17,090 research outputs found
Comparative Study Of Methyl-Tert-Butyl Ether Extractives From RYE And Rice Straw
The chemical composition of lipophilic extractives from rye and rice straws has been comparatively examined. Free fatty acids (19.04-22.95%), sterols (12.54-14.60%), waxes (9.53-27.14%), steryl esters (16.02-18.19%), and triglycerides (5.72-11.38%) were identified as the five major classes of lipids in the two straw extractives. Minor components of diglycerides (0.23-0.42%) and resin acids (0.05-0.12%) were also verified from the two straw lipophilic extracts. Of the individual compounds in each group, fifteen free fatty acids, four sterols, three waxes, five steryl esters, and three triglycerides were quantitatively determined. The most abundant saturated free fatty acids were palmitic acid (C16:0, 3.96-4.24%) and tetradecanoic acid (C14:0, 2.95-3.62%), whereas linoleic (C18:2) and/or oleic (C18: 1) acids (1.87-2.09%) were the most dominant unsaturated free fatty acids. β-Sitosterol was identified as a predominant component, accounting for 83.89% of the total sterols in rye straw extract and 94.45% in rice straw extractives. Palmitic acid palmityl ester was verified as a dominant component in a group of waxes, accounting for approximately 70% of the waxes analyzed in the two extracts. The steryl esters analyzed were composed mainly of steryl laurate (0.29-0.95%), steryl myristate (3.20-3.56%), steryl palmitate (1.86-2.28%), steryl margarate (2.20-2.93%), and steryl oleate (2.13%). Of the triglycerides verified, glyceryl tripalmitate (0.23-1.64%), 1,2-dipalmitoyl-3-oleoylrac-glycerol (1.06-2.08%), and triolein (cis-9) (0.77-1.61%) were identified in this group
Clinicopathological significance of stromal variables: angiogenesis, lymphangiogenesis, inflammatory infiltration, MMP and PINCH in colorectal carcinomas
Cancer research has mainly focused on alterations of genes and proteins in cancer cells themselves that result in either gain-of-function in oncogenes or loss-of-function in tumour-suppressor genes. However, stromal variables within or around tumours, including blood and lymph vessels, stromal cells and various proteins, have also important impacts on tumour development and progression. It has been shown that disruption of stromal-epithelial interactions influences cellular proliferation, differentiation, death, motility, genomic integrity, angiogenesis, and other phenotypes in various tissues. Moreover, stromal variables are also critical to therapy in cancer patients. In this review, we mainly focus on the clinicopathological significance of stromal variables including angiogenesis, lymphangiogenesis, inflammatory infiltration, matrix metalloproteinase (MMP), and the particularly interesting new cysteine-histidine rich protein (PINCH) in colorectal cancer (CRC)
Study of the weak annihilation contributions in charmless decays
In this paper, in order to probe the spectator-scattering and weak
annihilation contributions in charmless (where stands for a
light vector meson) decays, we perform the -analyses for the end-point
parameters within the QCD factorization framework, under the constraints from
the measured , , and
decays. The fitted results indicate that the end-point
parameters in the factorizable and nonfactorizable annihilation topologies are
non-universal, which is also favored by the charmless and (where
stands for a light pseudo-scalar meson) decays observed in the previous
work. Moreover, the abnormal polarization fractions measured by the LHCb
collaboration can be reconciled through the weak annihilation corrections.
However, the branching ratio of decay exhibits a
tension between the data and theoretical result, which dominates the
contributions to in the fits. Using the fitted end-point
parameters, we update the theoretical results for the charmless
decays, which will be further tested by the LHCb and Belle-II experiments in
the near future.Comment: 31 pages, 4 figures, 6 table
Restricted Generative Projection for One-Class Classification and Anomaly Detection
We present a simple framework for one-class classification and anomaly
detection. The core idea is to learn a mapping to transform the unknown
distribution of training (normal) data to a known target distribution.
Crucially, the target distribution should be sufficiently simple, compact, and
informative. The simplicity is to ensure that we can sample from the
distribution easily, the compactness is to ensure that the decision boundary
between normal data and abnormal data is clear and reliable, and the
informativeness is to ensure that the transformed data preserve the important
information of the original data. Therefore, we propose to use truncated
Gaussian, uniform in hypersphere, uniform on hypersphere, or uniform between
hyperspheres, as the target distribution. We then minimize the distance between
the transformed data distribution and the target distribution while keeping the
reconstruction error for the original data small enough. Comparative studies on
multiple benchmark datasets verify the effectiveness of our methods in
comparison to baselines
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