PCR-based generation of shRNA libraries from cDNAs

BACKGROUND: The use of small interfering RNAs (siRNAs) to silence target gene expression has greatly facilitated mammalian genetic analysis by generating loss-of-function mutants. In recent years, high-throughput, genome-wide screening of siRNA libraries has emerged as a viable approach. Two different methods have been used to generate short hairpin RNA (shRNA) libraries; one is to use chemically synthesized oligonucleotides, and the other is to convert complementary DNAs (cDNAs) into shRNA cassettes enzymatically. The high cost of chemical synthesis and the low efficiency of the enzymatic approach have hampered the widespread use of screening with shRNA libraries. RESULTS: We report here an improved method for constructing genome-wide shRNA libraries enzymatically. The method includes steps of cDNA fragmentation and endonuclease MmeI digestion to generate 19-bp fragments, capping the 19-bp cDNA fragments with a hairpin oligonucleotide, and amplification of the hairpin structures by PCR. The PCR step converts hairpins into double-stranded DNAs that contain head-to-head cDNA fragments that can be cloned into a vector downstream of a Pol III promoter. CONCLUSION: This method can readily be used to generate shRNA libraries from a small amount of mRNA and thus can be used to create cell- or tissue-specific libraries

On Exploring Node-feature and Graph-structure Diversities for Node Drop Graph Pooling

A pooling operation is essential for effective graph-level representation learning, where the node drop pooling has become one mainstream graph pooling technology. However, current node drop pooling methods usually keep the top-k nodes according to their significance scores, which ignore the graph diversity in terms of the node features and the graph structures, thus resulting in suboptimal graph-level representations. To address the aforementioned issue, we propose a novel plug-and-play score scheme and refer to it as MID, which consists of a \textbf{M}ultidimensional score space with two operations, \textit{i.e.}, fl\textbf{I}pscore and \textbf{D}ropscore. Specifically, the multidimensional score space depicts the significance of nodes through multiple criteria; the flipscore encourages the maintenance of dissimilar node features; and the dropscore forces the model to notice diverse graph structures instead of being stuck in significant local structures. To evaluate the effectiveness of our proposed MID, we perform extensive experiments by applying it to a wide variety of recent node drop pooling methods, including TopKPool, SAGPool, GSAPool, and ASAP. Specifically, the proposed MID can efficiently and consistently achieve about 2.8\% average improvements over the above four methods on seventeen real-world graph classification datasets, including four social datasets (IMDB-BINARY, IMDB-MULTI, REDDIT-BINARY, and COLLAB), and thirteen biochemical datasets (D\&D, PROTEINS, NCI1, MUTAG, PTC-MR, NCI109, ENZYMES, MUTAGENICITY, FRANKENSTEIN, HIV, BBBP, TOXCAST, and TOX21). Code is available at~\url{https://github.com/whuchuang/mid}.Comment: 14 pages, 14 figure

The Indoor Thermal Environment Simulation and Testing Validation of a Power Plant Turbine Room in Extreme Cold Area

AbstractThis paper conducts an analysis study on indoor thermal environment of a steam turbine room in power plant by CFD. Refer to a typical steam turbine room in an actual thermal power plant which has been conducted field test, the typical numerical simulation model is built including a reasonable indoor heat conditions, structural parameters and envelope architectural opening, flow boundary conditions. Indoor air temperature distribution and air velocity distribution of steam turbine room is obtained. Comparing the simulation results with the corresponding field measurement data on typical location show that two sets of results are very close. So accuracy and applicability of CFD simulations is proved. It is also proved that complete method for CFD simulations of the paper is appropriate for interior thermal environment study of typical steam turbine room and thus laid the foundation for the further studies of a large number of universal cases

Learning Affinity from Attention: End-to-End Weakly-Supervised Semantic Segmentation with Transformers

Weakly-supervised semantic segmentation (WSSS) with image-level labels is an important and challenging task. Due to the high training efficiency, end-to-end solutions for WSSS have received increasing attention from the community. However, current methods are mainly based on convolutional neural networks and fail to explore the global information properly, thus usually resulting in incomplete object regions. In this paper, to address the aforementioned problem, we introduce Transformers, which naturally integrate global information, to generate more integral initial pseudo labels for end-to-end WSSS. Motivated by the inherent consistency between the self-attention in Transformers and the semantic affinity, we propose an Affinity from Attention (AFA) module to learn semantic affinity from the multi-head self-attention (MHSA) in Transformers. The learned affinity is then leveraged to refine the initial pseudo labels for segmentation. In addition, to efficiently derive reliable affinity labels for supervising AFA and ensure the local consistency of pseudo labels, we devise a Pixel-Adaptive Refinement module that incorporates low-level image appearance information to refine the pseudo labels. We perform extensive experiments and our method achieves 66.0% and 38.9% mIoU on the PASCAL VOC 2012 and MS COCO 2014 datasets, respectively, significantly outperforming recent end-to-end methods and several multi-stage competitors. Code is available at https://github.com/rulixiang/afa.Comment: Accepted to CVPR 202