Search

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ydbio.2008.11.001 Byline: Bum Jun Kim, Norio Takamoto, Jun Yan, Sophia Y. Tsai, Ming-Jer Tsai Keywords: COUP-TFII; IGF-1; Akt1; mTOR; Cerebellum; Foliation; GSK-3[beta] Abstract: COUP-TFII (also known as Nr2f2), a member of the nuclear orphan receptor superfamily, is expressed in several regions of the central nervous system (CNS), including the ventral thalamus, hypothalamus, midbrain, pons, and spinal cord. To address the function of COUP-TFII in the CNS, we generated conditional COUP-TFII knockout mice using a tissue-specific NSE-Cre recombinase. Ablation of COUP-TFII in the brain resulted in malformation of the lobule VI in the cerebellum and a decrease in differentiation of cerebellar neurons and cerebellar growth. The decrease in cerebellar growth in NSE.sup.Cre/+/CII.sup.F/F mice is due to reduced proliferation and increased apoptosis in granule cell precursors (GCPs). Additional studies demonstrated that insulin like growth factor 1 (IGF-1) expression was reduced in the cerebellum of NSE.sup.Cre/+/CII.sup.F/F mice, thereby leading to decreased Akt1 and GSK-3[beta] activities, and the reduced expression of mTOR. Using ChIP assays, we demonstrated that COUP-TFII was recruited to the promoter region of IGF-1 in a Sp1-dependent manner. In addition, dendritic branching of Purkinje cells was decreased in the mutant mice. Thus, our results indicate that COUP-TFII regulates growth and maturation of the mouse postnatal cerebellum through modulation of IGF-1 expression. Author Affiliation: Department of Molecular and Cellular Biology and Development Program, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA Article History: Received 24 June 2008; Revised 29 October 2008; Accepted 3 November 2008

Predicting electrical power consumption is essential in many industries. This paper presents a method to predict total electrical power consumption in a certain period of Hot Rolling Mill (HRM) in steel mills. We adequately pre-processed operation data, including work schedule, work result, and power measurement. In addition, based on domain knowledge, we derived several useful variables relevant to power consumption. To predict power consumption, we developed a Multi-Level Stacked Regression model that combines the strengths of multiple prediction models. Real-world applications have dataset shift characteristics in which the covariance between features or distribution of the features changes over time due to frequent changes in the working environment. To verify the generalization accuracy of our model, we trained and tested our model for various training/test splits and compared it with several comparative models. Our model showed the best accuracy in all training/test splits. We verified whether the predictive model reflects the real-world environment well by analyzing the actual physical meaning of the essential features used in the predictive model. • A series of pre-processing methods are applied to real data from Hot Rolling Mill. • A Multi-Level Stacked Regression algorithm is proposed. • Performance on various training/test splits that contains dataset shifts is verified. [ABSTRACT FROM AUTHOR]

• A weakly supervised power line detection algorithm is proposed. • A line specialized refinement algorithm is applied for performance improvement. • A repeated learning framework with noise label develops the model output. Detection of power lines in aerial images is an important problem to prevent accidents of unmanned aerial vehicles operating at low altitudes in the electrical industry. Recently, pixel-level power line detection using deep learning has been studied but production of the pixel-level annotations for massive dataset is difficult. In this study, we propose a power line detection algorithm using weakly supervised learning method to reduce the labeling cost for dataset generation. The algorithm is divided into two stages. First, an approximately localized mask was generated based on a convolutional neural network which was trained with only patch-level labels. Second, recursive training of segmentation network with refined broken line segments was executed. A refinement algorithm, line segment connecting (LSC) is a power-line-specialized refinement module that connects broken lines by approximating the segments as partially straight. In proposed algorithm, predicted image at each recursive step was updated as a label of the next training and the label was developed by itself with LSC. The comprehensive experimental results of our algorithm showed state-of-art F 1 -score of 94.3% in weakly supervised learning approaches on public dataset. This result suggests that the proposed algorithm is useful for low labeling cost with high performance in line detection application. [ABSTRACT FROM AUTHOR]

The two-dimensional (2D) inorganic material Nb 3 I 8 , which can be separated into atomic planes, was synthesized through a vapor transport reaction between niobium and iodine. High-purity and cm scale large Nb 3 I 8 crystals were obtained by controlling the growth temperature (600–700 °C) and the stoichiometric ratio between niobium and iodine (using excess I 2 condition, niobium: iodine = 3:9). The semiconductor characteristics were measured by the change of electrical conductivity with temperature (increased conductivity with temperature in the range of 100 K–300 K), and have a band gap of ∼1 eV (∼1200 nm) through Infrared (IR)-visible light absorption analysis. Based on these characteristics, the device characteristics of detecting IR near 1200 nm region were verified. Besides few-layers of Nb 3 I 8 was acquired by the mechanical exfoliation method, and experimentally observed by atomic force microscopy (AFM). This 2D material is expected to have an important role in developing another 2D materials and in new nano-optoelectronic devices using it. • Synthesis of 2D material Nb 3 I 8 in centimeter scale. • Experimental estimation of band gap of Nb 3 I 8 about (∼1 eV) and application to IR detectors. • Bulk to few-layers separation of Nb 3 I 8 sheet using mechanical exfoliation method. [ABSTRACT FROM AUTHOR]