LDCSF: Local depth convolution-based Swim framework for classifying multi-label histopathology images

Histopathological images are the gold standard for diagnosing liver cancer. However, the accuracy of fully digital diagnosis in computational pathology needs to be improved. In this paper, in order to solve the problem of multi-label and low classification accuracy of histopathology images, we propose a locally deep convolutional Swim framework (LDCSF) to classify multi-label histopathology images. In order to be able to provide local field of view diagnostic results, we propose the LDCSF model, which consists of a Swin transformer module, a local depth convolution (LDC) module, a feature reconstruction (FR) module, and a ResNet module. The Swin transformer module reduces the amount of computation generated by the attention mechanism by limiting the attention to each window. The LDC then reconstructs the attention map and performs convolution operations in multiple channels, passing the resulting feature map to the next layer. The FR module uses the corresponding weight coefficient vectors obtained from the channels to dot product with the original feature map vector matrix to generate representative feature maps. Finally, the residual network undertakes the final classification task. As a result, the classification accuracy of LDCSF for interstitial area, necrosis, non-tumor and tumor reached 0.9460, 0.9960, 0.9808, 0.9847, respectively. Finally, we use the results of multi-label pathological image classification to calculate the tumor-to-stromal ratio, which lays the foundation for the analysis of the microenvironment of liver cancer histopathological images. Second, we released a multilabel histopathology image of liver cancer, our code and data are available at https://github.com/panliangrui/LSF.Comment: Submitted to BIBM202

PACS: Prediction and analysis of cancer subtypes from multi-omics data based on a multi-head attention mechanism model

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omic data and clinical characteristics among different cancer subtypes. Therefore, accurate classification of cancer subtypes can help doctors choose the most appropriate treatment options, improve treatment outcomes, and provide more accurate patient survival predictions. In this study, we propose a supervised multi-head attention mechanism model (SMA) to classify cancer subtypes successfully. The attention mechanism and feature sharing module of the SMA model can successfully learn the global and local feature information of multi-omics data. Second, it enriches the parameters of the model by deeply fusing multi-head attention encoders from Siamese through the fusion module. Validated by extensive experiments, the SMA model achieves the highest accuracy, F1 macroscopic, F1 weighted, and accurate classification of cancer subtypes in simulated, single-cell, and cancer multiomics datasets compared to AE, CNN, and GNN-based models. Therefore, we contribute to future research on multiomics data using our attention-based approach.Comment: Submitted to BIBM202

CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology Images

Histopathology image segmentation is the gold standard for diagnosing cancer, and can indicate cancer prognosis. However, histopathology image segmentation requires high-quality masks, so many studies now use imagelevel labels to achieve pixel-level segmentation to reduce the need for fine-grained annotation. To solve this problem, we propose an attention-based cross-view feature consistency end-to-end pseudo-mask generation framework named CVFC based on the attention mechanism. Specifically, CVFC is a three-branch joint framework composed of two Resnet38 and one Resnet50, and the independent branch multi-scale integrated feature map to generate a class activation map (CAM); in each branch, through down-sampling and The expansion method adjusts the size of the CAM; the middle branch projects the feature matrix to the query and key feature spaces, and generates a feature space perception matrix through the connection layer and inner product to adjust and refine the CAM of each branch; finally, through the feature consistency loss and feature cross loss to optimize the parameters of CVFC in co-training mode. After a large number of experiments, An IoU of 0.7122 and a fwIoU of 0.7018 are obtained on the WSSS4LUAD dataset, which outperforms HistoSegNet, SEAM, C-CAM, WSSS-Tissue, and OEEM, respectively.Comment: Submitted to BIBM202

One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization

As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance degradation on recent models UniXcoder and CodeT5. To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it. Updating only 0.6\% of the overall parameters compared to full-model fine-tuning for each programming language, adapter tuning yields consistent improvements on code search and summarization tasks, achieving state-of-the-art results. In addition, we experimentally show its effectiveness in cross-lingual and low-resource scenarios. Multilingual fine-tuning with 200 samples per programming language approaches the results fine-tuned with the entire dataset on code summarization. Our experiments on three probing tasks show that adapter tuning significantly outperforms full-model fine-tuning and effectively overcomes catastrophic forgetting.Comment: Accepted to the 45th International Conference on Software Engineering (ICSE 2023

Multi-Head Attention Mechanism Learning for Cancer New Subtypes and Treatment Based on Cancer Multi-Omics Data

Due to the high heterogeneity and clinical characteristics of cancer, there are significant differences in multi-omics data and clinical features among subtypes of different cancers. Therefore, the identification and discovery of cancer subtypes are crucial for the diagnosis, treatment, and prognosis of cancer. In this study, we proposed a generalization framework based on attention mechanisms for unsupervised contrastive learning (AMUCL) to analyze cancer multi-omics data for the identification and characterization of cancer subtypes. AMUCL framework includes a unsupervised multi-head attention mechanism, which deeply extracts multi-omics data features. Importantly, a decoupled contrastive learning model (DMACL) based on a multi-head attention mechanism is proposed to learn multi-omics data features and clusters and identify new cancer subtypes. This unsupervised contrastive learning method clusters subtypes by calculating the similarity between samples in the feature space and sample space of multi-omics data. Compared to 11 other deep learning models, the DMACL model achieved a C-index of 0.002, a Silhouette score of 0.801, and a Davies Bouldin Score of 0.38 on a single-cell multi-omics dataset. On a cancer multi-omics dataset, the DMACL model obtained a C-index of 0.016, a Silhouette score of 0.688, and a Davies Bouldin Score of 0.46, and obtained the most reliable cancer subtype clustering results for each type of cancer. Finally, we used the DMACL model in the AMUCL framework to reveal six cancer subtypes of AML. By analyzing the GO functional enrichment, subtype-specific biological functions, and GSEA of AML, we further enhanced the interpretability of cancer subtype analysis based on the generalizable AMUCL framework

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

To tackle the exponentially increasing throughput of Next-Generation Sequencing (NGS), most of the existing short-read aligners can be configured to favor speed in trade of accuracy and sensitivity. SOAP3-dp, through leveraging the computational power of both CPU and GPU with optimized algorithms, delivers high speed and sensitivity simultaneously. Compared with widely adopted aligners including BWA, Bowtie2, SeqAlto, GEM and GPU-based aligners including BarraCUDA and CUSHAW, SOAP3-dp is two to tens of times faster, while maintaining the highest sensitivity and lowest false discovery rate (FDR) on Illumina reads with different lengths. Transcending its predecessor SOAP3, which does not allow gapped alignment, SOAP3-dp by default tolerates alignment similarity as low as 60 percent. Real data evaluation using human genome demonstrates SOAP3-dp's power to enable more authentic variants and longer Indels to be discovered. Fosmid sequencing shows a 9.1 percent FDR on newly discovered deletions. SOAP3-dp natively supports BAM file format and provides a scoring scheme same as BWA, which enables it to be integrated into existing analysis pipelines. SOAP3-dp has been deployed on Amazon-EC2, NIH-Biowulf and Tianhe-1A.Comment: 21 pages, 6 figures, submitted to PLoS ONE, additional files available at "https://www.dropbox.com/sh/bhclhxpoiubh371/O5CO_CkXQE". Comments most welcom

MICA: A fast short-read aligner that takes full advantage of Many Integrated Core Architecture (MIC)

Background: Short-read aligners have recently gained a lot of speed by exploiting the massive parallelism of GPU. An uprising alterative to GPU is Intel MIC; supercomputers like Tianhe-2, currently top of TOP500, is built with 48,000 MIC boards to offer ~55 PFLOPS. The CPU-like architecture of MIC allows CPU-based software to be parallelized easily; however, the performance is often inferior to GPU counterparts as an MIC card contains only ~60 cores (while a GPU card typically has over a thousand cores). Results: To better utilize MIC-enabled computers for NGS data analysis, we developed a new short-read aligner MICA that is optimized in view of MIC's limitation and the extra parallelism inside each MIC core. By utilizing the 512-bit vector units in the MIC and implementing a new seeding strategy, experiments on aligning 150 bp paired-end reads show that MICA using one MIC card is 4.9 times faster than BWA-MEM (using 6 cores of a top-end CPU), and slightly faster than SOAP3-dp (using a GPU). Furthermore, MICA's simplicity allows very efficient scale-up when multiple MIC cards are used in a node (3 cards give a 14.1-fold speedup over BWA-MEM). Summary: MICA can be readily used by MIC-enabled supercomputers for production purpose. We have tested MICA on Tianhe-2 with 90 WGS samples (17.47 Tera-bases), which can be aligned in an hour using 400 nodes. MICA has impressive performance even though MIC is only in its initial stage of development. Availability and implementation: MICA's source code is freely available at http://sourceforge.net/projects/mica-aligner under GPL v3. Supplementary information: Supplementary information is available as "Additional File 1". Datasets are available at www.bio8.cs.hku.hk/dataset/mica.published_or_final_versio

Intracellular β\u3csub\u3e1\u3c/sub\u3e-Adrenergic Receptors and Organic Cation Transporter 3 Mediate Phospholamban Phosphorylation to Enhance Cardiac Contractility

Rationale: β1ARs (β1-adrenoceptors) exist at intracellular membranes and OCT3 (organic cation transporter 3) mediates norepinephrine entry into cardiomyocytes. However, the functional role of intracellular β1AR in cardiac contractility remains to be elucidated. Objective: Test localization and function of intracellular β1AR on cardiac contractility. Methods and Results: Membrane fractionation, super-resolution imaging, proximity ligation, coimmunoprecipitation, and single-molecule pull-down demonstrated a pool of β1ARs in mouse hearts that were associated with sarco/endoplasmic reticulum Ca2+-ATPase at the sarcoplasmic reticulum (SR). Local PKA (protein kinase A) activation was measured using a PKA biosensor targeted at either the plasma membrane (PM) or SR. Compared with wild-type, myocytes lacking OCT3 (OCT3-KO [OCT3 knockout]) responded identically to the membrane-permeant βAR agonist isoproterenol in PKA activation at both PM and SR. The same was true at the PM for membrane-impermeant norepinephrine, but the SR response to norepinephrine was suppressed in OCT3-KO myocytes. This differential effect was recapitulated in phosphorylation of the SR-pump regulator phospholamban. Similarly, OCT3-KO selectively suppressed calcium transients and contraction responses to norepinephrine but not isoproterenol. Furthermore, sotalol, a membrane-impermeant βAR-blocker, suppressed isoproterenol-induced PKA activation at the PM but permitted PKA activation at the SR, phospholamban phosphorylation, and contractility. Moreover, pretreatment with sotalol in OCT3-KO myocytes prevented norepinephrine-induced PKA activation at both PM and the SR and contractility. Conclusions: Functional β1ARs exists at the SR and is critical for PKA-mediated phosphorylation of phospholamban and cardiac contractility upon catecholamine stimulation. Activation of these intracellular β1ARs requires catecholamine transport via OCT3