Feature fusion network based on strip pooling

Contextual information is a key factor affecting semantic segmentation. Recently, many methods have tried to use the self-attention mechanism to capture more contextual information. However, these methods with self-attention mechanism need a huge computation. In order to solve this problem, a novel self-attention network, called FFANet, is designed to efficiently capture contextual information, which reduces the amount of calculation through strip pooling and linear layers. It proposes the feature fusion (FF) module to calculate the affinity matrix. The affinity matrix can capture the relationship between pixels. Then we multiply the affinity matrix with the feature map, which can selectively increase the weight of the region of interest. Extensive experiments on the public datasets (PASCAL VOC2012, CityScapes) and remote sensing dataset (DLRSD) have been conducted and achieved Mean Iou score 74.5%, 70.3%, and 63.9% respectively. Compared with the current typical algorithms, the proposed method has achieved excellent performance.

A new affinity matrix weighted k-nearest neighbors graph to improve spectral clustering accuracy

Researchers have thought about clustering approaches that incorporate traditional clustering methods and deep learning techniques. These approaches normally boost the performance of clustering. Getting knowledge from large data-sets is quite an interesting task. In this case, we use some dimensionality reduction and clustering techniques. Spectral clustering is gaining popularity recently because of its performance. Lately, numerous techniques have been introduced to boost spectral clustering performance. One of the most significant part of these techniques is to construct a similarity graph. We introduced weighted k-nearest neighbors technique for the construction of similarity graph. Using this new metric for the construction of affinity matrix, we achieved good results as we tested it both on real and artificial data-sets.

Method for detecting acetylated PD-L1 in cell lysates v1

Here we present a method that allows detection of acetylated PD-L1 and is applicable to a wide range of cell lines. The method captures >90% of acetylated PD-L1 species, is semi-quantitative and simple to perform in any lab equipped with tissue culture and western blot equipment. The method involves processing cells in a lysis buffer that has been optimized for efficient immunoprecipitation (IP) of acetylated species, an IP enrichment step utilizing an acetyl-lysine affinity matrix and western blot detection of both total and acetylated PD-L1 on the same blot. This technique compliments the alternative IP approach utilizing a PD-L1 antibody as the IP reagent and an anti-acetyl lysine antibody as the detection reagent. However, because the protocol described here enables the detection of both total and acetylated PD-L1 on the same blot, this method has the advantage of allowing quantitation of the percent of PD-L1 that is acetylated, an important parameter for mechanistic interpretation. The method described here utilizes beads that are covalently linked to the affinity antibody, resulting in extremely clean IP results. Western blots can be re-probed with a pan anti-acetyl lysine antibody to visualize the total protein acetylation profile in any given lysate, a property that is useful when examining PD-L1 acetylation in the presence of HDAC inhibitors or other treatments affecting global acetylation.

TLGP: a flexible transfer learning algorithm for gene prioritization based on heterogeneous source domain

Background Gene prioritization (gene ranking) aims to obtain the centrality of genes, which is critical for cancer diagnosis and therapy since keys genes correspond to the biomarkers or targets of drugs. Great efforts have been devoted to the gene ranking problem by exploring the similarity between candidate and known disease-causing genes. However, when the number of disease-causing genes is limited, they are not applicable largely due to the low accuracy. Actually, the number of disease-causing genes for cancers, particularly for these rare cancers, are really limited. Therefore, there is a critical needed to design effective and efficient algorithms for gene ranking with limited prior disease-causing genes. Results In this study, we propose a transfer learning based algorithm for gene prioritization (called TLGP) in the cancer (target domain) without disease-causing genes by transferring knowledge from other cancers (source domain). The underlying assumption is that knowledge shared by similar cancers improves the accuracy of gene prioritization. Specifically, TLGP first quantifies the similarity between the target and source domain by calculating the affinity matrix for genes. Then, TLGP automatically learns a fusion network for the target cancer by fusing affinity matrix, pathogenic genes and genomic data of source cancers. Finally, genes in the target cancer are prioritized. The experimental results indicate that the learnt fusion network is more reliable than gene co-expression network, implying that transferring knowledge from other cancers improves the accuracy of network construction. Moreover, TLGP outperforms state-of-the-art approaches in terms of accuracy, improving at least 5%. Conclusion The proposed model and method provide an effective and efficient strategy for gene ranking by integrating genomic data from various cancers.