A pulse crop dataset of agronomic traits and multispectral images from multiple environments.

Crop yield potential in breeding trials can be captured using unmanned aerial vehicle (UAV) based multispectral imagery. Several digital traits or phenotypes such as vegetation indices can represent canopy crop vigor and overall plant health, which can be used to evaluate differences in performance across varieties in crop breeding programs. This dataset contains agronomic data for named cultivars and breeding lines of spring-sown dry pea and chickpea, and over 275 multispectral images from advanced and preliminary breeding trials. The breeding trials were located at three locations in the "Palouse" region of Eastern Washington and Northern Idaho of the United States across 2017, 2018 and 2019 cropping seasons. The multispectral images were captured using a UAV integrated with a 5-band multispectral camera at multiple time points from early vegetative growth through pod development stages during each cropping season. This dataset details seed yield information from trials of dry peas and chickpea that were obtained from each location, as well as additional agronomic and phenological data recorded at one location (mostly Pullman, WA) for each cropping season. The dataset also includes 20-78 megabytes (MB) Tagged Image Format (TIF) uncalibrated stitched orthomosaic images generated from the photogrammetric software. The images can be processed using any convenient image processing algorithm to obtain vegetation indices and other useful information.

High-Throughput Extraction of Seed Traits Using Image Acquisition and Analysis.

Seed traits can easily be assessed using image processing tools to evaluate differences in crop variety performances in response to environment and stress. In this chapter, we describe a protocol to measure seed traits that can be applied to crops with small grains, including legume grains with little modification. The imaging processing tool can be applied to process a batch of images without human intervention. The method allows evaluation of geometric and color features, and currently extracts 11 seed traits that include number of seeds, seed area, major axis, minor axis, eccentricity, and mean and standard deviation of reflectance in red, green, and blue channels from seed images. Protocols or methods, including the one described in this chapter, facilitate phenotyping seed traits in a high-throughput and automated manner, which can be applied in plant breeding programs and food processing industry to evaluate seed quality.

Biogenic VOCs Emission Profiles Associated with Plant-Pest Interaction for Phenotyping Applications.

Pest attacks on plants can substantially change plants' volatile organic compounds (VOCs) emission profiles. Comparison of VOC emission profiles between non-infected/non-infested and infected/infested plants, as well as resistant and susceptible plant cultivars, may provide cues for a deeper understanding of plant-pest interactions and associated resistance. Furthermore, the identification of biomarkers-specific biogenic VOCs-associated with the resistance can serve as a non-destructive and rapid tool for phenotyping applications. This research aims to compare the VOCs emission profiles under diverse conditions to identify constitutive (also referred to as green VOCs) and induced (resulting from biotic/abiotic stress) VOCs released in potatoes and wheat. In the first study, wild potato Solanum bulbocastanum (accession# 22; SB22) was inoculated with Meloidogyne chitwoodi race 1 (Mc1), and Mc1 pathotype Roza (SB22 is resistant to Mc1 and susceptible to pathotype Roza), and VOCs emission profiles were collected using gas chromatography-flame ionization detection (GC-FID) at different time points. Similarly, in the second study, the VOCs emission profiles of resistant ('Hollis') and susceptible ('Alturas') wheat cultivars infested with Hessian fly insects were evaluated using the GC-FID system. In both studies, in addition to variable plant responses (susceptibility to pests), control treatments (non-inoculated or non-infested) were used to compare the VOCs emission profiles resulting from differences in stress conditions. The common VOC peaks (constitutive VOCs) between control and infected/infested samples, and unique VOC peaks (induced VOCs) presented only in infected/infested samples were analyzed. In the potato-nematode study, the highest unique peak was found two days after inoculation (DAI) for SB22 inoculated with Mc1 (resistance response). The most common VOC peaks in SB22 inoculated with both Mc1 and Roza were found at 5 and 10 DAI. In the wheat-insect study, only the Hollis showed unique VOC peaks. Interestingly, both cultivars released the same common VOCs between control and infected samples, with only a difference in VOC average peak intensity at 22.4 min retention time where the average intensity was 4.3 times higher in the infested samples of Hollis than infested samples of Alturas. These studies demonstrate the potential of plant VOCs to serve as a rapid phenotyping tool to assess resistance levels in different crops.

Diagnosis of Early Cervical Cancer with a Multimodal Magnetic Resonance Image under the Artificial Intelligence Algorithm.

This research was conducted to explore the value of multimodal magnetic resonance imaging (MRI) based on the alternating direction algorithm in the diagnosis of early cervical cancer. 64 patients diagnosed with early cervical cancer clinicopathologically were included, and according to the examination methods, they were divided into A group with conventional multimodal MRI examination and B group with the multimodal MRI examination under the alternating direction algorithm. The diagnostic results of two types of multimodal MRI for early cervical cancer staging were compared with the results of clinicopathological examination to judge the application value in the early diagnosis of cervical cancer. The results showed that in the 6 randomly selected samples of early cervical cancer patients, the peak signal-to-noise ratio (PSNR) and structural similarity image measurement (SSIM) of multimodal MRI images under the alternating direction algorithm were significantly higher than those of conventional multimodal MRI images and the image reconstruction was clearer under this algorithm. By comparing MRI multimodal staging, statistical analysis showed that the staging accuracy of B group was 75%, while that of A group was only 59.38%. For the results of postoperative medical examinations, the examination consistency of B group was better than that of A group, with a statistically significant difference (P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) of B group was larger than that of A group; thus, sensitivity was improved and misdiagnosis was reduced significantly. Multimodal MRI under the alternating direction algorithm was superior to conventional multimodal MRI examination in the diagnosis of early cervical cancer, as the lesions were displayed more clearly, which was conducive to the detection rate of small lesions and the staging accuracy. Therefore, it could be used as an ideal MRI method for the assistant diagnosis of cervical cancer staging.

A novel salt-tolerant strain Trichoderma atroviride HN082102.1 isolated from marine habitat alleviates salt stress and diminishes cucumber root rot caused by Fusarium oxysporum.

BACKGROUND: Salt stress threaten the growth of plants, and even aggravate plant disease. In this article, salt-tolerant Trichoderma strain was isolated, and its potential to alleviate salt stress and diminish cucumber root rot caused by Fusarium oxysporum was evaluated. RESULTS: Twenty-seven Trichoderma isolates were isolated from samples of sea muds and algae collected from the South Sea of China. Among these, the isolate HN082102.1 showed the most excellent salt tolerance and antagonistic activity against F. oxysporum causing root rot in cucumber and was identified as T. atroviride. Its antagonism ability may be due to mycoparasitism and inhibition effect of volatile substances. The application of Trichoderma mitigated the adverse effects of salt stress and promoted the growth of cucumber under 100 mM and 200 mM NaCl, especially for the root. When T. atroviride HN082102.1 was applied, root fresh weights increased by 92.55 and 84.86%, respectively, and root dry weights increased by 75.71 and 53.31%, respectively. Meanwhile, the application of HN082102.1 reduced the disease index of cucumber root rot by 63.64 and 71.01% under 100- and 0-mM saline conditions, respectively, indicating that this isolate could inhibit cucumber root rot under salt stress. CONCLUSIONS: This is the first report of salt-tolerant T. atroviride isolated from marine habitat showing antagonistic activity to F. oxysporum, and the results provide evidence for the novel strain T. atroviride HN082102.1 in alleviating salt stress and diminishing cucumber root rot, indicating that T. atroviride strain HN082102.1 can be used as biological control agent in saline alkali land.

Study on the Differences of Chemical Structures and Pyrolysis Characteristics between the Jurassic and Carboniferous Coking Coals.

Using Jurassic coking coals and Carboniferous coking coals as raw materials, carbonization experiments were carried out on the cokes produced by them in a self-made furnace in a laboratory-scale coking furnace, finding that the coke quality of the Jurassic fat coals and coking coals was obviously inferior to that of the Carboniferous coking coals of the same brand. In this study, the reasons for this phenomenon were studied by elemental analysis, Fourier transform infrared spectroscopy analysis, and thermogravimetric analysis of experimental coal samples and by combining the differences in chemical structures of experimental coal samples with pyrolysis characteristic parameters. It was found that the key factor affecting the quality of cokes made from the Jurassic fat coals, coking coals, and highly volatile coking coals was that the coals contained too many oxygen-containing functional groups, which were decomposed into reactive oxygen species in the main pyrolysis stage of coal. These reactive oxygen species would consume too much free-moving hydrogen and then trigger a large number of condensation and cross-linking reactions, resulting in poor plastic mass and coke quality.

Irisin Activates M1 Macrophage and Suppresses Th2-Type Immune Response in Rats with Pelvic Inflammatory Disease.

OBJECTIVE: To investigate the mechanism of irisin to treat rats with acute pelvic inflammatory disease (APID). METHODS: Female rats were established as APID. Firstly, the content of IL-6, IL-8, TNF-α, and NF-kB was tested in rats' serum by enzyme linked immunosorbent assay (ELISA). Interferon-γ (IFN-γ) and IL-4 in the supernatant of pelvic homogenates were also detected. The mRNA expression of the inducible nitric oxide synthase (iNOS), TNF-α, chemokine ligand 1 (CXCL1), arginase-1(Arg1), and chitinase-3-like-3 (Chi313) genes in the pelvic cavity was detected by quantitative reverse transcription polymerase chain reaction (RT-qPCR). IFN-γ and IL-4 secreted by spleen CD4+T cells and CD8+T cells were counted by flow cytometry, and the ratio of IFN-γ/IL-4 in CD4+T cells and CD8+T cells in the spleen was also detected by flow cytometry. RESULTS: Irisin reduced the levels of IL-6, IL-8, TNF-α, and NF-kB in serum. Compared with the APID group, the expression level of IL-4 in the APID + Irisin group was reduced in the homogenate. At the same time, Irisin promotes the activation of M1 macrophages in the uterus, ovaries, and uterine tubes of rats with APID. Irisin also inhibited Th2-type immune response. CONCLUSIONS: Irisin activates M1 macrophage and suppresses Th2-type immune response in APID rats.

Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.

Introduction: Tar spot is a high-profile disease, causing various degrees of yield losses on corn (Zea mays L.) in several countries throughout the Americas. Disease symptoms usually appear at the lower canopy in corn fields with a history of tar spot infection, making it difficult to monitor the disease with unmanned aircraft systems (UAS) because of occlusion. Methods: UAS-based multispectral imaging and machine learning were used to monitor tar spot at different canopy and temporal levels and extract epidemiological parameters from multiple treatments. Disease severity was assessed visually at three canopy levels within micro-plots, while aerial images were gathered by UASs equipped with multispectral cameras. Both disease severity and multispectral images were collected from five to eleven time points each year for two years. Image-based features, such as single-band reflectance, vegetation indices (VIs), and their statistics, were extracted from ortho-mosaic images and used as inputs for machine learning to develop disease quantification models. Results and discussion: The developed models showed encouraging performance in estimating disease severity at different canopy levels in both years (coefficient of determination up to 0.93 and Lin's concordance correlation coefficient up to 0.97). Epidemiological parameters, including initial disease severity or y0 and area under the disease progress curve, were modeled using data derived from multispectral imaging. In addition, results illustrated that digital phenotyping technologies could be used to monitor the onset of tar spot when disease severity is relatively low (< 1%) and evaluate the efficacy of disease management tactics under micro-plot conditions. Further studies are required to apply and validate our methods to large corn fields.

Screening and Identification of Trichoderma Strains isolated from Natural Habitats in China with Potential Agricultural Applications.

Trichoderma spp. are widely distributed in natural habitats and have been evaluated as a potential biocontrol agent (BCA) for disease control and plant growth promotion. In this study, 1308 Trichoderma strains were obtained from the plant rhizosphere soil, above-ground plants, and decaying wood from natural habitats in China. Among them, 49 Trichoderma strains showed a good inhibitory effect, especially against Botrytis cinerea, Fusarium oxysporum, and Colletotrichum gloeosporioides with inhibition rate above 85% in the dual culture test. Among these 49 strains, the 13 strains with broad-spectrum inhibitory effects also significantly promoted the seed germination of five crops (rice, cucumber, tomato, melon, and pakchoi) and root growth of four crop seedlings (watermelon, tomato, eggplant, and chili). Furthermore, these strains showed effective colonization in the rhizosphere and root of cucumber. Trichoderma strains SC012 and NX043 showed the highest chitinase and ß-1,3-glucanase activity among all strains. Based on the morphological characterization and phylogenetic analysis of the nuclear ribosomal internal transcribed spacer (ITS) and translation elongation factor 1 (tef1), twelve Trichoderma strains were identified as Trichoderma asperellum and one as Trichoderma afroharzianum. This study suggests that the 13 Trichoderma strains are promising BCAs and could be developed as biofertilizers and biological pesticides for agricultural applications.

The Combination of a Biocontrol Agent Trichoderma asperellum SC012 and Hymexazol Reduces the Effective Fungicide Dose to Control Fusarium Wilt in Cowpea.

The use of synthetic fungicide needs to be gradually reduced because of its adverse effect on human health and the environment. An integrated approach combining fungicides with biological control agents (BCAs) can be used to reduce the fungicide doses, thereby minimizing the risks associated with chemical fungicides. In this study, the combined application of a BCA Trichoderma and a fungicide hymexazol was used to manage the cowpea wilt disease caused by Fusarium oxysporum. The Trichoderma SC012 strain, which is resistant to hymexazol, was screened out and identified as T. asperellum. T. asperellum SC012 showed hyperparasitism to F. oxysporum and could penetrate and encircle the hyphae of pathogen on a medium amended or not with hymexazol. When combined with hymexazol, the population density in the rhizosphere soil of cowpea showed no significant difference compared with the treatment Trichoderma used alone. When the concentration of T. asperellum SC012 or hymexazol was halved, their combined application could control cowpea wilt disease more effectively than their individual use. The findings showed that the combination of Trichoderma and hymexazol could reduce the use of chemical fungicide, which is eco-friendly and may be an important part of integrated control of Fusarium wilt in cowpea.

Crop Performance Evaluation of Chickpea and Dry Pea Breeding Lines Across Seasons and Locations Using Phenomics Data.

The Pacific Northwest is an important pulse production region in the United States. Currently, pulse crop (chickpea, lentil, and dry pea) breeders rely on traditional phenotyping approaches to collect performance and agronomic data to support decision making. Traditional phenotyping poses constraints on data availability (e.g., number of locations and frequency of data acquisition) and throughput. In this study, phenomics technologies were applied to evaluate the performance and agronomic traits in two pulse (chickpea and dry pea) breeding programs using data acquired over multiple seasons and locations. An unmanned aerial vehicle-based multispectral imaging system was employed to acquire image data of chickpea and dry pea advanced yield trials from three locations during 2017-2019. The images were analyzed semi-automatically with custom image processing algorithm and features were extracted, such as canopy area and summary statistics associated with vegetation indices. The study demonstrated significant correlations (P < 0.05) between image-based features (e.g., canopy area and sum normalized difference vegetation index) with yield (r up to 0.93 and 0.85 for chickpea and dry pea, respectively), days to 50% flowering (r up to 0.76 and 0.85, respectively), and days to physiological maturity (r up to 0.58 and 0.84, respectively). Using image-based features as predictors, seed yield was estimated using least absolute shrinkage and selection operator regression models, during which, coefficients of determination as high as 0.91 and 0.80 during model testing for chickpea and dry pea, respectively, were achieved. The study demonstrated the feasibility to monitor agronomic traits and predict seed yield in chickpea and dry pea breeding trials across multiple locations and seasons using phenomics tools. Phenomics technologies can assist plant breeders to evaluate the performance of breeding materials more efficiently and accelerate breeding programs.

Effects of Boron Carbide on Coking Behavior and Chemical Structure of High Volatile Coking Coal during Carbonization.

Modified cokes with improved resistance to CO2 reaction were produced from a high volatile coking coal (HVC) and different concentrations of boron carbide (B4C) in a laboratory scale coking furnace. This paper focuses on modification mechanism about the influence of B4C on coking behavior and chemical structure during HVC carbonization. The former was studied by using a thermo-gravimetric analyzer. For the latter, four semi-cokes prepared from carbonization tests for HVC with or without B4C at 450 °C and 750 °C, respectively, were analyzed by using Fourier transform infrared spectrum and high-resolution transmission electron microscopy technologies. It was found that B4C will retard extensive condensation and crosslinking reactions by reducing the amount of active oxygen obtained from thermally produced free radicals and increase secondary cracking reactions, resulting in increasing size of aromatic layer and anisotropic degree in coke structure, which eventually improves the coke quality.

Dissecting the Genetic Architecture of Aphanomyces Root Rot Resistance in Lentil by QTL Mapping and Genome-Wide Association Study.

Lentil (Lens culinaris Medikus) is an important source of protein for people in developing countries. Aphanomyces root rot (ARR) has emerged as one of the most devastating diseases affecting lentil production. In this study, we applied two complementary quantitative trait loci (QTL) analysis approaches to unravel the genetic architecture underlying this complex trait. A recombinant inbred line (RIL) population and an association mapping population were genotyped using genotyping by sequencing (GBS) to discover novel single nucleotide polymorphisms (SNPs). QTL mapping identified 19 QTL associated with ARR resistance, while association mapping detected 38 QTL and highlighted accumulation of favorable haplotypes in most of the resistant accessions. Seven QTL clusters were discovered on six chromosomes, and 15 putative genes were identified within the QTL clusters. To validate QTL mapping and genome-wide association study (GWAS) results, expression analysis of five selected genes was conducted on partially resistant and susceptible accessions. Three of the genes were differentially expressed at early stages of infection, two of which may be associated with ARR resistance. Our findings provide valuable insight into the genetic control of ARR, and genetic and genomic resources developed here can be used to accelerate development of lentil cultivars with high levels of partial resistance to ARR.

Image-Based Phenotyping of Flowering Intensity in Cool-Season Crops.

The timing and duration of flowering are key agronomic traits that are often associated with the ability of a variety to escape abiotic stress such as heat and drought. Flowering information is valuable in both plant breeding and agricultural production management. Visual assessment, the standard protocol used for phenotyping flowering, is a low-throughput and subjective method. In this study, we evaluated multiple imaging sensors (RGB and multiple multispectral cameras), image resolution (proximal/remote sensing at 1.6 to 30 m above ground level/AGL), and image processing (standard and unsupervised learning) techniques in monitoring flowering intensity of four cool-season crops (canola, camelina, chickpea, and pea) to enhance the accuracy and efficiency in quantifying flowering traits. The features (flower area, percentage of flower area with respect to canopy area) extracted from proximal (1.6-2.2 m AGL) RGB and multispectral (with near infrared, green and blue band) image data were strongly correlated (r up to 0.89) with visual rating scores, especially in pea and canola. The features extracted from unmanned aerial vehicle integrated RGB image data (15-30 m AGL) could also accurately detect and quantify large flowers of winter canola (r up to 0.84), spring canola (r up to 0.72), and pea (r up to 0.72), but not camelina or chickpea flowers. When standard image processing using thresholds and unsupervised machine learning such as k-means clustering were utilized for flower detection and feature extraction, the results were comparable. In general, for applicability of imaging for flower detection, it is recommended that the image data resolution (i.e., ground sampling distance) is at least 2-3 times smaller than that of the flower size. Overall, this study demonstrates the feasibility of utilizing imaging for monitoring flowering intensity in multiple varieties of evaluated crops.

Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.).

Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height (r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.

Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil.

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R 2 = 0.45-0.73 and RMSE = 0.66-1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section - computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections - computed from wavelengths in the range of 630-670, 700-840, and 1320-1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R 2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R 2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs - computed from wavelengths of 700, 710, 730, and 790 nm - had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes.

Targeting of Wnt/ß-Catenin by Anthelmintic Drug Pyrvinium Enhances Sensitivity of Ovarian Cancer Cells to Chemotherapy.

BACKGROUND Aberrant activation of Wnt/ß-catenin has been shown to promote ovarian cancer proliferation and chemoresistance. Pyrvinium, an FDA-approved anthelmintic drug, has been identified as a potent Wnt inhibitor. Pyrvinium may sensitize ovarian cancer cells to chemotherapy. MATERIAL AND METHODS The effect of pyrvinium alone and its combination with paclitaxel in ovarian cancer was investigated using an in vitro culture system and in vivo xenograft models. The mechanisms of its action were also analyzed, focusing on the Wnt/ß-catenin pathway. RESULTS Pyrvinium inhibited growth and induced apoptosis of paclitaxel- and cisplatin-resistant epithelial ovarian cancer cell lines A2278/PTX and SK-OV-3. Its combination with paclitaxel was synergistic in targeting ovarian cancer cells in vitro. In 3 independent ovarian xenograft mouse models, pyrvinium alone inhibited tumor growth. More importantly, we observed significant inhibition of tumor growth throughout the treatment when using pyrvinium and paclitaxel combined. Mechanistically, pyrvinium increased the Wnt-negative regulator axin and decreased the b-catenin levels in ovarian cancer cells. In addition, pyrvinium suppressed Wnt/b-catenin-mediated transcription, as shown by the decreased mRNA levels of MYC, cyclin D, and BCL-9. In contrast, the inhibitory effects of pyrvinium were reversed by ß-catenin stabilization or overexpression, demonstrating that pyrvinium acted on ovarian cancer cells via targeting the Wnt/ß-catenin signaling pathway. CONCLUSIONS We demonstrated that the anthelmintic drug pyrvinium targets ovarian cancer cells through suppressing Wnt/ß-catenin signaling. Our work highlights the therapeutic value of inhibiting Wnt/ß-catenin in ovarian cancer.