Curated dataset of asphaltene structures

Asphaltenes, a distinct class of molecules found in crude oil, exhibit insolubility in nonpolar solvents like n-heptane but are soluble in aromatic solvents such as toluene and benzene. Understanding asphaltenes is crucial in the petroleum industry due to their detrimental effects on oil processing, resulting in significant economic losses and production disruptions. While no singular structure defines asphaltenes, two major molecular architectures, namely archipelago and continental models, have gained wide acceptance for their consistency with various experimental investigations and subsequent use in computational studies. The archipelago model comprises two or more polyaromatic hydrocarbon entities interconnected via aliphatic side chains. In contrast, the island or continental model features a unified polyaromatic hydrocarbon moiety with 4 to 10 fused aromatic rings, averaging around 7 rings. To establish a comprehensive collection, we meticulously curated over 250 asphaltene structures derived from previous experimental and computational studies in this field. Our curation process involved an extensive literature survey, conversion of figures from publications into molecular structure files, careful verification of conversion accuracy, and structure editing to ensure alignment with molecular formulas. Our database provides digital structure files and optimized geometries for both predominant structural motifs. The optimization procedure commenced with the PM6 semi-empirical method, followed by further optimization utilizing density functional theory employing the B3LYP functional and the 6-31+G(d,p) basis set. Furthermore, we compiled a range of structural and electronic features for these molecules, serving as a valuable foundation for employing machine learning algorithms to investigate asphaltenes. This work provides a ready to use structural database of asphaltenes and sets the stage for future research endeavours in this domain

Machine learning to identify structural motifs in asphaltenes

Asphaltenes are organic compounds that aggregate in crude oil with two dominant molecular architectures: archipelago and continental. Continental architectures possess a single uniform island structure composed of aromatic rings in contrast to archipelago architectures with aromatic cores interconnected through aliphatic chains. The structural composition of asphaltenes varies globally due to geographical differences, posing challenges in their classification due to a lack of uniformity. This study is the first known exploration of using image-based supervised machine learning, particularly the ResNet-50 neural network, for the binary classification of asphaltenes into continental and archipelago motifs. 255 continental and archipelago models underwent structural augmentations to create a sample size of 1,530 asphaltene structures that is robust enough for accurate results in both the training and testing portions of the machine learning. These augmentations included the repeated addition of carbons until a complete pentane chain was added to a specified carbon on each asphaltene structure. Using Mathematica, supervised ResNet-50 image-based classification was used on both original and augmented structure datasets to classify as either archipelago or continental. The classification was also implemented using topological similarity searching for association between atoms and the distance between them for further molecule identification. This study demonstrates the surprising effectiveness of image-based classification compared to traditional topological feature-based methods. Our results reveal that deep learning techniques, especially image-based approaches, provide novel and insightful ways to differentiate complex molecular structures like asphaltenes, challenging the traditional reliance on topological features alone. This research opens new avenues in chemical analysis and molecular characterization, highlighting the potential of machine learning in complex molecular systems

Covariant Lagrange multiplier constrained higher derivative gravity with scalar projectors

We formulate higher derivative gravity with Lagrange multiplier constraint and scalar projectors. Its gauge-fixed formulation as well as vector fields formulation is developed and corresponding spontaneous Lorentz symmetry breaking is investigated. We show that the only propagating mode is higher derivative graviton while scalar and vector modes do not propagate. Despite to higher derivatives structure of the action, its first FRW equation is the first order differential equation which admits the inflationary universe solution.Comment: Physics Letters B published version. LaTeX 12 page

The Precision nEDM Measurement with UltraCold Neutrons at TRIUMF

The TRIUMF Ultra-Cold Advanced Neutron (TUCAN) collaboration aims at a precision neutron electric dipole moment (nEDM) measurement with an uncertainty of $10^{-27}\,e\cdot\mathrm{cm}$, which is an order-of-magnitude better than the current nEDM upper limit and enables us to test Supersymmetry. To achieve this precision, we are developing a new high-intensity ultracold neutron (UCN) source using super-thermal UCN production in superfluid helium (He-II) and a nEDM spectrometer. The current development status of them is reported in this article.Comment: Proceedings of the 24th International Spin Symposium (SPIN 2021), 18-22 October 2021, Matsue, Japa

Analysis of Germline GLI1 Variation Implicates Hedgehog Signalling in the Regulation of Intestinal Inflammatory Pathways

Charlie Lees and colleagues identify a reduced-function variant of the hedgehog signaling pathway protein GLI1 that associates with inflammatory bowel disease, and investigate its role in a mouse model of colitis

Insulin-like growth factor-I induces the phosphorylation and nuclear exclusion of forkhead transcription factors in human neuroblastoma cells

Akt-mediated phosphorylation of forkhead transcription factors is linked to growth factor-stimulated cell survival. We investigated whether the survival activity of insulin-like growth factor-I (IGF-I) in SH-SY5Y human neuroblastoma (NBL) cells is associated with phosphorylation and/or localization changes in forkhead proteins. IGF-I induced phosphorylation of Erks (p42/p44), FKHR (FOXO1a) (Ser 253), FKHRL1 (FOXO3a) (Ser 256), and Akt (Ser 473). PI3-K inhibitor, LY294002, reduced IGF-I-stimulated phosphorylation of FKHR, FKHRL1, and Akt, but did not affect Erk phosphorylation. Using a GFP-FKHR construct, FKHR imported into the nucleus during growth factor withdrawal-induced apoptosis. In addition, IGF-I rescue from serum withdrawal-induced apoptosis is associated with a rapid export of GFP-FKHR into the cytoplasm. Leptomycin B, an inhibitor of Crm1-mediated nuclear export, decreased the level of FKHRL1 phosphorylation in the presence of IGF-I in vector and FKHR overexpressing cells, but had no effect on the phosphorylation status of FKHR. In addition, leptomycin B prevented IGF-I stimulated nuclear export of GFP-FKHR. These studies show IGF-I phosphorylation of FKHR and FKHRL1 via a PI3-K-dependent pathway in NBL cells.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/44353/1/10495_2005_Article_429.pd

Curated dataset of asphaltene structures

Asphaltenes, a distinct class of molecules found in crude oil, exhibit insolubility in nonpolar solvents like n-heptane but are soluble in aromatic solvents such as toluene and benzene. Understanding asphaltenes is crucial in the petroleum industry due to their detrimental effects on oil processing, resulting in significant economic losses and production disruptions. While no singular structure defines asphaltenes, two major molecular architectures, namely archipelago and continental models, have gained wide acceptance for their consistency with various experimental investigations and subsequent use in computational studies.The archipelago model comprises two or more polyaromatic hydrocarbon entities interconnected via aliphatic side chains. In contrast, the island or continental model features a unified polyaromatic hydrocarbon moiety with 4 to 10 fused aromatic rings, averaging around 7 rings. To establish a comprehensive collection, we meticulously curated over 250 asphaltene structures derived from previous experimental and computational studies in this field. Our curation process involved an extensive literature survey, conversion of figures from publications into molecular structure files, careful verification of conversion accuracy, and structure editing to ensure alignment with molecular formulas. Our database provides digital structure files and optimized geometries for both predominant structural motifs. The optimization procedure commenced with the PM6 semi-empirical method, followed by further optimization utilizing density functional theory employing the B3LYP functional and the 6-31+G(d,p) basis set. Furthermore, we compiled a range of structural and electronic features for these molecules, serving as a valuable foundation for employing machine learning algorithms to investigate asphaltenes. This work provides a ready to use structural database of asphaltenes and sets the stage for future research endeavours in this domain