424,427 research outputs found
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
Full text linkFlow 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>
LIN-2 and FRM-3 are required to maintain locomotory behaviour.
No full text(A) Genomic structure of lin-2 and frm-3 locus, mutant allele characterization, and protein architecture for LIN-2 and FRM-3. The whole promoter region of lin-2a and part of the kinase domain of LIN-2A is deleted in the e1309 mutants. The FERM domain in both frm-3a and frm-3b is deleted in the gk585 mutants. Syb1019 and syb1036 are stop codons in lin-2 and frm-3 that inactivate the expression of lin-2a and lin-2b, and frm-3a and frm-3b. (E, F) Locomotion speed is reduced by the loss of LIN-2 and FRM-3. Representative trajectories of locomotion in wild type (E) and mean locomotion speed in wild type, lin-2(e1309), frm-3(gk585), lin-2(syb1019), frm-3(syb1036), and lin-2(e1309);frm-3(gk585) mutants. To measure locomotion speed, young adult animals were washed with a drop of PBS and then transferred to fresh NGM plates with no bacterial lawn (30 worms per plate). Worm movement recordings (under room temperature 22°C) were started 10 min after the worms were transferred. A 2 min digital video of each plate was captured at 3.75 Hz frame rate by WormLab System (MBF Bioscience). Average speed and tracks were generated for each animal using WormLab software. To confirm the repeatability of the data, the locomotion speed was measured in two independent experiments in two days. For each mutant, around 10–40 animals were analyzed in one experiment. Significance was tested for each experiment. Data are mean ± SEM (**, p p < 0.001 when compared to wild type; n.s., non-significant; one-way ANOVA). The number of worms analyzed for each genotype is indicated in the bar.</p
LIN-39 promotes neuronal fate specification in the Q and V5 lineage.
No full text(A) The expression of lin-39 in AVM, SDQL/R, PDEL/R, and PVDL/R, indicated by the overlapping with neurotransmitter identity markers and specific fate markers (uIs115[mec-17p::TagRFP] for AVM, otIs181[dat-1p::mCh] for PDE, uIs117[lad-2p::mCh] for SDQ). (B) The expression of mab-5 in SDQL. (C) Summary of lin-39 (green) and mab-5 (cyan) expression in the descendants of Q and V5 lineages. (D) The loss of gcy-37 expression in AQR and AVM neurons in lin-39(n1760) mutants and the mispositioning of PQR in mab-5(gk670) mutants; the loss of lad-2 expression in SDQR in lin-39(n1760) mutants, the displacement of SDQL in mab-5(gk670) mutants, and the loss of lad-2 expression in both SDQs in lin-39(n1760) mab-5(e1239) mutants. The right panels show the penetrance for the loss of marker expression and cell body mispositioning. Mean ± SD for the percentage of cells showing corresponding phenotypes from three biological replicates are shown. Double asterisks indicate statistically significant difference (p Chi-square test. (E) The loss of ser-2 expression in PVD and PDE neurons and the loss of F49H12.4 expression in PVD in lin-39(n1760) and ceh-20(u843) mutants. (F) Dopaminergic marker dat-1 is normally expressed in PDE neurons in lin-39 mutants, but PDE shows axonal growth defects. The arrows indicate the termini of PDE axons. The expression of glutamatergic identity marker eat-4 and the PVD terminal selector gene mec-3 in PVD neurons in lin-39 mutants.</p
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