We develop a flat, analytic and nonlinear placement algorithm ePlace, which is more effective, generalized, simpler and faster than previous works. Based on the analogy between placement instance and electrostatic system, we develop a novel placement density function eDensity, which models every object as positive charge and the density cost as the potential energy of the electrostatic system. The electric potential and field distribution are coupled with density using a modified Poisson's equation, which is numerically solved by spectral methods using fast Fourier transform (FFT). Rather than conjugate gradient (CG) method by previous placers, we propose to use Nesterov's method for faster convergence. The efficiency bottleneck on line search is resolved by steplength prediction through an equation of Lipschitz constant. Through empirical validation, ePlace outperforms all prior placers with better quality and efficiency. On average of ISPD 2005 benchmarks, ePlace outperforms the leading placer BonnPlace with 2.83% shorter wirelength and runs 3.05x faster. On average of ISPD 2006 benchmarks, ePlace outperforms the leading placer MAPLE with 4.59% shorter wirelength and runs 2.84x faster. Based on the above placement prototype, we develop ePlace-MS, an electrostatics based placement algorithm for mixed-size circuits. The density function eDensity is extended to handle the mixed-size placement. We conduct detailed analysis on the correctness of the gradient formulation and the numerical solution, as well as the rationale of density equalization with its advantages over prior density functions. Nesterov's method is shown with high yet stable performance over mixed-size circuits. The steplength prediction methodology is enhanced with backtracking strategy to prevent overestimation. A nonlinear preconditioner is developed to minimize the topological and physical differences between large macros and standard cells. Besides, we devise a simulated annealer for direct macro-layout legalization. All the above innovations are integrated into our mixed-size placement prototype ePlace-MS, which outperforms all the related works in literature with better quality and efficiency. Compared to the leading-edge mixed-size placer NTUplace3, ePlace-MS produces up to 22.98% and on average 8.22% shorter wirelength over all the sixteen modern mixed -size (MMS) benchmark circuits with the same runtime

Background

Understanding how landscape factors, including suites of geographic and environmental variables, and both historical and contemporary ecological and evolutionary processes shape the distribution of genetic diversity is a primary goal of landscape and conservation genetics and may be particularly consequential for species involved in ecological restoration. In this study, we examine the factors that shape the distribution of genetic variation in a leguminous shrub (Caragana microphylla) important for restoration efforts on the Mongolian Plateau in China. This region houses several major bioclimatic gradients, and C. microphylla is an important restoration species because it stabilizes soils and prevents advancing desertification on the Inner Mongolia Plateau caused by ongoing climate change.

Results

We assembled an expansive genomic dataset, consisting of 22 microsatellite loci, four cpDNA regions, and 5788 genome-wide SNPs from ten populations of C. microphylla. We then applied ecological niche modelling and linear and non-linear regression techniques to investigate the historical and contemporary forces that explain patterns of genetic diversity and population structure in C. microphylla on the Inner Mongolia Plateau. We found strong evidence that both geographic and environmental heterogeneity contribute to genetic differentiation and that the spatial distribution of genetic diversity in C. microphylla appears to result partly from the presence of a glacial refugium at the southwestern edge of its current range.

Conclusions

These results suggest that geographic, environmental, and historical factors have all contributed to spatial genetic variation in this ecologically important species. These results should guide restoration plans to sustain genetic diversity during plant translocations.

Inflammatory myofibroblastic tumors (IMTs) are characterized by myofibroblast proliferation and an inflammatory cell infiltrate. Little is known about the molecular pathways that precipitate IMT formation. Here, we report the identification of somatic mutations in UPF1, a gene that encodes an essential component of the nonsense-mediated RNA decay (NMD) pathway, in 13 of 15 pulmonary IMT samples. The majority of mutations occurred in a specific region of UPF1 and triggered UPF1 alternative splicing. Several mRNA targets of the NMD pathway were upregulated in IMT samples, indicating that the UPF1 mutations led to reduced NMD magnitude. These upregulated NMD targets included NIK mRNA, which encodes a potent activator of NF-κB. In human lung cells, UPF1 depletion increased expression of chemokine-encoding genes in a NIK-dependent manner. Elevated chemokines and IgE class switching events were observed in IMT samples, consistent with NIK upregulation in these tumors. Together, these results support a model in which UPF1 mutations downregulate NMD, leading to NIK-dependent NF-κB induction, which contributes to the immune infiltration that is characteristic of IMTs. The molecular link between the NMD pathway and IMTs has implications for the diagnosis and treatment of these tumors.