AFPN: Asymptotic Feature Pyramid Network for Object Detection

Multi-scale features are of great importance in encoding objects with scale variance in object detection tasks. A common strategy for multi-scale feature extraction is adopting the classic top-down and bottom-up feature pyramid networks. However, these approaches suffer from the loss or degradation of feature information, impairing the fusion effect of non-adjacent levels. This paper proposes an asymptotic feature pyramid network (AFPN) to support direct interaction at non-adjacent levels. AFPN is initiated by fusing two adjacent low-level features and asymptotically incorporates higher-level features into the fusion process. In this way, the larger semantic gap between non-adjacent levels can be avoided. Given the potential for multi-object information conflicts to arise during feature fusion at each spatial location, adaptive spatial fusion operation is further utilized to mitigate these inconsistencies. We incorporate the proposed AFPN into both two-stage and one-stage object detection frameworks and evaluate with the MS-COCO 2017 validation and test datasets. Experimental evaluation shows that our method achieves more competitive results than other state-of-the-art feature pyramid networks. The code is available at \href{https://github.com/gyyang23/AFPN}{https://github.com/gyyang23/AFPN}

Short- and long-term effects of antiretroviral therapy on peripheral regulatory CD4+/CD25hi/CD127low T lymphocytes in people living with HIV/AIDS

The effect of antiretroviral therapy (ART) on CD4+/CD25hi/CD127low T lymphocyte changes in people living with HIV/AIDS (PLWHA) is still a matter of debate. From October 2015 to December 2019, peripheral blood from 70 cases of PLWHA were collected for the detection of CD4+/CD25hi/CD127low T lymphocytes by flow cytometry. Statistical analysis was performed to detect changes of CD4+/CD25hi/CD127low T lymphocytes in patients with different duration of ART and different treatment effects. We found that the number of CD4+/CD25hi/CD127low T lymphocytes in ART-naive PLWHA were lower than those in healthy volunteers (10.3±٦.٠ cells/uL vs 31.7±8.0 cells/uL, P < 0.05). CD4+/CD25hi/CD127low T lymphocyte counts increased to 17.8±٤.٠ cells/uL 6 months post-ART and 25.0±١١.٩ cells/uL 9 months post-ART, respectively (P < 0.05). There was no significant difference in CD4+/CD25hi/CD127low T lymphocyte counts between PLWHA who reached a complete immune reconstruction after ART and healthy volunteers. The growth of CD4+/CD25hi/CD127low T lymphocyte counts in patients who had baseline CD4 > 200 cells/uL was greater than those who had baseline CD4 ≤ 200 cells/uL (12.6±٤.٦ cells/uL vs 5.6±٥.٠ cells/uL, P = 0.027). CD4+/CD25hi/CD127low T lymphocyte counts were positively correlated with CD4+ T lymphocyte counts (r = 0.923, P < 0.001) and CD4+/CD8+ ratio (r = 0.741, P < 0.001), but were negatively correlated with HIV-VL (r = −0.648, P = 0.000). In conclusion, the results of the present study showed that changes in CD4+/CD25hi/CD127low T lymphocyte counts can be used to assess the effect of ART in PLWHA

Fremanezumab for the Preventive Treatment of Chronic Migraine.

BACKGROUND: Fremanezumab, a humanized monoclonal antibody targeting calcitonin gene-related peptide (CGRP), is being investigated as a preventive treatment for migraine. We compared two fremanezumab dose regimens with placebo for the prevention of chronic migraine. METHODS: In this phase 3 trial, we randomly assigned patients with chronic migraine (defined as headache of any duration or severity on ≥15 days per month and migraine on ≥8 days per month) in a 1:1:1 ratio to receive fremanezumab quarterly (a single dose of 675 mg at baseline and placebo at weeks 4 and 8), fremanezumab monthly (675 mg at baseline and 225 mg at weeks 4 and 8), or matching placebo. Both fremanezumab and placebo were administered by means of subcutaneous injection. The primary end point was the mean change from baseline in the average number of headache days (defined as days in which headache pain lasted ≥4 consecutive hours and had a peak severity of at least a moderate level or days in which acute migraine-specific medication [triptans or ergots] was used to treat a headache of any severity or duration) per month during the 12 weeks after the first dose. RESULTS: Of 1130 patients enrolled, 376 were randomly assigned to fremanezumab quarterly, 379 to fremanezumab monthly, and 375 to placebo. The mean number of baseline headache days (as defined above) per month was 13.2, 12.8, and 13.3, respectively. The least-squares mean (±SE) reduction in the average number of headache days per month was 4.3±0.3 with fremanezumab quarterly, 4.6±0.3 with fremanezumab monthly, and 2.5±0.3 with placebo (P CONCLUSIONS: Fremanezumab as a preventive treatment for chronic migraine resulted in a lower frequency of headache than placebo in this 12-week trial. Injection-site reactions to the drug were common. The long-term durability and safety of fremanezumab require further study. (Funded by Teva Pharmaceuticals; ClinicalTrials.gov number, NCT02621931 .)

General Rotation Invariance Learning for Point Clouds via Weight-Feature Alignment

Compared to 2D images, 3D point clouds are much more sensitive to rotations. We expect the point features describing certain patterns to keep invariant to the rotation transformation. There are many recent SOTA works dedicated to rotation-invariant learning for 3D point clouds. However, current rotation-invariant methods lack generalizability on the point clouds in the open scenes due to the reliance on the global distribution, \ie the global scene and backgrounds. Considering that the output activation is a function of the pattern and its orientation, we need to eliminate the effect of the orientation.In this paper, inspired by the idea that the network weights can be considered a set of points distributed in the same 3D space as the input points, we propose Weight-Feature Alignment (WFA) to construct a local Invariant Reference Frame (IRF) via aligning the features with the principal axes of the network weights. Our WFA algorithm provides a general solution for the point clouds of all scenes. WFA ensures the model achieves the target that the response activity is a necessary and sufficient condition of the pattern matching degree. Practically, we perform experiments on the point clouds of both single objects and open large-range scenes. The results suggest that our method almost bridges the gap between rotation invariance learning and normal methods.Comment: 4 figure

Identification and characterization of four immune-related signatures in keloid

A keloid is a fibroproliferative disorder of unknown etiopathogenesis that requires ill-defined treatment. Existing evidence indicates that the immune system plays an important role in the occurrence and development of keloid. However, there is still a lack of research on the immune-related signatures of keloid. Here we identified immune-related signatures in keloid and explored their pathological mechanisms. Transcriptomic datasets (GSE7890, GSE92566, and GSE44270) of keloid and normal skin tissues were obtained from the Gene Expression Omnibus database. The overlap of differentially expressed genes and immune-related genes was considered as differentially expressed immune-related genes (DEIGs). Functional analysis, expression, and distribution were applied to explore the function and characteristics of DEIGs, and the expression of these DEIGs in keloid and normal skin tissues was verified by immunohistochemistry. Finally, we conducted interactive network analysis and immune infiltration analysis to determine the therapeutic potential and immune correlation. We identified four DEIGs (LGR5, PTN, JAG1, and DKK1). In these datasets, only GSE7890 met the screening criteria. In the GSE7890 dataset, DKK1 and PTN were downregulated in keloid, whereas JAG1 and LGR5 were upregulated in keloid. In addition, we obtained the same conclusion through immunohistochemistry. Functional analysis indicated that these four DEIGs were mainly involved in stem cell, cell cycle, UV response, and therapy resistance. Through interactive network analysis, we found that these DEIGs were associated with drugs currently used to treat keloid, such as hydrocortisone, androstanolone, irinotecan, oxaliplatin, BHQ-880, and lecoleucovorin. Finally, many immune cells, including CD8+ T cells, resting memory CD4+ T cells, and M1 macrophages, were obtained by immune infiltration analysis. In conclusion, we identified four immune signaling molecules associated with keloid (LGR5, PTN, JAG1, and DKK1). These immune-related signaling molecules may be important modules in the pathogenesis of keloid. Additionally, we developed novel therapeutic targets for the treatment of this challenging disease

Image classification-based brain tumour tissue segmentation

Brain tumour tissue segmentation is essential for clinical decision making. While manual segmentation is time consuming, tedious, and subjective, it is very challenging to develop automatic segmentation methods. Deep learning with convolutional neural network (CNN) architecture has consistently outperformed previous methods on such challenging tasks. However, the local dependencies of pixel classes cannot be fully reflected in the CNN models. In contrast, hand-crafted features such as histogram-based texture features provide robust feature descriptors of local pixel dependencies. In this paper, a classification-based method for automatic brain tumour tissue segmentation is proposed using combined CNN-based and hand-crafted features. The CIFAR network is modified to extract CNN-based features, and histogram-based texture features are fused to compensate the limitation in the CIFAR network. These features together with the pixel intensities of the original MRI images are sent to a decision tree for classifying the MRI image voxels into different types of tumour tissues. The method is evaluated on the BraTS 2017 dataset. Experiments show that the proposed method produces promising segmentation results

A self-paced learning algorithm for change detection in synthetic aperture radar images

Detecting changed regions between two given synthetic aperture radar images is very important to monitor the change of landscapes, change of ecosystem and so on. This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data. Hence, we propose an unsupervised algorithm aiming at constructing a classifier based on self-paced learning. Self-paced learning is a recently developed supervised learning approach and has been proven to be capable to overcome effectively this shortcoming. After applying a pre-classification to the difference image, we uniformly select samples using the initial result. Then, self-paced learning is utilized to train a classifier. Finally, a filter is used based on spatial contextual information to further smooth the classification result. In order to demonstrate the efficiency of the proposed algorithm, we apply our proposed algorithm on five real synthetic aperture radar images datasets. The results obtained by our algorithm are compared with five other state-of-the-art algorithms, which demonstrates that our algorithm outperforms those state-of-the-art algorithms in terms of accuracy and robustness

Maximizing spin-orbit torque efficiency of Ta(O)/Py via modulating oxygen-induced interface orbital hybridization

Spin-orbit torques due to interfacial Rashba and spin Hall effects have been widely considered as a potentially more efficient approach than the conventional spin-transfer torque to control the magnetization of ferromagnets. We report a comprehensive study of spin-orbit torque efficiency in Ta(O)/Ni81Fe19 bilayers by tuning low-oxidation of \b{eta}-phase tantalum, and find that the spin Hall angle {\theta}DL increases from ~ -0.18 of the pure Ta/Py to the maximum value ~ -0.30 of Ta(O)/Py with 7.8% oxidation. Furthermore, we distinguish the efficiency of the spin-orbit torque generated by the bulk spin Hall effect and by interfacial Rashba effect, respectively, via a series of Py/Cu(0-2 nm)/Ta(O) control experiments. The latter has more than twofold enhancement, and even more significant than that of the former at the optimum oxidation level. Our results indicate that 65% enhancement of the efficiency should be related to the modulation of the interfacial Rashba-like spin-orbit torque due to oxygen-induced orbital hybridization cross the interface. Our results suggest that the modulation of interfacial coupling via oxygen-induced orbital hybridization can be an alternative method to boost the change-spin conversion rate.Comment: 15 pages, 4 figure