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Point cloud-based retrieval for place recognition is essential in robotic applications like autonomous driving or simultaneous localization and mapping. However, this remains challenging in complex real-world scenes. Existing methods are sensitive to noisy, low-density point clouds and require extensive storage and computation, posing limitations for hardware-limited scenarios. To overcome these challenges, we propose LWR-Net, a lightweight place recognition network for efficient and robust point cloud retrieval in noisy, low-density conditions. Our approach incorporates a fast dilated sampling and grouping module with a residual MLP structure to learn geometric features from local neighborhoods. We also introduce a lightweight attentional weighting module to enhance global feature representation. By utilizing the Generalized Mean pooling structure, we aggregated the global descriptor for point cloud retrieval. We validated LWR-Net's efficiency and robustness on the Oxford robotcar dataset and three in-house datasets. The results demonstrate that our method efficiently and accurately retrieves matching scenes while being more robust to variations in point density and noise intensity. LWR-Net achieves state-of-the-art accuracy and robustness with a lightweight model size of 0.4M parameters. These efficiency, robustness, and lightweight advantages make our network highly suitable for robotic applications relying on point cloud-based place recognition. [ABSTRACT FROM AUTHOR]

The pedestrian stride-length estimation is a crucial piece of personal behavior data for many smartphone applications, such as health monitoring and indoor location. The performance of the present stride-length algorithms is suitable for simple gaits and single scenes, but when applied to sophisticated gaits or heterogeneous devices, their inaccuracy varies dramatically. This paper proposes an efficient learning-based stride-length estimation model using a smartphone to obtain the correct stride length. The model uses adaptive learning to extract different elements for changing and recognition tasks, including Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) modules. The direct fusion method maps the eigenvectors to the appropriate stride length after combining the features from the learning modules. We presented an online learning module to update the model to increase the SLE model's generalization. Extensive experiments are conducted with heterogeneous devices or users, various gaits, and switched scenarios. The results confirm that the proposed method outperforms other state-of-the-art methods and achieves an average 4.26% estimation error rate in various environments. [ABSTRACT FROM AUTHOR]

Background: Hyperhomocysteinemia has been reported in psoriasis. We investigated the effect of methylenetetrahydrofolate reductase (MTHFR), polymorphism and folic acid supplementation on serum homocysteine levels in psoriasis. Methods: Serum homocysteine levels were detected at baseline and at week 12 in 201 patients who were genotyped with MTHFR rs1801133 without and 93 psoriatic patients with folate supplement. Results: TT genotype carriers of MTHFR rs1801133 had significantly higher serum homocysteine levels at baseline and at week 12, a better PASI 75 response rate at week 8, and a higher PASI 90 response rate at week 12 than the CT and CC genotype carriers. Multiple regression analysis demonstrated that serum homocysteine concentration at baseline was significantly associated with sex, weight, PASI score at baseline, and the rs1801133 genotype. The significant upregulation of serum homocysteine levels after treatment with methotrexate (MTX) was only observed in male CT and CC genotype carriers and female CC genotype carriers. In contrast, folic acid supplementation significantly decreased serum homocysteine levels after MTX treatment but only in male psoriatic patients. Conclusions: The effect of MTX on serum homocysteine levels was associated with the polymorphism of MTHFR rs1801133 and sex. Folic acid supplementation only decreased serum homocysteine levels in male psoriatic patients. [ABSTRACT FROM AUTHOR]

Detection of the Fusarium head blight (FHB) is crucial for wheat yield protection, with precise and rapid FHB detection increasing wheat yield and protecting the agricultural ecological environment. FHB detection tasks in agricultural production are currently handled by cloud servers and utilize unmanned aerial vehicles (UAVs). Hence, this paper proposed a lightweight model for wheat ear FHB detection based on UAV-enabled edge computing, aiming to achieve the purpose of intelligent prevention and control of agricultural disease. Our model utilized the You Only Look Once version 4 (YOLOv4) and MobileNet deep learning architectures and was applicable in edge devices, balancing accuracy, and FHB detection in real-time. Specifically, the backbone network Cross Stage Partial Darknet53 (CSPDarknet53) of YOLOv4 was replaced by a lightweight network, significantly decreasing the network parameters and the computing complexity. Additionally, we employed the Complete Intersection over Union (CIoU) and Non-Maximum Suppression (NMS) to regress the loss function to guarantee the detection accuracy of FHB. Furthermore, the loss function incorporated the focal loss to reduce the error caused by the unbalanced positive and negative sample distribution. Finally, mixed-up and transfer learning schemes enhanced the model's generalization ability. The experimental results demonstrated that the proposed model performed admirably well in detecting FHB of the wheat ear, with an accuracy of 93.69%, and it was somewhat better than the MobileNetv2-YOLOv4 model (F1 by 4%, AP by 3.5%, Recall by 4.1%, and Precision by 1.6%). Meanwhile, the suggested model was scaled down to a fifth of the size of the state-of-the-art object detection models. Overall, the proposed model could be deployed on UAVs so that wheat ear FHB detection results could be sent back to the end-users to intelligently decide in time, promoting the intelligent control of agricultural disease. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) are pivotal in various fields due to their rich spectral–spatial information. While convolutional neural networks (CNNs) have notably enhanced HSI classification, they often generate redundant spatial features. To address this, we introduce a novel HSI classification method, OMDSC, employing 3D Octave convolution combined with multiscale depthwise separable convolutional networks. This method initially utilizes 3D Octave convolution for efficient spectral–spatial feature extraction from HSIs, thereby reducing spatial redundancy. Subsequently, multiscale depthwise separable convolution is used to further improve the extraction of spatial features. Finally, the HSI classification results are output by softmax classifier. This work compares the method with other methods on three publicly available datasets in order to confirm its efficacy. The outcomes show that the method performs better in terms of classification. [ABSTRACT FROM AUTHOR]

Background: Annexin A6 (AnxA6) is a lipid-binding protein that regulates cholesterol homeostasis and secretory pathways. However, the correlation of AnxA6 polymorphism with lipometabolism has never been studied in psoriasis. Objectives: To investigate the impact of AnxA6 polymorphism on lipid profiles and the expression of AnxA6 protein in both peripheral blood mononuclear cells (PBMCs) and lipometabolism in psoriasis. Methods: A total of 265 psoriatic patients received methotrexate (MTX) treatment for 12 weeks, after which their lipid profiles were determined by measuring total cholesterol (TC), triglycerides (TGs), lipoprotein (a) [LP(a)], high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL), apolipoprotein (a)1 (ApoA1), and apolipoprotein B (ApoB). In addition, AnxA6 (rs11960458) was genotyped in 262 patients and the expression of AnxA6 in PBMCs was measured by Western blotting at baseline and week 8 post-MTX treatment. Results: The CC genotype carriers of rs11960458 had a lower expression of AnxA6 and lower levels of the pro-atherogenic lipids TC, LDL, and ApoB compared to TC genotype carriers. MTX significantly downregulated the levels of the anti-atherogenic lipids HDL-C and ApoA1 and the level of AnxA6 in TC genotype carriers, as well as the level of TGs in CC genotype carriers. Conclusions: The polymorphism of AnxA6, rs11960458, was statistically associated with the levels of pro-atherogenic lipids and with the downregulation of MTX on the levels of anti-atherogenic lipids and TGs in psoriasis. [ABSTRACT FROM AUTHOR]

In order to categorize feature classes by capturing subtle differences, hyperspectral images (HSIs) have been extensively used due to the rich spectral-spatial information. The 3D convolution-based neural networks (3DCNNs) have been widely used in HSI classification because of their powerful feature extraction capability. However, the 3DCNN-based HSI classification approach could only extract local features, and the feature maps it produces include a lot of spatial information redundancy, which lowers the classification accuracy. To solve the above problems, we proposed a spatial attention network (SATNet) by combining 3D OctConv and ViT. Firstly, 3D OctConv divided the feature maps into high-frequency maps and low-frequency maps to reduce spatial information redundancy. Secondly, the ViT model was used to obtain global features and effectively combine local-global features for classification. To verify the effectiveness of the method in the paper, a comparison with various mainstream methods on three publicly available datasets was performed, and the results showed the superiority of the proposed method in terms of classification evaluation performance. [ABSTRACT FROM AUTHOR]

Background: The co-inhibitory molecule B7-H4 is located in the genomic regions associated with type 1 diabetes (T1D) susceptibility. However, the correlation of B7-H4 with glycometabolism and dyslipidemia has never been studied. Objective: To explore the influence of B7-H4 polymorphism on the prevalence of diabetes mellitus (DM) and dyslipidemia in psoriasis. Methods: In this single-center cross-sectional study, we recruited 265 psoriatic patients receiving methotrexate (MTX) treatment. Thirteen single-nucleotide polymorphisms (SNPs) in B7-H4 were genotyped. Serum levels of total cholesterol (TC), triglycerides (TG), lipoprotein (a) (LP(a)), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein (LDL), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB) were measured at baseline and week 12. Results: The GG genotype carriers of rs12025144 in B7-H4 had a higher prevalence of DM (57.14% vs. 17.71% vs. 18.67%, p = 0.0018), and had a poorer response to MTX in diabetic patients (p < 0.05), compared with AA or AG genotype carriers. The AG genotype of rs2066398 was associated with higher levels of pro-atherogenic lipids. MTX significantly downregulated the level of anti-atherogenic lipid ApoA1 in AA genotype carriers of rs2066398. Conclusions: The genotypes rs12025144 and rs2066398 in B7-H4 were correlated with a higher prevalence of DM and dyslipidemia in psoriasis, respectively. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is one of the indicators measuring the growth of rice in the field. LAI monitoring plays an important role in ensuring the stable increase of grain yield. In this study, the canopy reflectance spectrum of rice was obtained by ASD at the elongation, booting, heading and post-flowering stages of rice, and the correlations between the original reflectance (OR), first-derivative transformation (FD), reciprocal transformation (1/R), and logarithmic transformation (LOG) with LAI were analyzed. Characteristic bands of spectral data were then selected based on the successive projections algorithm (SPA) and Pearson correlation. Moreover, ridge regression (RR), partial least squares (PLS), and multivariate stepwise regression (MSR) were conducted to establish estimation models based on characteristic bands and vegetation indices. The research results showed that the correlation between canopy spectrum and LAI was significantly improved after FD transformation. Modeling using SPA to select FD characteristic bands performed better than using Pearson correlation. The optimal modeling combination was FD-SPA-VI-RR, with the coefficient of determination (R2) of 0.807 and the root-mean-square error (RMSE) of 0.794 for the training set, R2 of 0.878 and RMSE of 0.773 for the validation set 1, and R2 of 0.705 and RMSE of 1.026 for the validation set 2. The results indicated that the present model may predict the rice LAI accurately, meeting the requirements of large-scale statistical monitoring of rice growth indicators in the field. [ABSTRACT FROM AUTHOR]