FMNL1 promotes growth and metastasis of breast cancer by inhibiting BRCA1 via upregulation of HMGA1

In the earlier published article, “Herbei Province” included in the affiliation of the second author is incorrect. “Chongqing” is a municipality directly under the Central Government and does not belong to "Hebei Province”. At the request of the author, the correct affiliation is provided above. New citation: Zhang Q, Yang H, Tang C, Wang Q, Ren L, Jia C, et al. FMNL1 promotes growth and metastasis of breast cancer by inhibiting BRCA1 via upregulation of HMGA1. Trop J Pharm Res 2021; 20(8):1559-1564 doi: 10.4314/tjpr.v20i8.2. Erratum: 2022; 21(8): 1807 doi: 10.4314/ tjpr.v 21i8.31 Earlier citation: Zhang Q, Yang H, Tang C, Wang Q, Ren L, Jia C, et al. FMNL1 promotes growth and metastasis of breast cancer by inhibiting BRCA1 via upregulation of HMGA1. Trop J Pharm Res 2021; 20(8):1559-1564 doi: 10.4314/tjpr.v20i8.

Construction and optical-electrical properties of inorganic/organic heterojunction nanostructures

We have designed and synthesized a series of ordered inorganic/organic hybrid aggregate nanostructures of by self-assembly and self-organizing technique. The process and mechanism of growing hybrid aggregate nanostructures have been studied. The ability to tune the size and morphologies of hybrid aggregate nanostructures has been achieved by controlling reaction conditions. The effects of morphologies and size dependent on electrical and optical properties have been demonstrated. These semiconductor molecular hybrid aggregate nanostructures exhibit interesting electrical, optical, and optoelectronic properties for use in next-generation electronic and optoelectronic devices. REFERENCES [1] Liu, H. B.; Zuo, Z. C.; Guo, Y. B.; Li, Y. J.; Li, Y. L. Angew. Chem. Int. Ed. 2010, 49, 2705. [2] Huang, C. S.; Li, Y. L.; Song, Y. L.; Li, Y. J.; Liu, H. B.; Zhu, D. B. Adv. Mater. 2010, 22, 3532. [3] Wang, K.; Yang, H.; Qian, X. M.; Xue, Z.; Li, Y. J.; Liu, H. B.; Li, Y. L. Dalton Trans. 2014, 43, 11542. [4] Liu, H. B.; Wang, K.; Zhang, L.; Qian, X. M.; Y. J.; Li, Y. L. Dalton Trans. 2014, 43, 432. [5] Guo, Y. B.; Xu, L.; Liu, H. B.; Li, Y. J.; Che, C.-M.; Li, Y. L. Adv. Mater. 2015, 27, 985

External validation of a mammographic texture marker for breast cancer risk in a case–control study

Purpose: The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic texture feature developed from a previous study in a new setting. Approach: A case–control study (316 invasive cases and 1339 controls) of women in Virginia, USA was used to validate a mammographic texture feature (MMTEXT) derived in a independent previous study. Analysis of predictive ability was adjusted for age, demographic factors, questionnaire risk factors (combined through the Tyrer-Cuzick model), and optionally BI-RADS breast density. Odds ratios per interquartile range (IQ-OR) in controls were estimated. Subgroup analysis assessed heterogeneity by mode of cancer detection (94 not detected by mammography). Results: MMTEXT was not a significant risk factor at 0.05 level after adjusting for classical risk factors (IQ-OR  =  1.16, 95%CI 0.92 to 1.46), nor after further adjustment for BI-RADS density (IQ-OR  =  0.92, 95%CI 0.76 to 1.10). There was weak evidence that MMTEXT was more predictive for cancers that were not detected by mammography (unadjusted for density: IQ-OR  =  1.46, 95%CI 0.99 to 2.15 versus 1.03, 95%CI 0.79 to 1.35, Phet 0.10; adjusted for density: IQ-OR  =  1.11, 95%CI 0.70 to 1.77 versus 0.76, 95%CI 0.55 to 1.05, Phet 0.21). Conclusions: MMTEXT is unlikely to be a useful imaging marker for invasive breast cancer risk assessment in women attending mammography screening. Future studies may benefit from a larger sample size to confirm this as well as developing and validating other measures of risk. This negative finding demonstrates the importance of external validation

Holistic, Instance-Level Human Parsing

Object parsing -- the task of decomposing an object into its semantic parts -- has traditionally been formulated as a category-level segmentation problem. Consequently, when there are multiple objects in an image, current methods cannot count the number of objects in the scene, nor can they determine which part belongs to which object. We address this problem by segmenting the parts of objects at an instance-level, such that each pixel in the image is assigned a part label, as well as the identity of the object it belongs to. Moreover, we show how this approach benefits us in obtaining segmentations at coarser granularities as well. Our proposed network is trained end-to-end given detections, and begins with a category-level segmentation module. Thereafter, a differentiable Conditional Random Field, defined over a variable number of instances for every input image, reasons about the identity of each part by associating it with a human detection. In contrast to other approaches, our method can handle the varying number of people in each image and our holistic network produces state-of-the-art results in instance-level part and human segmentation, together with competitive results in category-level part segmentation, all achieved by a single forward-pass through our neural network.Comment: Poster at BMVC 201

Wang Li (1900-1986)

Wang Li (Wang Liaoyi) was one of the three most prominent linguists in China in the 20th century. He was born August 10, 1900, in what is now Bobai County of the Guangxi Zhuang Autonomous Area

Zero curvature representation for a new fifth-order integrable system

In this brief note we present a zero-curvature representation for one of the new integrable system found by Mikhailov, Novikov and Wang in nlin.SI/0601046.Comment: 2 pages, LaTeX 2e, no figure