251,425 research outputs found
Dr. Lin Sun, CAU, March 2013
No full textThis video is a conversation with Dr. Lin Sun. Dr. Sun talks about an exhibit at the Woodruff Library titled "At The Boundary." Jordan Moore, AUC Woodruff Library, is the interviewer
An Analysis of <i>Judge Lin</i>
No full textBiography of Lin Wen Zhong Gong has another way to call, that is Judge Lin. The leading character is Lin Ze-Xu. This book is based on functionary experience of Lin Ze-Xu, with the captivating plots of court case, helping by highly skilled military attach\uc3\ua9s and chivalrous knights, and the history facts of Opium War. It makes Lin Ze-Xu\ue2s Confucian temperament and tragic mood more, also contrasts with author\ue2s sorrow and furiousness for the politics at the time. History, court case, martial arts\ue2\ua6\ue2\ua6etc. are essence of this book and it broadens the way of this writing style.
The topic of the thesis is \ue2An Analysis of Judge Lin\ue2. The following thesis will be divided into six different chapters. The introduction is Chapter one of the thesis, which is including researching motive and purpose, literature review of predecessors, researching version by existing information, raising questions, choosing research methods and arranging chapters. In chapter Two, I discuss the study of characters of Lin Ze-Xu, also makes a deep analysis of author\u27s purpose of writing him. In chapter Three, I analyze supporting actors and actress. Meanwhile, I illustrate author\u27s purpose of writing supporting actress because the author had different manner to describe supporting actress. Moving to the Chapter Four, I mainly focus on the plots of Judge Lin, and organize cases of Lin Ze-Xu and his subordinates to understand features of cases. In Chapter Five, I represent the causes of Opium War. China and England had difference of opinions of opium. Therefore, it is easier to comprehend what the author\u27s purpose is. In the last chapter I summarize the main points of the preceding chapters and confirm particularity of Judge Lin
LIN-39 does not regulate TRN fate markers in AVM.
No full text(A) AVM was posteriorly displaced in lin-39(n1760) mutants, but the expression of TRN fate marker uIs115[mec-17p::TagRFP] was not affected in lin-39 mutants. (B) The displaced AVM also expressed the TRN fate marker zdIs5[mec-4p::GFP] in lin-39 mutants. Scale Bars = 100 μm. (TIF)</p
LIN-39 motif in the promoters of neuronal fate markers.
No full text(A) The sequences of functional Hox sites identified in this study (Fig 3), which likely mediate the regulation of neuronal fate markers by LIN-39. (B) The sequence logo of LIN-39 binding sites generated from ChIP-seq data. These logos were downloaded from http://cisbp.ccbr.utoronto.ca/. (TIF)</p
Spatial Chow-Lin Methods for Data Completion in Econometric Flow Models
Get PDFFlow data across regions can be modeled by spatial econometric models, see LeSage and Pace (2009). Recently, regional studies became interested in the aggregation and disaggregation of flow models, because trade data cannot be obtained at a disaggregated level but data are published on an aggregate level. Furthermore, missing data in disaggregated flow models occur quite often since detailed measurements are often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete flow data. The Chow and Lin (1971) method was developed for completing disaggregated incomplete time series data. We will extend this method in a general framework to spatially correlated flow data using the cross-sectional Chow-Lin method of Polasek et al. (2009). The missing disaggregated data can be obtained either by feasible GLS prediction or by a Bayesian (posterior) predictive density.Missing values in spatial econometrics, MCMC, non-spatial Chow-Lin (CL) and spatial Chow-Lin (SCL) methods, spatial internal flow (SIF) models, origin and destination (OD) data
LIN-1 sumoylation is required for ventral toroid contraction.
No full text(A) Wild-type and K10A, K169A mutant LIN-1::GFP expression in L3 larvae at the Pn.px stage after VPC-specific degradation of AID::SMO-1 from the L2 stage onward. The 1° and 2° VPC descendants are underlined in white. The left panels show the corresponding DIC images overlaid with the LIN-1::GFP signal in green. (B) Quantification of LIN-1::GFP expression levels in 1° and 2° VPC descendants at the Pn.px stage in LIN-1::GFP wild-type and K10A, K169A double mutants under the indicated conditions. See S3 Fig for the corresponding measurements at the Pn.pxx stage. (C) Toroid morphogenesis defects in LIN-1 K10A and K169A single and double mutants at the L4 stage. Left panels show lateral views of z-projections. vulA and vulB1 toroids are outlined by the white rectangle in the top left panel and shown in top (xz) views in the right panels. (D) Quantification of vulA contraction, calculated as the ratio of the vulA and vulB1 toroid diameter. The box plots show the median values with the 25th and 75th percentiles and the whiskers indicate the maximum and minimum values. Where indicated, untreated controls are labelled with–IAA (blue) and animals treated with 1 mM auxin with +IAA (red). In each graph, the numbers of animals scored are indicated by the numbers in brackets. Statistical significance in (B) and (D) was calculated with unpaired two-tailed t-tests. p-values are indicated as * p ≤ 0.05; ** p ≤ 0.01; *** p ≤ 0.001. The scale bars are 10 μm.</p
Mao ne dormait pas, de Lin Shen
No full textNaour Françoise, Shen Lin. Mao ne dormait pas, de Lin Shen. In: Perspectives chinoises, n°39, 1997. p. 68
Xiaohe-Lin/Heritable_variation: v1.0.0
No full text<h1>This repository contains data and codes related to</h1>
<p>Lin et al. 2024. Environment-induced heritable variation is common.</p>
<blockquote>
<p>The specific content includes</p>
</blockquote>
<ul>
<li><p>Phenotype analysis</p>
<ul>
<li><p>Statistics assessing the effect of genotypes and ancestral environments using linear mixed effect model;</p>
</li>
<li><p>Calculation of genotype-specific effect sizes;</p>
</li>
<li><p>Estimating factors impacting occurrence of transgenerational effects using generalized linear mixed-effect models.</p>
</li>
</ul>
</li>
<li><p>Transcriptom analysis : Data processing and extraction of expression matrix</p>
</li>
<li><p>Methylome analysis : Data processing and extraction of methylation matrix</p>
</li>
</ul>
<h1>Contacts</h1>
<p>Please direct comments to the issues page or [email protected]</p>
<p><strong>Full Changelog</strong>: https://github.com/Xiaohe-Lin/Heritable_variation/commits/v1.0.0</p>
yc-lin-geo/Georgia_GIA: GEORGIA: a Graph neural network based EmulatOR for Glacial Isostatic Adjustment
No full text<p>This repository contains the Python code base for Lin et al., : "GEORGIA: a Graph neural network based EmulatOR for Glacial Isostatic Adjustment".</p>
<p><strong>Project abstract:</strong></p>
<blockquote>
<p>Glacial isostatic adjustment (GIA) modelling is not only useful for understanding past relative sea-level change but also for projecting future sea-level change due to ongoing land deformation. However, GIA model predictions are subject to ranges of uncertainties, including poorly-constrained global ice history. An effective way to reduce this uncertainty is to perform data-model comparisons over a large ensemble of possible ice histories, which is often prohibited by the limited computation resources. Here we address this problem by building a statistical GIA emulator that can mimic the behaviour of a physics-based GIA model (assuming a single 1-D Earth rheology) while being computationally cheap to evaluate. Based on deep learning algorithms, our emulator shows 0.54 m mean absolute error on 150 out-of-sample testing data with <0.5 seconds emulation time. Using this emulator, two illustrative applications related to calculate barystatic sea level are provided for use by the sea-level community.</p>
</blockquote>
- …
