DiffPose:SpatioTemporal Diffusion Model for Video-Based Human Pose Estimation

Denoising diffusion probabilistic models that were initially proposed for realistic image generation have recently shown success in various perception tasks (e.g., object detection and image segmentation) and are increasingly gaining attention in computer vision. However, extending such models to multi-frame human pose estimation is non-trivial due to the presence of the additional temporal dimension in videos. More importantly, learning representations that focus on keypoint regions is crucial for accurate localization of human joints. Nevertheless, the adaptation of the diffusion-based methods remains unclear on how to achieve such objective. In this paper, we present DiffPose, a novel diffusion architecture that formulates video-based human pose estimation as a conditional heatmap generation problem. First, to better leverage temporal information, we propose SpatioTemporal Representation Learner which aggregates visual evidences across frames and uses the resulting features in each denoising step as a condition. In addition, we present a mechanism called Lookup-based MultiScale Feature Interaction that determines the correlations between local joints and global contexts across multiple scales. This mechanism generates delicate representations that focus on keypoint regions. Altogether, by extending diffusion models, we show two unique characteristics from DiffPose on pose estimation task: (i) the ability to combine multiple sets of pose estimates to improve prediction accuracy, particularly for challenging joints, and (ii) the ability to adjust the number of iterative steps for feature refinement without retraining the model. DiffPose sets new state-of-the-art results on three benchmarks: PoseTrack2017, PoseTrack2018, and PoseTrack21

Comparative Transcriptome Analysis Between Resistant and Susceptible Rice Cultivars Responding to Striped Stem Borer (SSB), Chilo suppressalis (Walker) Infestation

The striped stem borer, Chilo suppressalis (Walker), is a notorious pest of rice that causes large losses in China. Breeding and screening of resistance rice cultivars are effective strategies for C. suppressalis management. In this study, insect-resistant traits of 47 rice cultivars were investigated by C. suppressalis artificial infestation (AI) both in field and greenhouse experiments, using the susceptible (S) cultivar 1665 as a control. Results suggest that two rice cultivars, namely 1688 and 1654, are resistant (R) and moderately resistant (MR) to C. suppressalis, respectively. Then, a comparative transcriptome (RNA-Seq) was de novo assembled and differentially expressed genes (DEGs) with altered expression levels were investigated among cultivars 1688, 1654, and 1665, with or without C. suppressalis infestation for 24 h. A total of 2569 and 1861 genes were up-regulated, and 3852 and 1861 genes were down-regulated in cultivars 1688 and 1654, respectively after artificial infestation with C. suppressalis compared to the non-infested control (CK). For the susceptible cultivar 1665, a total of 882 genes were up-regulated and 3863 genes were down-regulated after artificial infestation with C. suppressalis compared to the CK. Twenty four DEGs belong to proteinase inhibitor, lectin and chitinase gene families; plant hormone signal transduction and plant-pathogen interaction pathways were selected as candidate genes to test their possible role in C. suppressalis resistance. RT-qPCR results revealed that 13 genes were significantly up-regulated and 8 were significantly down-regulated in the resistant cultivar 1688 with C. suppressalis artificial infestation (1688AI) compared to the CK. Three genes, LTPL164, LTPL151, and LOC Os11g32100, showed more than a 10-fold higher expression in 1688AI than in 1688CK, suggesting their potential role in insect resistance. Overall, our results provide an important foundation for further understanding the insect resistance mechanisms of selected resistant varieties that will help us to breed C. suppressalis resistant rice varieties

Hydroxychloroquine improves pregnancy outcomes of women with positive antinuclear antibody spectrum test results

Background:Empirical use of Hydroxychloroquine (HCQ) in patients with positive antinuclear antibody spectrum (ANAs) test result is controversial regarding its impact on improving perinatal outcomes. This study aimed to investigate the effect of HCQ on adverse pregnancy outcomes associated with placental dysfunction in ANAs-positive patients.Methods:The study included pregnant women with positive ANAs test result from 2016 to 2020 in our center, and divided into a weakly positive and a positive group in just ANA positive patients among them. Univariate and multivariate analyses were conducted to determine the effect of HCQ on pregnancy outcomes in each subgroup. Stratified and interactive analyses were performed to assess the value of HCQ in improving pregnancy outcomes.Results:(i) A total of 261 cases were included, accounting for 30.60% of pregnancy complicated with autoimmune abnormalities, and 65.12% of them used HCQ during pregnancy. (ii) The application of HCQ significantly reduced the incidence of early-onset preeclampsia (1.18% vs. 12.09%, p = 0.040) and small-for-gestational-age infants (10.06% vs. 25.84%, p = 0.003) in the ANAs-positive population, increased birth weight (3075.87 ± 603.91 g vs. 2847.53 ± 773.73 g, p = 0.025), and prolonged gestation (38.43 ± 2.31 vs. 36.34 ± 5.45 weeks, p < 0.001). (iii) A total of 185 just ANA-positive patients were stratified according to titers. Among them, the rate of HCQ usage was significantly higher than that in the weakly positive group (81.03% vs. 58.27%, p = 0.003). (vi) Stratified univariate analysis showed that HCQ usage in the ANA-positive group could reduce the incidence of preeclampsia (2.13% vs. 27.27%, p = 0.019) and prolong gestation (38.29 ± 2.54 vs. 34.48 ± 7.68 weeks, p = 0.006). In the ANA-weakly positive group, HCQ significantly reduced the incidence of preeclampsia (6.76% vs. 28.30%, p = 0.002), early-onset preeclampsia (1.35% vs. 13.21%, p = 0.027), and small-for-gestational-age infants (7.89% vs. 35.19%, p < 0.001). Multivariate regression analysis showed that HCQ significantly reduced the incidence of preeclampsia in both groups. Intergroup interaction analysis showed no significant difference in the value of HCQ in reducing the incidence of preeclampsia between the two groups.Conclusion:ANAs positivity is an important abnormal autoimmunity type in pregnancy. HCQ can be considered as a choice for improving adverse pregnancy outcomes related to placental dysfunction, such as preeclampsia, in this population

Raman Spectroscopy for Pharmaceutical Quantitative Analysis by Low-Rank Estimation

Raman spectroscopy has been widely used for quantitative analysis in biomedical and pharmaceutical applications. However, the signal-to-noise ratio (SNR) of Raman spectra is always poor due to weak Raman scattering. The noise in Raman spectral dataset will limit the accuracy of quantitative analysis. Because of high correlations in the spectral signatures, Raman spectra have the low-rank property, which can be used as a constraint to improve Raman spectral SNR. In this paper, a simple and feasible Raman spectroscopic analysis method by Low-Rank Estimation (LRE) is proposed. The Frank-Wolfe (FW) algorithm is applied in the LRE method to seek the optimal solution. The proposed method is used for the quantitative analysis of pharmaceutical mixtures. The accuracy and robustness of Partial Least Squares (PLS) and Support Vector Machine (SVM) chemometric models can be improved by the LRE method

An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses

ObjectivesThis study was conducted in order to develop and validate an ultrasonic-based radiomics nomogram for diagnosing solid renal masses.MethodsSix hundred renal solid masses with benign renal lesions (n = 204) and malignant renal tumors (n = 396) were divided into a training set (n = 480) and a validation set (n = 120). Radiomics features were extracted from ultrasound (US) images preoperatively and then a radiomics score (RadScore) was calculated. By integrating the RadScore and independent clinical factors, a radiomics nomogram was constructed. The diagnostic performance of junior physician, senior physician, RadScore, and radiomics nomogram in identifying benign from malignant solid renal masses was evaluated based on the area under the receiver operating characteristic curve (ROC) in both the training and validation sets. The clinical usefulness of the nomogram was assessed using decision curve analysis (DCA).ResultsThe radiomics signature model showed satisfactory discrimination in the training set [area under the ROC (AUC), 0.887; 95% confidence interval (CI), 0.860–0.915] and the validation set (AUC, 0.874; 95% CI, 0.816–0.932). The radiomics nomogram also demonstrated good calibration and discrimination in the training set (AUC, 0.911; 95% CI, 0.886–0.936) and the validation set (AUC, 0.861; 95% CI, 0.802–0.921). In addition, the radiomics nomogram model showed higher accuracy in discriminating benign and malignant renal masses compared with the evaluations by junior physician (DeLong p = 0.004), and the model also showed significantly higher specificity than the senior and junior physicians (0.93 vs. 0.57 vs. 0.46).ConclusionsThe ultrasonic-based radiomics nomogram shows favorable predictive efficacy in differentiating solid renal masses