P2RBox: A Single Point is All You Need for Oriented Object Detection

Oriented object detection, a specialized subfield in computer vision, finds applications across diverse scenarios, excelling particularly when dealing with objects of arbitrary orientations. Conversely, point annotation, which treats objects as single points, offers a cost-effective alternative to rotated and horizontal bounding boxes but sacrifices performance due to the loss of size and orientation information. In this study, we introduce the P2RBox network, which leverages point annotations and a mask generator to create mask proposals, followed by filtration through our Inspector Module and Constrainer Module. This process selects high-quality masks, which are subsequently converted into rotated box annotations for training a fully supervised detector. Specifically, we've thoughtfully crafted an Inspector Module rooted in multi-instance learning principles to evaluate the semantic score of masks. We've also proposed a more robust mask quality assessment in conjunction with the Constrainer Module. Furthermore, we've introduced a Symmetry Axis Estimation (SAE) Module inspired by the spectral theorem for symmetric matrices to transform the top-performing mask proposal into rotated bounding boxes. P2RBox performs well with three fully supervised rotated object detectors: RetinaNet, Rotated FCOS, and Oriented R-CNN. By combining with Oriented R-CNN, P2RBox achieves 62.26% on DOTA-v1.0 test dataset. As far as we know, this is the first attempt at training an oriented object detector with point supervision

Microbial Properties Depending on Fertilization Regime in Agricultural Soils with Different Texture and Climate Conditions: A Meta-Analysis

Over-fertilization has a significant impact on soil microbial properties and its ecological environment. However, the effects of long-term fertilization on microbial properties on a large scale are still vague. This meta-analysis collected 6211 data points from 109 long-term experimental sites in China to evaluate the effects of fertilizer type and fertilization duration, as well as soil and climate conditions, on the effect sizes on various microbial properties and indices. The organic fertilizers combined with straw (NPKS) and manure (NPKM) had the highest effect sizes, while the chemical fertilizers N (sole N fertilizer) and NPK (NPK fertilizer) had the lowest. When compared with the control, NPKM treatment had the highest effect size, while N treatment had the lowest effect size on MBN (111% vs. 19%), PLFA (110% vs. −7%), fungi (88% vs. 43%), Actinomycetes (97% vs. 44%), urease (77% vs. 25%), catalase (15% vs. −11%), and phosphatase (58% vs. 4%). NPKM treatment had the highest while NPK treatment had the lowest effect size on bacteria (123% vs. 33%). NPKS treatment had the highest while N treatment had the lowest effect sizes on MBC (77% vs. 8%) and invertase (59% vs. 0.2%). NPKS treatment had the highest while NPK treatment had the lowest effect size on the Shannon index (5% vs. 1%). The effect sizes of NPKM treatment were the highest predominantly in arid regions because of the naturally low organic carbon in soils of these regions. The effect sizes on various microbial properties were also highly dependent on soil texture. In coarse-textured soils the effect sizes on MBC and MBN peaked sooner compared with those of clayey or silty soils, although various enzymes were most active in silty soils during the first 10 years of fertilization. Effect sizes on microbial properties were generally higher under NPKM and NPKS treatments than under NPK or N treatments, with considerable effects due to climate conditions. The optimal field fertilizer regime could be determined based on the effects of fertilizer type on soil microorganisms under various climate conditions and soil textures. This will contribute to the microbial biodiversity and soil health of agricultural land. Such controls should be used for adaptation of fertilization strategies to global changes

Cationic Polystyrene Resolves Nonalcoholic Steatohepatitis, Obesity, and Metabolic Disorders by Promoting Eubiosis of Gut Microbiota and Decreasing Endotoxemia.

A pandemic of metabolic diseases, consisting of type 2 diabetes, nonalcoholic fatty liver disease, and obesity, has imposed critical challenges for societies worldwide, prompting investigation of underlying mechanisms and exploration of low-cost and effective treatment. In this report, we demonstrate that metabolic disorders in mice generated by feeding with a high-fat diet without dietary vitamin D can be prevented by oral administration of polycationic amine resin. Oral administration of cholestyramine, but not the control uncharged polystyrene, was able to sequester negatively charged bacterial endotoxin in the gut, leading to 1) reduced plasma endotoxin levels, 2) resolved systemic inflammation and hepatic steatohepatitis, and 3) improved insulin sensitivity. Gut dysbiosis, characterized as an increase of the phylum Firmicutes and a decrease of Bacteroidetes and Akkermansia muciniphila, was fully corrected by cholestyramine, indicating that the negatively charged components in the gut are critical for the dysbiosis. Furthermore, fecal bacteria transplant, derived from cholestyramine-treated animals, was sufficient to antagonize the metabolic disorders of the recipient mice. These results indicate that the negatively charged components produced by dysbiosis are critical for biogenesis of metabolic disorders and also show a potential application of cationic polystyrene to treat metabolic disorders through promoting gut eubiosis

Extensive Classification of Visual Art Paintings for Enhancing Education System using Hybrid SVM-ANN with Sparse Metric Learning based on Kernel Regression

In recent decades, the collection of visual art paintings is large, digitized, and available for public uses that are rapidly growing. The development of multi-media systems is needed due to the huge amount of digitized artwork collections for retrieving and archiving this large-scale data. This multimedia system benefits from high-level tasks and has an essential step for measuring the similarity of visual between the artistic items. For modeling the similarities between the artworks or paintings, it is essential to extract useful features of visual paintings and propose the best approach for learning these similarity metrics. The infield of visual arts education, knowing the similarities and features, makes education more attractive by enhancing cognitive development in students. In this paper, the detailed visual features are listed, and the similarity measurement between the paintings is optimized by the Sparse Metric Learning-based Kernel Regression (KR-SML). A classification model is developed using hybrid SVM-ANN for semantic-level understanding to predict painting’s genre, artist, and style. Furthermore, the Human-Computer Interaction (HCI) based formulation model is built to analyze the proposed technique. The simulation results show that the proposed model is better in terms of performance than other existing techniques

Serum hsa-miR-98-5p and RORC may be new biomarkers related to esophageal cancer

This study aims to use bioinformatics methods to discover new serum miRNA markers for esophageal cancer, and provide a theoretical basis for early diagnosis of esophageal cancer. We used GEO2R to analyze the differential serum miRNAs in esophageal cancer based on GSE112264 from the GEO database. Then target genes of top 10 differential miRNAs were predicted. Obtain RNA-Seq data of esophageal cancer from the TCGA database, and use R software for analysis of differential expression. Overlap the predicted target genes with the differentially down-regulated genes, then perform analysis of GO and KEGG enrichment. Use GEPIA and UALCAN databases to perform verification of expression and prognostic analysis of key genes in the pathway. The results showed there are 2565 differential miRNAs in the serum of esophageal cancer patients. The top 10 up-regulated miRNAs predicted 1676 target genes, then 63 overlapped genes were obtained from target genes and 1642 down-regulated genes. GO enrichment obtained 14 biological processes, and KEGG enrichment obtained the circadian rhythm pathway. Only RORC is related to the poor prognosis of patients with esophageal cancer. Our study concluded serum hsa-miR-98-5p and its target gene RORC may be new biological markers for early diagnosis and treatment of esophageal cancer

Integration of aggregation-induced emission and delayed fluorescence into electronic donor–acceptor conjugates

A series of luminogens comprised electron donors and acceptors are found to possess two types of interesting photophysical processes of aggregation-induced emission (AIE) and delayed fluorescence. According to theory calculation, restriction of intramolecular motions accounts for their AIE characteristics. Moreover, a separated distribution of the HOMOs and the LUMOs of these luminogens leads to small DEST values and therefore delayed fluorescence

Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile

The kinematics of human movements and locomotion are closely linked to the activation and contractions of muscles. To investigate this, we present a multimodal dataset with benchmarks collected using a novel pair of Intelligent Knee Sleeves (Texavie MarsWear Knee Sleeves) for human pose estimation. Our system utilizes synchronized datasets that comprise time-series data from the Knee Sleeves and the corresponding ground truth labels from the visualized motion capture camera system. We employ these to generate 3D human models solely based on the wearable data of individuals performing different activities. We demonstrate the effectiveness of this camera-free system and machine learning algorithms in the assessment of various movements and exercises, including extension to unseen exercises and individuals. The results show an average error of 7.21 degrees across all eight lower body joints when compared to the ground truth, indicating the effectiveness and reliability of the Knee Sleeve system for the prediction of different lower body joints beyond the knees. The results enable human pose estimation in a seamless manner without being limited by visual occlusion or the field of view of cameras. Our results show the potential of multimodal wearable sensing in a variety of applications from home fitness to sports, healthcare, and physical rehabilitation focusing on pose and movement estimation.Comment: Accepted by Thirty-seventh Conference on Neural Information Processing Systems (Neurips) D&B Trac

A novel sulfur dioxide probe inhibits high glucose-induced endothelial cell senescence

Sulfur dioxide (SO2) is an important gas signal molecule produced in the cardiovascular system, so it has an important regulatory effect on human umbilical vascular endothelial cells (HUVECs). Studies have shown that high glucose (HG) has become the main cause of endothelial dysfunction and aging. However, the mechanism by which SO2 regulates the senescence of vascular endothelial cells induced by HG has not yet been clarified, so it is necessary to find effective tools to elucidate the effect of SO2 on senescence of HUVECs. In this paper, we identified a novel sulfur dioxide probe (2-(4-(dimethylamino)styryl)-1,1,3-trimethyl-1H-benzo [e]indol-3-ium, DLC) that inhibited the senescence of HUVECs. Our results suggested that DLC facilitated lipid droplets (LDs) translocation to lysosomes and triggered upregulation of LAMP1 protein levels by targeting LDs. Further study elucidated that DLC inhibited HG-induced HUVECs senescence by promoting the decomposition of LDs and protecting the proton channel of V-ATPase on lysosomes. In conclusion, our study revealed the regulatory effect of lipid droplet-targeted sulfur dioxide probes DLC on HG-induced HUVECs senescence. At the same time, it provided the new experimental evidence for elucidating the regulatory mechanism of intracellular gas signaling molecule sulfur dioxide on vascular endothelial fate

Intra-annual carbon fluxes and resource use efficiency of subtropical urban forests: insights from Chongming Island ecological observatory

Understanding the carbon budget within cities is crucial in the context of carbon peaking and carbon neutrality. This study investigates the carbon source-sink dynamics of urban forest ecosystems using carbon flux observations from the Chongming Island Ecological Observatory in Shanghai. The study aims to reveal the intra-annual variations of carbon fluxes and explore the changes in resource use efficiency of urban forest ecosystems within the framework of the big-leaf model. The results reveal distinct patterns in temperature (Tair), relative humidity (RH), radiation, and vapor pressure deficit (VPD). Diurnal cycles of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) exhibit seasonal variations, with higher amplitudes observed from April to September. The observed forest ecosystem acts as a moderate carbon sink (318.47 gC m−2 year−1), with the highest carbon uptake occurring in May and the highest carbon emission in February. During the growing season, the total carbon sink was 225.37 gC m−2, composed of GPP 1337.01 gC m−2 and Reco 1111.64 gC m−2. Water-use efficiency (WUE) and light-use efficiency (LUE) exhibit seasonal variations, while carbon-use efficiency (CUE) declines after May. These findings contribute to our understanding of urban forest carbon dynamics and their potential role in carbon management strategies