large-scale data sets in biomedical informatics. For example, the CytoGPS algorithm has enabled the use of text-based karyotypes by transforming them into a binary model. However, such advances are accompanied by new problems of data sparsity, heterogeneity, and noisiness that are magnified by the large-scale multidimensional nature of the data. To address these problems, we developed the Mercator R package, which processes and visualizes binary biomedical data. We use Mercator to address biomedical questions of cytogenetic patterns relating to lymphoid hematologic malignancies, which include a broad set of leukemias and lymphomas. Karyotype data are one of the most common form of genetic data collected on lymphoid malignancies, because karyotyping is part of the standard of care in these cancers.ResultsIn this paper we combine the analytic power of CytoGPS and Mercator to perform a large-scale multidimensional pattern recognition study on 22,741 karyotype samples in 47 different hematologic malignancies obtained from the public Mitelman database.ConclusionOur findings indicate that Mercator was able to identify both known and novel cytogenetic patterns across different lymphoid malignancies, furthering our understanding of the genetics of these diseases.
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