Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning

Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging. We propose a general approach that precisely estimates the Quality-of-Result (QoR), such as delay and area, of unseen synthesis flows for specific designs. The main idea is training a Recurrent Neural Network (RNN) regressor, where the flows are inputs and QoRs are ground truth. The RNN regressor is constructed with Long Short-Term Memory (LSTM) and fully-connected layers. This approach is demonstrated with 1.2 million data points collected using 14nm, 7nm regular-voltage (RVT), and 7nm low-voltage (LVT) FinFET technologies with twelve IC designs. The accuracy of predicting the QoRs (delay and area) within one technology is $\boldsymbol{\geq}$\textbf{98.0}\% over $\sim$240,000 test points. To enable accurate predictions cross different technologies and different IC designs, we propose a transfer-learning approach that utilizes the model pre-trained with 14nm datasets. Our transfer learning approach obtains estimation accuracy $\geq$96.3\% over $\sim$960,000 test points, using only 100 data points for training

Excess PLAC8 promotes an unconventional ERK2-dependent EMT in colon cancer

The epithelial-to-mesenchymal transition (EMT) transcriptional program is characterized by repression of E-cadherin (CDH1) and induction of N-cadherin (CDH2), and mesenchymal genes like vimentin (VIM). Placenta-specific 8 (PLAC8) has been implicated in colon cancer; however, how PLAC8 contributes to disease is unknown, and endogenous PLAC8 protein has not been studied. We analyzed zebrafish and human tissues and found that endogenous PLAC8 localizes to the apical domain of differentiated intestinal epithelium. Colon cancer cells with elevated PLAC8 levels exhibited EMT features, including increased expression of VIM and zinc finger E-box binding homeobox 1 (ZEB1), aberrant cell motility, and increased invasiveness. In contrast to classical EMT, PLAC8 overexpression reduced cell surface CDH1 and upregulated P-cadherin (CDH3) without affecting CDH2 expression. PLAC8-induced EMT was linked to increased phosphorylated ERK2 (p-ERK2), and ERK2 knockdown restored cell surface CDH1 and suppressed CDH3, VIM, and ZEB1 upregulation. In vitro, PLAC8 directly bound and inactivated the ERK2 phosphatase DUSP6, thereby increasing p-ERK2. In a murine xenograft model, knockdown of endogenous PLAC8 in colon cancer cells resulted in smaller tumors, reduced local invasion, and decreased p-ERK2. Using MultiOmyx, a multiplex immunofluorescence-based methodology, we observed coexpression of cytosolic PLAC8, CDH3, and VIM at the leading edge of a human colorectal tumor, supporting a role for PLAC8 in cancer invasion in vivo

Suppression of Phospholipase Dγs Confers Increased Aluminum Resistance in Arabidopsis thaliana

Aluminum (Al) toxicity is the major stress in acidic soil that comprises about 50% of the world's arable land. The complex molecular mechanisms of Al toxicity have yet to be fully determined. As a barrier to Al entrance, plant cell membranes play essential roles in plant interaction with Al, and lipid composition and membrane integrity change significantly under Al stress. Here, we show that phospholipase Dγs (PLDγs) are induced by Al stress and contribute to Al-induced membrane lipid alterations. RNAi suppression of PLDγ resulted in a decrease in both PLDγ1 and PLDγ2 expression and an increase in Al resistance. Genetic disruption of PLDγ1 also led to an increased tolerance to Al while knockout of PLDγ2 did not. Both RNAi-suppressed and pldγ1-1 mutants displayed better root growth than wild-type under Al stress conditions, and PLDγ1-deficient plants had less accumulation of callose, less oxidative damage, and less lipid peroxidation compared to wild-type plants. Most phospholipids and glycolipids were altered in response to Al treatment of wild-type plants, whereas fewer changes in lipids occurred in response to Al stress in PLDγ mutant lines. Our results suggest that PLDγs play a role in membrane lipid modulation under Al stress and that high activities of PLDγs negatively modulate plant tolerance to Al

Data files associated with the tomographic velocity model in the Alaska subduction zone

<p>For P- and S-wave arrival time data, it includes three files:</p><p>1) AK-stations: all of the stations used in the tomography</p><p>latitude, longitude, elevation(km), network name</p><p>2)Alaska-Data-P and Alaska-Data-S: seismic P and S wave arrival times</p><p>Event time, latitude, longitude, depth, total arrival times for per event</p><p> station, arrival time, phase</p><p>For P- and S- wave models, it includes two files:</p><p>1)Alaska-velocitymodel_Vp and Alaska-velocitymodel_Vs<br>   depth, latitude, longitude, velocity perturbation(%) </p&gt

Mudslide susceptibility assessment based on a two-channel residual network

AbstractIn response to the challenges posed by rugged terrain in Yunnan, hindering large-scale mudslide screening efforts, this article introduces a dual-channel Convolutional Neural Network (CNN) constructed using elevation data from historical mudslide-prone valleys (Digital Elevation Model, DEM) and remote sensing imagery. The network is designed to facilitate the comprehensive assessment of potential mudslide hazards in gullies, serving as a crucial tool for early mudslide disaster warning. The model initially employs an enhanced residual structure to extract fundamental features from both types of data. Subsequently, it leverages the SE module and deep separable structure to emphasize the importance of relevant features and expedite model convergence. Finally, the model classifies the gullies under evaluation based on their similarity to gullies where mudslides have previously occurred. Experimental results demonstrate the model’s robust performance in assessing mudflow-prone gullies, achieving an impressive precision rate of up to 81.10% and a recall rate of 82.76%. When applied to evaluate the potential hazard of mudslide gullies across the entirety of Nujiang Prefecture, the model predicts that 87.80% of the mudslide locations are at an extremely high risk. These findings underscore the viability of utilizing image-based gully feature analysis for assessing the hazard levels of mudslide-prone gullies