Helper T Cell (CD4(+)) Targeted Tacrolimus Delivery Mediates Precise Suppression of Allogeneic Humoral Immunity

Antibody-mediated rejection (ABMR) is a major cause of dysfunction and loss of transplanted kidney. The current treatments for ABMR involve nonspecific inhibition and clearance of T/B cells or plasma cells. However, the prognosis of patients following current treatment is poor. T follicular helper cells (Tfh) play an important role in allograft-specific antibodies secreting plasma cell (PC) development. Tfh cells are therefore considered to be important therapeutic targets for the treatment of antibody hypersecretion disorders, such as transplant rejection and autoimmune diseases. Tacrolimus (Tac), the primary immunosuppressant, prevents rejection by reducing T cell activation. However, its administration should be closely monitored to avoid serious side effects. In this study, we investigated whether Tac delivery to helper T (CD4(+)) cells using functionalized mesoporous nanoparticles can block Tfh cell differentiation after alloantigen exposure. Results showed that Tac delivery ameliorated humoral rejection injury in rodent kidney graft by suppressing Tfh cell development, PC, and donor-specific antibody (DSA) generation without causing severe side effects compared with delivery through the drug administration pathway. This study provides a promising therapeutic strategy for preventing humoral rejection in solid organ transplantation. The specific and controllable drug delivery avoids multiple disorder risks and side effects observed in currently used clinical approaches

Driver Fatigue Detection Using Improved Deep Learning and Personalized Framework

In transportation, drivers’ state directly affects traffic safety. Therefore, an accurate driver’s fatigue detection is crucial for ensuring driving safety. Real-time and accurate technology is needed for driver fatigue detection. To address this problem, this article proposes a fatigue detection method based on an improved deep learning and personalized framework. First, clustering is applied to face size, and cluster numbers are used to determine the number of detection layers. Then, the size of anchor boxes is set according to the face size. In the proposed framework, the number of convolutional networks is set according to the principle that the receptive field should match the face size in the predicted feature map. Finally, a variety of fatigue features are learned by minimizing the loss function. In addition, a personalized face fatigue detection method is put forward for building a fatigue detection framework to judge the driver’s fatigue status more reasonably. The experimental results show that the proposed method based on an improved clustering method and local receptive field can improve the detection speed of driver’s fatigue while maintaining high detection accuracy. The proposed method can reach 125 fps by using GPU GeForce GTX TITAN, which satisfies the real-time requirement. In addition, the personalized framework can achieve high detection accuracy while keeping acceptable speed. The proposed model can accurately and timely detect driver fatigue, which can help to avoid accidents. </jats:p

HTCNet: Hybrid Transformer-CNN for SAR Image Denoising

Synthetic aperture radar (SAR) is extensively utilized in diverse fields, including military defense and resource exploration, due to its all-day, all-weather characteristics. However, the extraction of information from SAR images is severely affected by speckle noise, making denoising crucial. This article proposes a hybrid transformer-convolutional neural networks (CNNs) network, a hybrid denoising network that combines transformer and CNN. The three core designs of the network ensure its suitability for SAR image denoising: 1) The network integrates a transformer-based encoder with a CNN-based decoder, capturing both local and global dependencies inherent in SAR images, thereby enhancing the effectiveness of noise removal. 2) Patch embedding blocks enhance the convolutional neural network&#x0027;s perception of features at different scales. 3) Depthwise separable convolutions are fused into the Transformer block to further improve the network&#x0027;s ability to capture spatial information while reducing computational complexity. The proposed algorithm demonstrates excellent denoising performance in both simulated and real SAR images, as evidenced by experimental results. Compared to other denoising algorithms, this method efficiently removes speckle noise while preserving the texture information within the images

Analysis of circumferential gas force on star-wheel in a single screw expander

Service life of a star-wheel directly affects operation stability of a single screw expander. In addition, since star-wheel tooth of a single screw expander is prone to wear, circumferential force of the star-wheel rotor is investigated in this paper. Geometric gap models between the meshing pair with a single line envelope profile at different rotation angles are established and meshed section pressure fields and velocity fields of a referent tooth flank are simulated. Circumferential gas torque calculation model of a referent tooth and multi-tooth are established, and torque influencing factors are analyzed. Results indicate that the pressure difference between the expansion cavity and low-pressure cavity as well as gap geometry shape have a significant influence on the pressure field and velocity field. Both gas forces that act on the meshed section and the unmeshed section of the tooth have significant effects on the circumferential torque. Due to multi-tooth coupling, total gas torque periodically changes with the star-wheel rotation angle. Moreover, the negative values indicate that the gas torque on the left side of the star-wheel is greater than that on the right side. Unbalanced gas torque can cause star-wheel teeth deviation towards the side with less force. This consequently results in wear increase. </jats:p

A calibrated SVM based on weighted smooth &#x1d43a;&#x1d43f;_{1∕2} for Alzheimer’s disease prediction

Alzheimer’s disease (AD) is currently one of the mainstream senile diseases in the world. It is a key problem predicting the early stage of AD. Low accuracy recognition of AD and high redundancy brain lesions are the main obstacles. Traditionally, Group Lasso method can achieve good sparseness. But, redundancy inside group is ignored. This paper proposes an improved smooth classification framework which combines the weighted smooth &#x1d43a;&#x1d43f;1∕2 (&#x1d464;&#x1d446;&#x1d43a;&#x1d43f;1∕2) as feature selection method and a calibrated support vector machine (cSVM) as the classifier. &#x1d464;&#x1d446;&#x1d43a;&#x1d43f;1∕2 can make intra-group and inner-group features sparse, in which the group weights can further improve the efficiency of the model. cSVM can enhance the speed and stability of model by adding calibrated hinge function. Before feature selecting, an anatomical boundary-based clustering, called as ac-SLIC-AAL, is designed to make adjacent similar voxels into one group for accommodating the overall differences of all data. The &#x1d450;&#x1d446;&#x1d449; &#x1d440; model is fast convergence speed, high accuracy and good interpretability on AD classification, AD early diagnosis and MCI transition prediction. In experiments, all steps are tested respectively, including classifiers’ comparison, feature selection verification, generalization verification and comparing with state-of-the-art methods. The results are supportive and satisfactory. The superior of the proposed model are verified globally. At the same time, the algorithm can point out the important brain areas in the MRI, which has important reference value for the doctor’s predictive work. The source code and data is available at http://github.com/Hu-s-h/c-SVMForMRI

Parallel computing of fuzzy integrals: Performance and test

Fuzzy integral in data mining is an excellent information fusion tool. It has obvious advantages in solving the combination of features and has more successful applications in classification problems. However, with the increase of the number of features, the time complexity and space complexity of fuzzy integral will also increase exponentially. This problem limits the development of fuzzy integral. This article proposes a high-efficiency fuzzy integral—Parallel and Sparse Frame Based Fuzzy Integral (PSFI) for reducing time complexity and space complexity in the calculation of fuzzy integrals, which is based on the distributed parallel computing framework-Spark combined with the concept of sparse storage. Aiming at the efficiency problem of the Python language, Cython programming technology is introduced in the meanwhile. Our algorithm is packaged into an algorithm library to realize a more efficient PSFI. The experiments verified the impact of the number of parallel nodes on the performance of the algorithm, test the performance of PSFI in classification, and apply PSFI on regression problems and imbalanced big data classification. The results have shown that PSFI reduces the variable storage space requirements of datasets with aplenty of features by thousands of times with the increase of computing resources. Furthermore, it is proved that PSFI has higher prediction accuracy than the classic fuzzy integral running on a single processor.</jats:p