Acid-stable carmine has recently been distributed in the U.S. market because of its good acid stability, but it is not permitted in Japan. We analyzed and determined the structure of the major pigment in acid-stable carmine, in order to establish an analytical method for it. Carminic acid was transformed into a different type of pigment, named acid-stable carmine, through amination when heated in ammonia solution. The features of the structure were clarified using a model compound, purpurin, in which the orientation of hydroxyl groups on the A ring of the anthraquinone skeleton is the same as that of carminic acid. By spectroscopic means and the synthesis of acid-stable carmine and purpurin derivatives, the structure of the major pigment in acid-stable carmine was established as 4-aminocarminic acid, a novel compound.
Aiming at the objective diagnosis of metastatic axillary lymph nodes in breast cancer, axillary lymph nodes of 220 patients with breast cancer patients were examinedby ultrasonography (US) preoperatively, and those findings were analyzed and compared with histological findings of the metastases. Axillar dissections were performed to examine metastases histologically. Metastatic lymph nodes were detected as definitely lower echoic lesions compared with fatty tissue with distinct or sometimes a little vagus margin. US was available in diagnosing metastatic lymph node with small diameter. Results (US/palpation) were; 0.84/0.47 in sensitivity, 0.66/0.89 in specificity, and 0.74.0.71 in accuracy. US showed a higher sensitivity but a lower specificity compared with palpation method. In addition, US could detect 36 cases of prositive lymph nodes for which palpation failed to find. From these findings US can be thought as available preoperative diagnostic method with fewer false-negative in detecting axillar metastasis of breast cancer. In the diagnosis of axillary lymph node metastasis, combinative use of palpation and US would be recommended.
Accurate solutions to the electronic Schr\"odinger equation can provide valuable insight for electron interactions within molecular systems, accelerating the molecular design and discovery processes in many different applications. However, the availability of such accurate solutions are limited to small molecular systems due to both the extremely high computational complexity and the challenge of operating and executing these workloads on high-performance compute clusters. This work presents a massively scalable cloud-based quantum chemistry platform by implementing a highly parallelizable quantum chemistry method that provides a polynomial-scaling approximation to full configuration interaction (FCI). Our platform orchestrates more than one million virtual CPUs on the cloud to analyze the bond-breaking behaviour of carbon-fluoride bonds of per- and polyfluoroalkyl substances (PFAS) with near-exact accuracy within the chosen basis set. This is the first quantum chemistry calculation utilizing more than one million virtual CPUs on the cloud and is the most accurate electronic structure computation of PFAS bond breaking to date.
Integral equation theory for molecular liquids, three-dimensional reference interaction site model (3D-RISM), is used to study pressure and cosolvent effects on the solvation structure of staphylococcal nuclease (SNase). Solvation structure, partial molar volume, and partial molar volume-compressibility, which relates to the response of solvation structure with respect to the pressure, around SNase are discussed.
A statistical-mechanical, three-dimensional molecular theory of solvation (also know as 3D-RISM) and molecular mechanics were used to study the thermodynamics of aggregation of misfolded prion proteins, based on the theoretical molecular models proposed so far. These include the beta-helical prion trimer (BPT) model of Govaerts et al. (2004), the domain-swapped trimeric prion (DSTP) model of Yang et al. (2005), and the model built after the spiral model of DeMarco and Daggett (2004). It is shown that the solvation contribution to the association free energy can overcome the gain in the internal energy upon association of the proteins. The solvation entropic contribution is as important as the energetic term in the total association free energy. Our calculations show that the spiral-like model is thermodynamically less stable, compared to the DSTP and BPT models. Among the latter two models, the DSTP model is more favorable to association. Quantitative assessment of the solvation effects on the association thermodynamics of prion proteins is provided, and explicitly shows that the solvation contribution is a driving force of the association, in particular, for the existing theoretical models of misfolded prion proteins.
Abstract There can be three types of heterogeneity among players in a rent‐seeking contest. First, effectiveness of player's effort on the winning probabilities may differ among players. Secondly, players may evaluate the rent or prize of the rent‐seeking contest differently. Thirdly, players may face different financial constraints. This article proves under standard assumptions in the literature that there exists a unique pure‐strategy Nash equilibrium in a general asymmetric rent‐seeking contest with these three types of heterogeneity among players.
The majority of computational methods for predicting toxicity of chemicals are typically based on "nonmechanistic" cheminformatics solutions, relying on an arsenal of QSAR descriptors, often vaguely associated with chemical structures, and typically employing "black-box" mathematical algorithms. Nonetheless, such machine learning models, while having lower generalization capacity and interpretability, typically achieve a very high accuracy in predicting various toxicity endpoints, as unambiguously reflected by the results of the recent Tox21 competition. In the current study, we capitalize on the power of modern AI to predict Tox21 benchmark data using merely simple 2D drawings of chemicals, without employing any chemical descriptors. In particular, we have processed rather trivial 2D sketches of molecules with a supervised 2D convolutional neural network (2DConvNet) and demonstrated that the modern image recognition technology results in prediction accuracies comparable to the state-of-the-art cheminformatics tools. Furthermore, the performance of the image-based 2DConvNet model was comparatively evaluated on an external set of compounds from the Prestwick chemical library and resulted in experimental identification of significant and previously unreported antiandrogen potentials for several well-established generic drugs.
Sandarac resin, a natural gum base, is described as “a substance composed mainly of sandaracopimaric acid obtained from the secretion of sandarac trees” in the List of Existing Food Additives in Japan. To evaluate its quality as a food additive, the main constituents in a sandarac resin product were investigated. Three constituents were isolated and identified as sandaracopimaric acid, sandaracopimarinol and 4-epidehydroabietic acid by MS and 2D-NMR. Quantification of the main constituent, sandaracopimaric acid, was performed by HPLC and its content in the product was determined to be 11.6%.
