An Empirical Study of CLIP for Text-based Person Search

Text-based Person Search (TBPS) aims to retrieve the person images using natural language descriptions. Recently, Contrastive Language Image Pretraining (CLIP), a universal large cross-modal vision-language pre-training model, has remarkably performed over various cross-modal downstream tasks due to its powerful cross-modal semantic learning capacity. TPBS, as a fine-grained cross-modal retrieval task, is also facing the rise of research on the CLIP-based TBPS. In order to explore the potential of the visual-language pre-training model for downstream TBPS tasks, this paper makes the first attempt to conduct a comprehensive empirical study of CLIP for TBPS and thus contribute a straightforward, incremental, yet strong TBPS-CLIP baseline to the TBPS community. We revisit critical design considerations under CLIP, including data augmentation and loss function. The model, with the aforementioned designs and practical training tricks, can attain satisfactory performance without any sophisticated modules. Also, we conduct the probing experiments of TBPS-CLIP in model generalization and model compression, demonstrating the effectiveness of TBPS-CLIP from various aspects. This work is expected to provide empirical insights and highlight future CLIP-based TBPS research.Comment: 13 pages, 5 fiugres and 17 tables. Code is available at https://github.com/Flame-Chasers/TBPS-CLI

Can fecal calprotectin accurately identify histological activity of ulcerative colitis? A meta-analysis

Background and Aims: Elevated fecal calprotectin (FC) levels have been reported to correlate with histological activity in patients with ulcerative colitis (UC). However, the accuracy of FC for evaluating histological activity of UC remains to be determined. The aim of this study was to determine the accuracy of FC for evaluating histological activity of UC, based on updated definitions. Methods: Related studies were retrieved from the PubMed, Web of Science, Embase, and Cochrane databases. Adult participants diagnosed with UC were included when sufficient data could be extracted to calculate the accuracy of FC for evaluating histological activity. The primary outcome was histological response, and the secondary outcome was histological remission, defined according to a recently updated position paper of European Crohn’s and Colitis Organization. Statistics were pooled using bivariate mixed-effects models. The area under the curve was estimated by summary receiver-operating characteristic curves. Results: Nine studies were included, from which 1039 patients were included for the analysis of histological response and 591 patients for histological remission. For the evaluation of histological response, the pooled sensitivity, specificity, and the area under the curve were 0.69 [95% confidence interval (CI): 0.52–0.82], 0.77 (95% CI: 0.63–0.87), and 0.80 (95% CI: 0.76–0.83), respectively. For the evaluation of histological remission, the corresponding estimates were 0.76 (95% CI: 0.71–0.81), 0.71 (95% CI: 0.62–0.78), and 0.79 (95% CI: 0.75–0.82), respectively. FC had a higher accuracy in studies using Nancy Index. For histological response, the cut-off values of FC ranged from 50 to 172 µg/g, and the sensitivity was higher in studies with FC cut-off values >100 µg/g (0.77 versus 0.65). Conclusion: FC is a valuable biomarker for assessing histological activity in patients with UC. A cut-off value of 100–200 µg/g is more appropriate to spare patients from an unnecessary endoscopy and biopsy

Association between AGTR1 (c.1166 A>C) Polymorphisms and Kidney Injury in Hypertension

Background: High blood pressure is the main cause of cardiovascular diseases. Kidney damage is one of the most common organ secondary damage to hypertension. The study of hypertension gene polymorphisms is an important means of precision treatment of primary hypertension. Objectives: The objective of this study was to explore the relationship between AGTR1 (c.1166 A>C) gene polymorphisms and hypertension combined with kidney damage, while exploring the relationship between codominant, dominant and recessive gene model and hypertension with kidney injury and the susceptibility of different genotypes to hypertension with kidney injury. Methods: The distribution of AGTR1 polymorphism in the AGTR1 in hypertensive patients (hypertension group, 292 patients) and hypertension with kidney injury patients (44 patients) were detected and compared by PCR-melting curve method. Results: The genotype distribution of hypertension and combined groups met Hardy-Weinberg equilibrium (p > 0.05); the distribution difference between the three genotypes was statistically significant (p 0.05). Conclusions: Our study identified the relationship of AGTRA (c.1166 A>C) with hypertension combined with renal injury, and compared the susceptibility of different genetic models, which may provide novel targets for precision gene therapy of hypertension. Clinical Trial Registration: URL: https://www.chictr.org.cn/indexEN.html; Unique identifier: ChiCTR2100051472

PointNest: Learning Deep Multiscale Nested Feature Propagation for Semantic Segmentation of 3-D Point Clouds

3-D point cloud semantic segmentation is a fundamental task for scene understanding, but this task remains challenging due to the diverse scene classes, data defects, and occlusions. Most existing deep learning-based methods focus on new designs of feature extraction operators but neglect the importance of exploiting multiscale point information in the network, which is crucial for identifying objects under complex scenes. To tackle this limitation, we propose an innovative network called PointNest that efficiently learns multiscale point feature propagation for accurate point segmentation. PointNest employs a deep nested U-shape encoder–decoder architecture, where the encoder learns multiscale point features through nested feature aggregation units at different network depths and propagates local geometric contextual information with skip connections along horizontal and vertical directions. The decoder then receives multiscale nested features from the encoder to progressively recover geometric details of the abstracted decoding point features for pointwise semantic prediction. In addition, we introduce a deep supervision strategy to further promote multiscale information propagation in the network for efficient training and performance improvement. Experiments on three public benchmarks demonstrate that PointNest outperforms existing mainstream methods with the mean intersection over union scores of 68.8%, 74.7%, and 62.7% in S3DIS, Toronto-3D, and WHU-MLS datasets, respectively

Regulation of the Rhythmic Emission of Plant Volatiles by the Circadian Clock

Like other organisms, plants have endogenous biological clocks that enable them to organize their metabolic, physiological, and developmental processes. The representative biological clock is the circadian system that regulates daily (24-h) rhythms. Circadian-regulated changes in growth have been observed in numerous plants. Evidence from many recent studies indicates that the circadian clock regulates a multitude of factors that affect plant metabolites, especially emitted volatiles that have important ecological functions. Here, we review recent progress in research on plant volatiles showing rhythmic emission under the regulation of the circadian clock, and on how the circadian clock controls the rhythmic emission of plant volatiles. We also discuss the potential impact of other factors on the circadian rhythmic emission of plant volatiles

Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data

Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data

RG-GCN: A Random Graph Based on Graph Convolution Network for Point Cloud Semantic Segmentation

Point cloud semantic segmentation, a challenging task in 3D data processing, is popular in many realistic applications. Currently, deep learning methods are gradually being applied to point cloud semantic segmentation. However, as it is difficult to manually label point clouds in 3D scenes, it remains difficult to obtain sufficient training samples for the supervised deep learning network. Although an increasing number of excellent methods have been proposed in recent years, few of these have focused on the problem of semantic segmentation with insufficient samples. To address this problem, this paper proposes a random graph based on graph convolution network, referred to as RG-GCN. The proposed network consists of two key components: (1) a random graph module is proposed to perform data augmentation by changing the topology of the built graphs; and (2) a feature extraction module is proposed to obtain local significant features by aggregating point spatial information and multidimensional features. To validate the performance of the RG-GCN, the indoor dataset S3DIS and outdoor dataset Toronto3D are used to validate the proposed network via a series of experiments. The results show that the proposed network achieves excellent performance for point cloud semantic segmentation of the two different datasets

Occurrence of Functional Molecules in the Flowers of Tea (Camellia sinensis) Plants: Evidence for a Second Resource

Tea (Camellia sinensis) is an important crop, and its leaves are used to make the most widely consumed beverage, aside from water. People have been using leaves from tea plants to make teas for a long time. However, less attention has been paid to the flowers of tea plants, which is a waste of an abundant resource. In the past 15 years, researchers have attempted to discover, identify, and evaluate functional molecules from tea flowers, and have made insightful and useful discoveries. Here, we summarize the recent investigations into these functional molecules in tea flowers, including functional molecules similar to those in tea leaves, as well as the preponderant functional molecules in tea flowers. Tea flowers contain representative metabolites similar to those of tea leaves, such as catechins, flavonols, caffeine, and amino acids. The preponderant functional molecules in tea flowers include saponins, polysaccharides, aromatic compounds, spermidine derivatives, and functional proteins. We also review the safety and biological functions of tea flowers. Tea flower extracts are proposed to be of no toxicological concern based on evidence from the evaluation of mutagenicity, and acute and subchronic toxicity in rats. The presence of many functional metabolites in tea flowers indicates that tea flowers possess diverse biological functions, which are mostly related to catechins, polysaccharides, and saponins. Finally, we discuss the potential for, and challenges facing, future applications of tea flowers as a second resource from tea plants