135 research outputs found
Nonuniform quadrupolar orders in the spin-3/2 generalized Heisenberg chain
The generation of nonuniform quadrupole states plays a crucial role in
understanding various fascinating phenomena observed in the advancement of
several research areas, e.g., multiferroic compounds, nonmagnetic
superconductors, etc. In this work, we investigate the ground-state phase
diagram of a generalized spin-3/2 bilinear-biquadratic-bicubic Heisenberg chain
in the representation of multipolar operators. By numerical simulations with
the large-scale density-matrix renormalization group (DMRG) method, we
successfully identify a tetramerization phase and a stripe-Q phase. These
phases are characterized by the emergence of nonuniform quadrupole orders
resulting from the spontaneous breaking of translation symmetry. In particular,
tetramerization phase refers to the quadrupole operators take a four-cycle,
while the stripe-Q phase represents a striped pattern in quadrupole operators.
Additionally, we demonstrate the presence of a Wess-Zumino-Witten (WZW) model
with level k = 1 at the transition point between the dimerized (DM) phase and
the Luttinger liquid (LL) phase, based on strong numerical findings
2,4-Di-tert-butyl-6-[1-(3,5-di-tert-butyl-2-hydroxyphenyl)ethyl]phenyl 4-methylbenzenesulfonate
The title compound, C37H52O4S, was obtained by the reaction of 6,6′-(ethane-1,1-diyl)bis(2,4-di-tert-butylphenol) and 4-methylbenzene-1-sulfonyl chloride. The molecular conformation is stabilized by an intramolecular O—H⋯O hydrogen bond. Two of the tert-butyl groups are disordered over two sets of sites with occupancies 0.530 (15)/0.470 (15) and 0.615 (11)/0.385 (11)
(E)-N-[2-(3,5-Di-tert-butyl-2-hydroxybenzylideneamino)cyclohexyl]-4-methylbenzenesulfonamide
In the crystal structure of the title compound, C28H40N2O3S, there are two molecules per asymmetric unit; in each of these molecules, the cyclohexyl rings adopt chair conformations. The dihedral angles between the benzene rings are 16.89 (9) and 34.11 (9)°. Each molecule contains an intramolecular O—H⋯N hydrogen bond, and intermolecular N—H⋯O hydrogen bonds are also present. In both molecules, the methyl groups of one tert-butyl group are disordered over two positions; the site-occupancy factors in both cases are ca 0.6 and 0.4
AaABF3, an Abscisic Acid–Responsive Transcription Factor, Positively Regulates Artemisinin Biosynthesis in Artemisia annua
Artemisinin is well known for its irreplaceable curative effect on the devastating parasitic disease, Malaria. This sesquiterpenoid is specifically produced in Chinese traditional herbal plant Artemisia annua. Earlier studies have shown that phytohormone abscisic acid (ABA) plays an important role in increasing the artemisinin content, but how ABA regulates artemisinin biosynthesis is still poorly understood. In this study, we identified that AaABF3 encoded an ABRE (ABA-responsive elements) binding factor. qRT-PCR analysis showed that AaABF3 was induced by ABA and expressed much higher in trichomes where artemisinin is synthesized and accumulated. To further investigate the mechanism of AaABF3 regulating the artemisinin biosynthesis, we carried out dual-luciferase analysis, yeast one-hybrid assay and electrophoretic mobility shift assay. The results revealed that AaABF3 could directly bind to the promoter of ALDH1 gene, which is a key gene in artemisinin biosynthesis, and activate the expression of ALDH1. Functional analysis revealed that overexpression of AaABF3 in A. annua enhanced the production of artemisinin, while RNA interference of AaABF3 resulted in decreased artemisinin content. Taken together, our results demonstrated that AaABF3 played an important role in ABA-regulated artemisinin biosynthesis through direct regulation of artemisinin biosynthesis gene, ALDH1
Generation of integration-free neural progenitor cells from cells in human urine
Human neural stem cells hold great promise for research and therapy in neural disease. We describe the generation of integration-free and expandable human neural progenitor cells (NPCs). We combined an episomal system to deliver reprogramming factors with a chemically defined culture medium to reprogram epithelial-like cells from human urine into NPCs (hUiNPCs). These transgene-free hUiNPCs can self-renew and can differentiate into multiple functional neuronal subtypes and glial cells in vitro. Although functional in vivo analysis is still needed, we report that the cells survive and differentiate upon transplant into newborn rat brain.postprin
Neural progenitor cells from human induced pluripotent stem cells generated less autogenous immune response
Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks
Drug detection and identification technology are of great significance in drug supervision and management. To determine the exact source of drugs, it is often necessary to directly identify multiple varieties of drugs produced by multiple manufacturers. Near-infrared spectroscopy (NIR) combined with chemometrics is generally used in these cases. However, existing NIR classification modeling methods have great limitations in dealing with a large number of categories and spectra, especially under the premise of insufficient samples, unbalanced samples, and sensitive identification error cost. Therefore, this paper proposes a NIR multi-classification modeling method based on a modified Bidirectional Generative Adversarial Networks (Bi-GAN). It makes full utilization of the powerful feature extraction ability and good sample generation quality of Bi-GAN and uses the generated samples with obvious features, an equal number between classes, and a sufficient number within classes to replace the unbalanced and insufficient real samples in the courses of spectral classification. 1721 samples of four kinds of drugs produced by 29 manufacturers were used as experimental materials, and the results demonstrate that this method is superior to other comparative methods in drug NIR classification scenarios, and the optimal accuracy rate is even more than 99% under ideal conditions
Identification of Multi-Class Drugs Based on Near Infrared Spectroscopy and Bidirectional Generative Adversarial Networks
Drug detection and identification technology are of great significance in drug supervision and management. To determine the exact source of drugs, it is often necessary to directly identify multiple varieties of drugs produced by multiple manufacturers. Near-infrared spectroscopy (NIR) combined with chemometrics is generally used in these cases. However, existing NIR classification modeling methods have great limitations in dealing with a large number of categories and spectra, especially under the premise of insufficient samples, unbalanced samples, and sensitive identification error cost. Therefore, this paper proposes a NIR multi-classification modeling method based on a modified Bidirectional Generative Adversarial Networks (Bi-GAN). It makes full utilization of the powerful feature extraction ability and good sample generation quality of Bi-GAN and uses the generated samples with obvious features, an equal number between classes, and a sufficient number within classes to replace the unbalanced and insufficient real samples in the courses of spectral classification. 1721 samples of four kinds of drugs produced by 29 manufacturers were used as experimental materials, and the results demonstrate that this method is superior to other comparative methods in drug NIR classification scenarios, and the optimal accuracy rate is even more than 99% under ideal conditions
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