DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional Unet architectures and their transformer-integrated variants excel in automated segmentation tasks. However, they lack the ability to harness the intrinsic position and channel features of image. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block(DA-Block) into the traditional U-shaped architecture. Unlike earlier transformer-based U-net models, DA-TransUNet utilizes Transformers and DA-Block to integrate not only global and local features, but also image-specific positional and channel features, improving the performance of medical image segmentation. By incorporating a DA-Block at the embedding layer and within each skip connection layer, we substantially enhance feature extraction capabilities and improve the efficiency of the encoder-decoder structure. DA-TransUNet demonstrates superior performance in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across multiple datasets. In summary, DA-TransUNet offers a significant advancement in medical image segmentation, providing an effective and powerful alternative to existing techniques. Our architecture stands out for its ability to improve segmentation accuracy, thereby advancing the field of automated medical image diagnostics. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet

Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences

Saputri Dianita S., Ismanto Hendra S., Nugraha Dendi K., et al. Deciphering the antigen specificities of antibodies by clustering their complementarity determining region sequences. mSystems 8, e00722-23 (2023); https://doi.org/10.1128/msystems.00722-23.Recent advances in adaptive immune receptor repertoire sequencing have provided abundant B cell receptor (BCR) sequences under various conditions, including vaccination and disease. However, determining target antigen and epitope specificity of the corresponding antibodies is a major challenge due to their exceptional sequence diversity. Here, we introduce a novel method to cluster antibodies sharing antigenic targets based on their complementarity determining region (CDR) sequences. Using the proposed method, we demonstrate that SARS-CoV-2 spike protein receptor-binding domain (RBD) binders and non-RBD binders from publicly available BCR data were classified correctly, with a cluster purity of 95%. These clusters were then leveraged for annotating unlabeled COVID-19 patient BCR data, enabling the discovery of novel anti-RBD antibodies. We further validated the method by clustering BCR repertoires obtained from single-cell immune profiling of diphtheria-tetanus-pertussis (DTP)-vaccinated donors. Antibody expression and antigen-binding assays demonstrated that the clusters exhibited 96% antigen purity, surpassing the apparent 82% purity achieved by assigning antigens to the same B cells using fluorescently labeled DTP antigen probes. Moreover, antibodies within certain clusters were found to possess neutralizing activity, suggesting that CDR clusters contain epitope-level information. Together, this study offers a simple approach for antigen- and epitope-specific BCR discovery that is reproducible, inexpensive, and applicable to a wide range of antigen targets

DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation

Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with parameter efficiency and computational complexity, often due to the extensive use of Transformers. However, they lack the ability to harness the image’s intrinsic position and channel features. Research employing Dual Attention mechanisms of position and channel have not been specifically optimized for the high-detail demands of medical images. To address these issues, this study proposes a novel deep medical image segmentation framework, called DA-TransUNet, aiming to integrate the Transformer and dual attention block (DA-Block) into the traditional U-shaped architecture. Also, DA-TransUNet tailored for the high-detail requirements of medical images, optimizes the intermittent channels of Dual Attention (DA) and employs DA in each skip-connection to effectively filter out irrelevant information. This integration significantly enhances the model’s capability to extract features, thereby improving the performance of medical image segmentation. DA-TransUNet is validated in medical image segmentation tasks, consistently outperforming state-of-the-art techniques across 5 datasets. In summary, DA-TransUNet has made significant strides in medical image segmentation, offering new insights into existing techniques. It strengthens model performance from the perspective of image features, thereby advancing the development of high-precision automated medical image diagnosis. The codes and parameters of our model will be publicly available at https://github.com/SUN-1024/DA-TransUnet

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor

Acid-Promoted Selective Carbon–Fluorine Bond Activation and Functionalization of Hexafluoropropene by Nickel Complexes Supported with Phosphine Ligands

The electron-rich complex Ni­(PMe₃)₄ was utilized to react with perfluoropropene to obtain Ni­(CF₂CFCF₃)­(PMe₃)₃ (<b>1</b>). The selective C–F bond activation process of the π-coordinated perfluoropropene in <b>1</b> was conducted with the promotion of Lewis acids (ZnCl₂, LiBr, and LiI) under mild conditions to afford the products Ni­(CF₃CCF₂)­(PMe₃)₂X (X = Cl (<b>2</b>), Br (<b>3</b>), I (<b>4</b>)). The structures of complexes <b>2</b> and <b>3</b> determined by X-ray single-crystal diffraction confirmed that the C–F bond activation occurred at the geminal position of the trifluoromethyl group. Surprisingly, CF₃COOH as a protonic acid could also carry out a similar activation reaction to give rise to Ni­(CF₃CCF₂)­(CF₃COO)­(PMe₃)₂ (<b>7</b>), while only the addition products Ni­(CF₂CFHCF₃)­(CH₃COO)­(PMe₃) (<b>5</b>) and Ni­(CF₂CFHCF₃)­(CH₃SO₃)­(PMe₃) (<b>6</b>) were obtained with CH₃COOH and CH₃SO₃H. The transmetalation products Ni­(CF₃CCF₂)­Ph­(PMe₃)₂ (<b>8</b>), Ni­(CF₃CCF₂)­(<i>p</i>-MeOPh)­(PMe₃)₂ (<b>9</b>), and Ni­(CF₃CCF₂)­(CCPh)­(PMe₃)₂ (<b>10</b>) were obtained through the reactions of Ni­(CF₃CCF₂)­(PMe₃)₂Cl (<b>2</b>) with PhMgBr, (<i>p</i>-MeOPh)­MgBr, and PhCCLi. In contrast, the reaction of complex <b>2</b> with PhCH₂CH₂MgBr delivered complex <b>11</b>, Ni­(CF₃CHC–CH₂CH₂Ph)­(PMe₃)₂, via double C–F bond activation. All of the C­(sp²)–F bonds in complex <b>11</b> were activated and cleaved. The structures of complexes <b>5</b> and <b>7</b>–<b>11</b> were determined by X-ray single-crystal structure analysis. A reasonable mechanism was proposed and partially experimentally verified through operando IR and <i>in situ</i> ¹H NMR spectroscopy

Acid-Promoted Selective Carbon–Fluorine Bond Activation and Functionalization of Hexafluoropropene by Nickel Complexes Supported with Phosphine Ligands

The electron-rich complex Ni­(PMe₃)₄ was utilized to react with perfluoropropene to obtain Ni­(CF₂CFCF₃)­(PMe₃)₃ (<b>1</b>). The selective C–F bond activation process of the π-coordinated perfluoropropene in <b>1</b> was conducted with the promotion of Lewis acids (ZnCl₂, LiBr, and LiI) under mild conditions to afford the products Ni­(CF₃CCF₂)­(PMe₃)₂X (X = Cl (<b>2</b>), Br (<b>3</b>), I (<b>4</b>)). The structures of complexes <b>2</b> and <b>3</b> determined by X-ray single-crystal diffraction confirmed that the C–F bond activation occurred at the geminal position of the trifluoromethyl group. Surprisingly, CF₃COOH as a protonic acid could also carry out a similar activation reaction to give rise to Ni­(CF₃CCF₂)­(CF₃COO)­(PMe₃)₂ (<b>7</b>), while only the addition products Ni­(CF₂CFHCF₃)­(CH₃COO)­(PMe₃) (<b>5</b>) and Ni­(CF₂CFHCF₃)­(CH₃SO₃)­(PMe₃) (<b>6</b>) were obtained with CH₃COOH and CH₃SO₃H. The transmetalation products Ni­(CF₃CCF₂)­Ph­(PMe₃)₂ (<b>8</b>), Ni­(CF₃CCF₂)­(<i>p</i>-MeOPh)­(PMe₃)₂ (<b>9</b>), and Ni­(CF₃CCF₂)­(CCPh)­(PMe₃)₂ (<b>10</b>) were obtained through the reactions of Ni­(CF₃CCF₂)­(PMe₃)₂Cl (<b>2</b>) with PhMgBr, (<i>p</i>-MeOPh)­MgBr, and PhCCLi. In contrast, the reaction of complex <b>2</b> with PhCH₂CH₂MgBr delivered complex <b>11</b>, Ni­(CF₃CHC–CH₂CH₂Ph)­(PMe₃)₂, via double C–F bond activation. All of the C­(sp²)–F bonds in complex <b>11</b> were activated and cleaved. The structures of complexes <b>5</b> and <b>7</b>–<b>11</b> were determined by X-ray single-crystal structure analysis. A reasonable mechanism was proposed and partially experimentally verified through operando IR and <i>in situ</i> ¹H NMR spectroscopy

Energy-Saving Speed Planning for Electric Vehicles Based on RHRL in Car following Scenarios

Eco-driving is a driving vehicle strategy aimed at minimizing energy consumption; that is, it is a method to improve vehicle efficiency by optimizing driving behavior without making any hardware changes, especially for autonomous vehicles. To enhance energy efficiency across various driving scenarios, including road slopes, car following scenarios, and traffic signal interactions, this research introduces an energy-conserving speed planning approach for self-driving electric vehicles employing reinforcement learning. This strategy leverages vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication to acquire real-time data regarding traffic signal timing, leading vehicle speeds, and other pertinent driving conditions. In the framework of rolling horizon reinforcement learning (RHRL), predictions are made in each window using a rolling time domain approach. In the evaluation stage, Q-learning is used to obtain the optimal evaluation value, so that the vehicle can reach a reasonable speed. In conclusion, the algorithm’s efficacy is confirmed through vehicle simulation, with the results demonstrating that reinforcement learning adeptly modulates vehicle speed to minimize energy consumption, all while taking into account factors like road grade and maintaining a secure following distance from the preceding vehicle. Compared with the results of traditional adaptive cruise control (ACC), the algorithm can save 11.66% and 30.67% of energy under two working conditions