Current induced magnetization switching in PtCoCr structures with enhanced perpendicular magnetic anisotropy and spin-orbit torques

Magnetic trilayers having large perpendicular magnetic anisotropy (PMA) and high spin-orbit torques (SOTs) efficiency are the key to fabricate nonvolatile magnetic memory and logic devices. In this work, PMA and SOTs are systematically studied in Pt/Co/Cr stacks as a function of Cr thickness. An enhanced perpendicular anisotropy field around 10189 Oe is obtained and is related to the interface between Co and Cr layers. In addition, an effective spin Hall angle up to 0.19 is observed due to the improved antidamping-like torque by employing dissimilar metals Pt and Cr with opposite signs of spin Hall angles on opposite sides of Co layer. Finally, we observed a nearly linear dependence between spin Hall angle and longitudinal resistivity from their temperature dependent properties, suggesting that the spin Hall effect may arise from extrinsic skew scattering mechanism. Our results indicate that 3d transition metal Cr with a large negative spin Hall angle could be used to engineer the interfaces of trilayers to enhance PMA and SOTs.Comment: 14 pages, 6 figure

DATR: Unsupervised Domain Adaptive Detection Transformer with Dataset-Level Adaptation and Prototypical Alignment

Object detectors frequently encounter significant performance degradation when confronted with domain gaps between collected data (source domain) and data from real-world applications (target domain). To address this task, numerous unsupervised domain adaptive detectors have been proposed, leveraging carefully designed feature alignment techniques. However, these techniques primarily align instance-level features in a class-agnostic manner, overlooking the differences between extracted features from different categories, which results in only limited improvement. Furthermore, the scope of current alignment modules is often restricted to a limited batch of images, failing to learn the entire dataset-level cues, thereby severely constraining the detector's generalization ability to the target domain. To this end, we introduce a strong DETR-based detector named Domain Adaptive detection TRansformer (DATR) for unsupervised domain adaptation of object detection. Firstly, we propose the Class-wise Prototypes Alignment (CPA) module, which effectively aligns cross-domain features in a class-aware manner by bridging the gap between object detection task and domain adaptation task. Then, the designed Dataset-level Alignment Scheme (DAS) explicitly guides the detector to achieve global representation and enhance inter-class distinguishability of instance-level features across the entire dataset, which spans both domains, by leveraging contrastive learning. Moreover, DATR incorporates a mean-teacher based self-training framework, utilizing pseudo-labels generated by the teacher model to further mitigate domain bias. Extensive experimental results demonstrate superior performance and generalization capabilities of our proposed DATR in multiple domain adaptation scenarios. Code is released at https://github.com/h751410234/DATR.Comment: Manuscript submitted to IEEE Transactions on Image Processin

Dexmedetomidine preconditioning alleviates apoptosis in rat cardiomyocytes by suppressing programmed cell death 4 (PDCD4) after myocardial ischemia-reperfusion injury

Purpose: To determine the role of dexmedetomidine (Dex) in hypoxia/reoxygenation (H/R)-induced myocardial cell injury and the possible involvement of the programmed cell death 4 (Pdcd4) gene in Dex-mediated myocardial cell apoptosis after ischemia-reperfusion (I/R) injury. Methods: An in vivo I/R-injured rat model and in vitro H/R rat cell model were evaluated to ascertain the role of Dex in apoptosis. Programmed cell death 4 (PDCD4) gene expression levels were measured after Dex preconditioning. The effects of Pdcd4 knockdown or overexpression on Dex-mediated apoptosis during H/R injury were determined. Results: Dex pretreatment alleviated myocardial infarction in rats, suppressed myocardial cell apoptosis, and inhibited PDCD4 expression (p < 0.05). Treatment with Dex also alleviated H/R-induced apoptosis in rat cardiomyocytes, while PDCD4 expression decreased after Dex treatment (p < 0.05). Moreover, PDCD4 overexpression reversed the inhibitory effect of Dex on H/R myocardial cell apoptosis. Conclusion: Dex alleviates myocardial infarction in rats via its effect on PDCD4 expression. Therefore, Dex can potentially be used for the treatment but this has to clinical studies

Unraveling the role of the gut microbiome in pregnancy disorders: insights and implications

The gut microbiota is the collective term for the microorganisms that reside in the human gut. In recent years, advances in sequencing technology and bioinformatics gradually revealed the role of gut microbiota in human health. Dramatic changes in the gut microbiota occur during pregnancy due to hormonal and dietary changes, and these changes have been associated with certain gestational diseases such as preeclampsia (PE) and gestational diabetes mellitus (GDM). Modulation of gut microbiota has also been proposed as a potential treatment for these gestational diseases. The present article aims to review current reports on the association between gut microbiota and gestational diseases, explore possible mechanisms, and discuss the potential of probiotics in gestational diseases. Uncovering the link between gut microbiota and gestational diseases could lead to a new therapeutic approach

Inverse design of multistable kirigami metamaterial via geometry-enabled shape programming and transforming

The inverse design of metamaterials with desired properties represents a significant challenge in mechanical science. Despite the potential demonstrated by recent algorithm models, their adoption has been limited by constraints such as the geometric limitations of elementary building cells. The use of the kirigami principle, which offers large deformation and nonlinear stiffness, has been explored. However, existing kirigami geometries, which remain isotropic, may restrict the design space. Our objective is to leverage the capabilities of geometry in shape programming and transformation to provide a framework for inverse design. This framework utilizes a unified geometry in kirigami cutting that is easily parameterized to generate independent anisotropic deformation and bistability. By integrating machine learning with a genetic algorithm, we achieve an inverse design process. The resulting kirigami architectures can be preprogrammed into target shapes and transformed between multiple stable states. This work underscores the significance of cell geometry topology, offering a powerful tool for the inverse design of metamaterials with reconfigurable and tailored mechanical properties, applicable in various fields such as robotics, electronics, and beyond

Research on sea surface NB-IoT coverage based on improved SPM

Based on the standard SPM,an improved sea surface propagation model was proposed.At the same time,a propagation model correction algorithm based on WLS algorithm was proposed.Using the CW test data of Qiongzhou Strait,the parameters of the improved SPM were corrected.Based on the corrected propagation model,the current base station of Qiongzhou Strait coast was used to carry out link level simulation and coverage simulation for NB-IoT.The experimental results show that the proposed method can effectively achieve the coverage of NB-IoT in Qiongzhou Strait and contribute to the scientific research of Internet of things

Landmark Tracking in Liver US images Using Cascade Convolutional Neural Networks with Long Short-Term Memory

This study proposed a deep learning-based tracking method for ultrasound (US) image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from a US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest (ROI) proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box regression, proposal classification, and landmark segmentation. The LSTM network models the temporal relationship among the successive image frames for bounding box regression and proposal classification. To consolidate the final proposal, a selection method is designed according to the similarities between sequential frames. The proposed method was tested on the liver US tracking datasets used in the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 challenges, where the landmarks were annotated by three experienced observers to obtain their mean positions. Five-fold cross-validation on the 24 given US sequences with ground truths shows that the mean tracking error for all landmarks is 0.65+/-0.56 mm, and the errors of all landmarks are within 2 mm. We further tested the proposed model on 69 landmarks from the testing dataset that has a similar image pattern to the training pattern, resulting in a mean tracking error of 0.94+/-0.83 mm. Our experimental results have demonstrated the feasibility and accuracy of our proposed method in tracking liver anatomic landmarks using US images, providing a potential solution for real-time liver tracking for active motion management during radiation therapy