PURPL Promotes M2 Macrophage Polarization in Lung Cancer by Regulating RBM4/xCT Signaling.

Lung cancer is the most common malignancy worldwide. Long non-coding RNA (lncRNA) p53 upregulated regulator of P53 levels (PURPL) is abnormally in various cancers. However, the reports on its roles in lung cancer are limited. The purpose of present study is to investigate the potentials of lncRNA PURPL in lung cancer. PURPL and mRNA expression was determined using real-time reverse transcriptase-polymerase chain reaction (RT-qPCR). The location of PURPL was detected using RNA fluorescence in situ hybridization (FISH) assay. Protein expression was detected using western blot. Cellular functions were determined using flow cytometry. The interaction between PURPL and RNA-binding motif 4 (RBM4) was confirmed using RNA immunoprecipitation (RIP) assay. PURPL was overexpressed in lung cancer cells and patients. Overexpressed PURPL promoted M2 macrophage polarization and suppressed ferroptosis. Additionally, PURPL maintained the mRNA stability of cystine glutamate reverse transporter (xCT) via regulating RBM4. xCT knockdown antagonized the effects of overexpressed PURPL and inhibited M2 macrophage polarization via inducing macrophage ferroptosis. PURPL/RBM4/xCT axis promoted M2 macrophage polarization in lung cancer. Therefore, PURPL may be a potential target of lung cancer.

KIAA1429-mediated RXFP1 attenuates non-small cell lung cancer tumorigenesis via N6-methyladenosine modification.

BACKGROUND: N6-methyladenosine (m6A) modification has been associated with non-small cell lung cancer (NSCLC) tumorigenesis. OBJECTIVES: This study aimed to determine the functions of Vir-like m6A methyltransferase-associated (KIAA1429) and relaxin family peptide receptor 1 (RXFP1) in NSCLC. METHODS: A quantitative real-time polymerase chain reaction was used to analyze the mRNA levels of KIAA1429 and RXFP1 in NSCLC. After silencing KIAA1429 or RXFP1 in NSCLC cells, changes in the malignant phenotypes of NSCLC cells were assessed using cell counting kit-8, colony formation, and transwell assays. Finally, the m6A modification of RXFP1 mediated by KIAA1429 was confirmed using luciferase, methylated RNA immunoprecipitation, and western blot assays. RESULTS: KIAA1429 and RXFP1 were upregulated and downregulated in NSCLC, respectively. Silencing of KIAA1429 attenuated the viability, migration, and invasion of NSCLC cells, whereas silencing of RXFP1 showed the opposite function in NSCLC cells. Moreover, RXFP1 expression was inhibited by KIAA1429 via m6A-modification. Therefore, silencing RXFP1 reversed the inhibitory effect of KIAA1429 knockdown in NSCLC cells. CONCLUSION: Our findings confirmed that the KIAA1429/RXFP1 axis promotes NSCLC tumorigenesis. This is the first study to reveal the inhibitory function of RXFP1 in NSCLC via KIAA1429-mediated m6A-modification. These findings may help identify new biomarkers for targeted NSCLC therapy.

A nomogram for predicting cause-specific mortality among patients with cecal carcinoma: a study based on SEER database.

OBJECTIVE: Classical Cox proportional hazard models tend to overestimate the event probability in a competing risk setup. Due to the lack of quantitative evaluation of competitive risk data for colon cancer (CC), the present study aims to evaluate the probability of CC-specific death and construct a nomogram to quantify survival differences among CC patients. METHODS: Data on patients diagnosed with CC between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database. Patients were divided into a training dataset for the establishment of the model and a validation dataset to evaluate the performance the model at a ratio of 7:3. To evaluate the ability of multiple variables to predict cause-specific death in CC patients, univariate and multivariate analyses with Fine-Gray models were performed to screen the predictors of cause-specific death, and a nomogram for predicting cause-specific mortality was constructed. Then, the receiver operating characteristic (ROC) curve and the calibration curve were plotted to evaluate the prognostic performance of the nomogram. RESULTS: The dataset was randomly divided into a training (n = 16,655) dataset and a validation (n = 7,139) dataset at a ratio of 7:3. In the training dataset, variables including pathological subtypes of tumors, pathological grading (degree of differentiation), AJCC staging, T-staging, surgical type, lymph node surgery, chemotherapy, tumor deposits, lymph node metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for cause-specific death of CC patients. Among these factors, the AJCC stage had the strongest predictive ability, and these features were used to construct the final model. In the training dataset, the consistency index (C-index) of the model was 0.848, and the areas under the receiver operating characteristic curve (AUC) at 1, 3, and 5 years was 0.852, 0.861, and 0.856, respectively. In the validation dataset, the C-index of the model was 0.847, and the AUC at 1 year, 3 years, and 5 years was 0.841, 0.862, and 0.852, respectively, indicating that this nomogram had an excellent and robust predictive performance. CONCLUSION: This study can help clinical doctors make better clinical decisions and provide better support for patients with CC.

Logarithmic Schatten- p Norm Minimization for Tensorial Multi-View Subspace Clustering.

The low-rank tensor could characterize inner structure and explore high-order correlation among multi-view representations, which has been widely used in multi-view clustering. Existing approaches adopt the tensor nuclear norm (TNN) as a convex approximation of non-convex tensor rank function. However, TNN treats the different singular values equally and over-penalizes the main rank components, leading to sub-optimal tensor representation. In this paper, we devise a better surrogate of tensor rank, namely the tensor logarithmic Schatten- p norm ([Formula: see text]N), which fully considers the physical difference between singular values by the non-convex and non-linear penalty function. Further, a tensor logarithmic Schatten- p norm minimization ([Formula: see text]NM)-based multi-view subspace clustering ([Formula: see text]NM-MSC) model is proposed. Specially, the proposed [Formula: see text]NM can not only protect the larger singular values encoded with useful structural information, but also remove the smaller ones encoded with redundant information. Thus, the learned tensor representation with compact low-rank structure will well explore the complementary information and accurately characterize the high-order correlation among multi-views. The alternating direction method of multipliers (ADMM) is used to solve the non-convex multi-block [Formula: see text]NM-MSC model where the challenging [Formula: see text]NM problem is carefully handled. Importantly, the algorithm convergence analysis is mathematically established by showing that the sequence generated by the algorithm is of Cauchy and converges to a Karush-Kuhn-Tucker (KKT) point. Experimental results on nine benchmark databases reveal the superiority of the [Formula: see text]NM-MSC model.

Multi-Attribute Subspace Clustering via Auto-Weighted Tensor Nuclear Norm Minimization.

Self-expressiveness based subspace clustering methods have received wide attention for unsupervised learning tasks. However, most existing subspace clustering methods consider data features as a whole and then focus only on one single self-representation. These approaches ignore the intrinsic multi-attribute information embedded in the original data feature and result in one-attribute self-representation. This paper proposes a novel multi-attribute subspace clustering (MASC) model that understands data from multiple attributes. MASC simultaneously learns multiple subspace representations corresponding to each specific attribute by exploiting the intrinsic multi-attribute features drawn from original data. In order to better capture the high-order correlation among multi-attribute representations, we represent them as a tensor in low-rank structure and propose the auto-weighted tensor nuclear norm (AWTNN) as a superior low-rank tensor approximation. Especially, the non-convex AWTNN fully considers the difference between singular values through the implicit and adaptive weights splitting during the AWTNN optimization procedure. We further develop an efficient algorithm to optimize the non-convex and multi-block MASC model and establish the convergence guarantees. A more comprehensive subspace representation can be obtained via aggregating these multi-attribute representations, which can be used to construct a clustering-friendly affinity matrix. Extensive experiments on eight real-world databases reveal that the proposed MASC exhibits superior performance over other subspace clustering methods.

Adaptive graph convolutional clustering network with optimal probabilistic graph.

The graph convolutional network (GCN)-based clustering approaches have achieved the impressive performance due to strong ability of exploiting the topological structure. The adjacency graph seriously affects the clustering performance, especially for non-graph data. Existing approaches usually conduct two independent steps, i.e., constructing a fixed graph structure and then graph embedding representation learning by GCN. However, the constructed graph structure may be unreliable one due to noisy data, resulting in sub-optimal graph embedding representation. In this paper, we propose an adaptive graph convolutional clustering network (AGCCN) to alternatively learn the similarity graph structure and node embedding representation in a unified framework. Our AGCCN learns the weighted adjacency graph adaptively from the node representations by solving the optimization problem of graph learning, in which adaptive and optimal neighbors for each sample are assigned with probabilistic way according to local connectivity. Then, the attribute feature extracted by parallel Auto-Encoder (AE) module is fused into the input of adaptive graph convolution module layer-by-layer to learn the comprehensive node embedding representation and strengthen its representation ability. This also skillfully alleviates the over-smoothing problem of GCN. To further improve the discriminant ability of node representation, a dual self-supervised clustering mechanism is designed to guide model optimization with pseudo-labels information. Extensive experimental results on various real-world datasets consistently show the superiority and effectiveness of the proposed deep graph clustering method.

Updated Pharmacological Effects, Molecular Mechanisms, and Therapeutic Potential of Natural Product Geniposide.

At present, the potential of natural products in new drug development has attracted more and more scientists' attention, and natural products have become an important source for the treatment of various diseases or important lead compounds. Geniposide, as a novel iridoid glycoside compound, is an active natural product isolated from the herb Gardenia jasminoides Ellis (GJ) for the first time; it is also the main active component of GJ. Recent studies have found that geniposide has multiple pharmacological effects and biological activities, including hepatoprotective activity, an anti-osteoporosis effect, an antitumor effect, an anti-diabetic effect, ananti-myocardial dysfunction effect, a neuroprotective effect, and other protective effects. In this study, the latest research progress of the natural product geniposide is systematically described, and the pharmacological effects, pharmacokinetics, and toxicity of geniposide are also summarized and discussed comprehensively. We also emphasize the major pathways modulated by geniposide, offering new insights into the pharmacological effects of geniposide as a promising drug candidate for multiple disorders.

Gut microbiota and differential genes-maintained homeostasis is key to maintaining health of individuals with Yang-deficiency constitution.

OBJECTIVE: Yang-deficiency constitution (YADC) is a common unbalanced constitution that predisposes individuals to certain diseases. However, not all people with YADC manifest develop diseases. This calls for delineation of the underlying molecular mechanisms. Previous studies suggested that the gut microbiota and gene differential expression should be considered. METHODS: In the present study, we compared profiles of gut microbiota between four healthy YADC individuals and those of five healthy balanced constitution (BC) counterparts, based on 16S rRNA gene sequence analysis. Furthermore, YADC relevant genes identified by comparing 62 healthy YADC and 58 healthy BC individuals in total to perform intersection analysis, functional clustering and pathway enrichment analyses. RESULTS: The levels of harmful gut microbiota (Prevotellaceae, LDA score > 4.0, P = 0.0141) and beneficial gut microbiota (Ruminococcaceae, LDA score > 4.0, P = 0.0025, Faecalibacterium, LDA score > 4.0, P = 0.0484) were both elevated in healthy YADC individuals. Also, we found that the specific metabolic pathway with 2, 6-Dichloro-p-hydroquinone 1, 2-Dioxygenase (PcpA) as the core in gut microbiota and the glutathione transferase activity has been enriched by YADC relevant genes in healthy YADC individuals were both responsible for the detoxification of halogenated aromatic hydrocarbon substances. CONCLUSIONS: Both beneficial and harmful factors had been detected in healthy YADC individuals, functionally, they may have triggered homeostasis to maintain the health of individuals with YADC. The homeostasis may be maintained by beneficial and harmful factors from gut flora and genes. Future studies are expected to focus on halogenated aromatic hydrocarbons and their detoxification processes.

Rank Consistency Induced Multiview Subspace Clustering via Low-Rank Matrix Factorization.

Multiview subspace clustering has been demonstrated to achieve excellent performance in practice by exploiting multiview complementary information. One of the strategies used in most existing methods is to learn a shared self-expressiveness coefficient matrix for all the view data. Different from such a strategy, this article proposes a rank consistency induced multiview subspace clustering model to pursue a consistent low-rank structure among view-specific self-expressiveness coefficient matrices. To facilitate a practical model, we parameterize the low-rank structure on all self-expressiveness coefficient matrices through the tri-factorization along with orthogonal constraints. This specification ensures that self-expressiveness coefficient matrices of different views have the same rank to effectively promote structural consistency across multiviews. Such a model can learn a consistent subspace structure and fully exploit the complementary information from the view-specific self-expressiveness coefficient matrices, simultaneously. The proposed model is formulated as a nonconvex optimization problem. An efficient optimization algorithm with guaranteed convergence under mild conditions is proposed. Extensive experiments on several benchmark databases demonstrate the advantage of the proposed model over the state-of-the-art multiview clustering approaches.

Barrier Properties and Hydrophobicity of Biodegradable Poly(lactic acid) Composites Reinforced with Recycled Chinese Spirits Distiller's Grains.

Adding natural biomass to poly(lactic acid) (PLA) as a reinforcing filler is a way to change the properties of PLA. This paper is about preparing PLA/biomass composites by physically melting and blending Chinese Spirits distiller's grains (CSDG) biomass and PLA to optimize the composite performance. Composites of modified PLA (MPLA) with varying amounts of CSDG were also prepared by the melt-mixing method, and unmodified PLA/CSDG composites were used as a control group for comparative analysis. The functional groups of MPLA enhanced the compatibility between the polymer substrate and CSDG. The composite water vapor/oxygen barrier and mechanical properties were studied. It was found that the barrier and mechanical properties of MPLA/CSDG composites were significantly improved. SEM was adopted to examine the tensile section structure of the composites, and the compatibility between the filler and the matrix was analyzed. An appropriate amount of CSDG had a better dispersibility in the matrix, and it further improved the interfacial bonding force, which in turn improved the composite mechanical properties. X-ray diffraction, thermogravimetric analysis, and differential scanning calorimetry were conducted to determine the crystalline properties and to analyze the stability of the composites. It was found that the CSDG content had a significant effect on the crystallinity. Barrier and biodegradation mechanisms were also discussed.

Epigenetic driver mutations in ARID1A shape cancer immune phenotype and immunotherapy.

Whether mutations in cancer driver genes directly affect cancer immune phenotype and T cell immunity remains a standing question. ARID1A is a core member of the polymorphic BRG/BRM-associated factor chromatin remodeling complex. ARID1A mutations occur in human cancers and drive cancer development. Here, we studied the molecular, cellular, and clinical impact of ARID1A aberrations on cancer immunity. We demonstrated that ARID1A aberrations resulted in limited chromatin accessibility to IFN-responsive genes, impaired IFN gene expression, anemic T cell tumor infiltration, poor tumor immunity, and shortened host survival in many human cancer histologies and in murine cancer models. Impaired IFN signaling was associated with poor immunotherapy response. Mechanistically, ARID1A interacted with EZH2 via its carboxyl terminal and antagonized EZH2-mediated IFN responsiveness. Thus, the interaction between ARID1A and EZH2 defines cancer IFN responsiveness and immune evasion. Our work indicates that cancer epigenetic driver mutations can shape cancer immune phenotype and immunotherapy.

Ferroptosis: A Novel Anti-tumor Action for Cisplatin.

PURPOSE: Ferroptosis is a new mode of regulated cell death, which is completely distinct from other cell death modes based on morphological, biochemical, and genetic criteria. This study evaluated the therapeutic role of ferroptosis in classic chemotherapy drugs, including the underlying mechanism. MATERIALS AND METHODS: Cell viabilitywas detected by using the methylthiazoltetrazlium dye uptake method. RNAiwas used to knockout iron-responsive element binding protein 2, and polymerase chain reaction, western blot was used to evaluate the efficiency. Intracellular reduced glutathione level and glutathione peroxidases activitywere determined by related assay kit. Intracellularreactive oxygen species levelswere determined by flowcytometry. Electron microscopywas used to observe ultrastructure changes in cell. RESULTS: Among five chemotherapeutic drugs screened in this study, cisplatin was found to be an inducer for both ferroptosis and apoptosis in A549 and HCT116 cells. The depletion of reduced glutathione caused by cisplatin and the inactivation of glutathione peroxidase played the vital role in the underlying mechanism. Besides, combination therapy of cisplatin and erastin showed significant synergistic effect on their anti-tumor activity. CONCLUSION: Ferroptosis had great potential to become a new approach in anti-tumor therapies and make up for some classic drugs, which open up a new way for their utility in clinic.

Synthesis, characterization and catalytic epoxidation properties of lanthanide-stabilized peroxoisopolytungstates.

A series of new lanthanide-containing peroxoisopolyoxotungstates, K6Na4[H32{Ln4(WO4)(H2O)16[W7O22(O2)2]4}3]·105H2O [Ln = CeIII (1), NdIII (2), SmIII (3), TbIII (4), ErIII (5)], have been successfully synthesized and structurally characterized. All polyanions [Ln(WO4)(H2O)16{W7O22(O2)2}4]14- are isostructural and consist of a central [Ln(WO4)(H2O)16]10+ cluster surrounded by four peripheral [W7O22(O2)2]6- units. They could act as efficient recyclable catalysts for the epoxidation of various alkenes including different cycloalkenes, styrene derivatives, internal and long-chain alkenes. Under optimal conditions, catalyst 2 displays the best catalytic activity for the oxidation of cyclooctene with high cyclooctene conversion (98.3%) and excellent selectivity (up to 99%) and could be reused for three cycles with a negligible decrease in reactivity.

A Nanosized Gly-Decorated Praseodymium-Stabilized Selenotungstate Cluster: Synthesis, Structure, and Oxidation Catalysis.

A flexible one-pot strategy with pyramidal SeIV heteroatoms was employed for the assembly of the praseodymium-containing gly-decorated polyoxotungstate [{Pr3 (H2 O)10 [Se2 W22 O76 (gly)2 ]}2 (Se2 W7 O30 H2 )]18- (1 a), which is constructed from one {Se2 W7 O30 H2 } unit and two identical {Pr3 (H2 O)10 [Se2 W22 O76 (gly)2 ]} units. Furthermore, the catalytic performance of Cs2 Na4 H12 [{Pr3 (H2 O)10 [Se2 W22 O76 (gly)2 ]}2 (Se2 W7 O30 H2 )]⋅25 H2 O (1) for alkene epoxidation with hydrogen peroxide was investigated under mild reaction conditions, and the experimental results suggested that compound 1 exhibits good catalytic performance for the epoxidation of cyclooctene.

Urinary metabolomic profiling in rats exposed to dietary di(2-ethylhexyl) phthalate (DEHP) using ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS).

Di(2-ethylhexyl) phthalate (DEHP) is an omnipresent environmental chemical with widespread nonoccupational human exposure through multiple ways. Although considerable efforts have been invested to investigate mechanisms of DEHP toxicity, the key metabolic biomarkers of DEHP toxicity remain to be identified. The aim of this study was to assess the urinary metabonomics of dietary DEHP in rats using the technique of ultra-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF-MS). Fourteen female Wistar rats were divided into two groups and given increasing dietary doses of DEHP for 30 consecutive days. The urinary metabolite profile was studied using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) enabled clusters to be clearly separated. Eleven principal urinary metabolites were identified as contributing to the clusters. The clusters in the positive electrospray ionization (ESI) mode were xanthurenic acid, kynurenic acid, nonate, N6-methyladenosine, and L-isoleucyl-L-proline. The clusters in the negative ESI mode were hippuric acid, tetrahydrocortisol, citric acid, phenylpropionylglycine, cPA(18:2(9Z, 12Z)/0:0), and LysoPC(14:1(9Z)). The urinary metabonomic changes indicated that exposure to dietary DEHP can affect energy-related metabolism, liver and renal function, fatty acid metabolism, and cause DNA damage in rats. The findings of this study on the urinary metabolites and metabolic pathways of DEHP may form the basis for future studies on the mechanisms of toxicity of this commonly found environmental chemical.

Effects of Long-Term In Vivo Exposure to Di-2-Ethylhexylphthalate on Thyroid Hormones and the TSH/TSHR Signaling Pathways in Wistar Rats.

Di-(2-ethylhexyl)phthalate (DEHP) was a widely used chemical with human toxicity. Recent in vivo and in vitro studies suggested that DEHP-exposure may be associated with altered serum thyroid hormones (THs) levels, but the underlying molecular mechanisms were largely unknown. To explore the possible molecular mechanisms, 128 Wistar rats were dosed with DEHP by gavage at 0, 150, 300, and 600 mg/kg/day for 3 months (M) and 6 M, respectively. After exposure, expression of genes and proteins in the thyroid, pituitary, and hypothalamus tissues of rats were analyzed by Q-PCR and western blot, while the sera and urine samples were assayed by radioimmunoassay and ELISA. Results showed that serum THs levels were suppressed by DEHP on the whole. DEHP treatment influenced the levels of rats' thyrotropin releasing hormone receptor (TRHr), Deiodinases 1 (D1), thyroid stimulating hormone beta (TSHß), sodium iodide symporter (NIS), thyroid stimulating hormone receptor (TSHr), thyroperoxidase (TPO), thyroid transcription factor 1 (TTF-1), and thyroglobulin (TG) mRNA/protein expression in the hypothalamus-pituitary-thyroid (HPT) axis and decreased urine iodine. Taken together, observed findings indicate that DEHP could reduce thyroid hormones via disturbing the HPT axis, and the activated TSH/TSHR pathway is required to regulate thyroid function via altering TRHr, TSHß, NIS, TSHr, TPO, TTF-1 and TG mRNA/protein expression of the HPT axis.

Interaction Effects between Organochlorine Pesticides and Isoflavones In Vitro and In Vivo.

Organochlorine pesticides (OCPs) have caused increasing global concern due to their high toxicity, persistence, bioaccumulation, and significant adverse effects on human health. This study was to explore the interaction effects between OCPs and isoflavones. Six kinds of OCPs and 2 kinds of isoflavones-genistein and daidzein were included to study their effect on MCF-7 cells in vitro. Eighty-one female Sprague-Dawley rats were randomized to 9 groups according to factorial design to study the interaction effect between isoflavones and γ-HCH. Compared to organochlorine pesticides alone group, proliferation rate of MCF-7 cells was lower in 100 µmol/L genistein + organochlorine pesticides and 100 µmol/L daidzein + organochlorine pesticides group (p < 0.05). In vivo study showed that there are interaction effects on kidney weight and liver weight when treated with isoflavones and γ-HCH. The changes in uterine morphology and positive expression of ERα showed inhibition effects between isoflavones and γ-HCH. In conclusion, the data suggests that there are interactions between isoflavones and OCPs in vitro and in vivo.

Isopentatungstate-supported metal carbonyl derivative: synthesis, characterization, and catalytic properties for alkene epoxidation.

A novel isopentatungstate-supported metal carbonyl derivative KH[(CH3)4N]3{[Re(CO)3]4[(µ2-OH)(µ3-O)(W5O18)]}·6H2O () has been synthesized and characterized. Compound represents the second example of isopolyoxotungstate-supported metal carbonyl derivative. The catalysis of for alkene epoxidation with H2O2 was investigated, and was found to efficiently catalyze the epoxidation of cyclooctene with high conversion (98.9%) and excellent selectivity (99%). Furthermore, the solution behavior of the polyanion was also explored using electrospray ionization mass spectrometry.

A multi-component polyoxometalate and its catalytic performance for CO2 cycloaddition reactions.

A multi-component polyoxometalate based on earth-abundant elements (NH4)10[Co8(H2O)10V10Mo23O104(OH)6]·34.5H2O () has been successfully obtained and characterized. Furthermore, compound acted as a Lewis acid catalyst and promoted the conversion of carbon dioxide to a cyclic carbonate under mild reaction conditions.

Thermolides, potent nematocidal PKS-NRPS hybrid metabolites from thermophilic fungus Talaromyces thermophilus.

Macrocyclic PKS-NRPS hybrid metabolites represent a unique family of natural products mainly from bacteria with broad and outstanding biological activities. However, their distribution in fungi has rarely been reported, and little has been reported regarding their nematocidal activity. Here we describe an unprecedented class of PKS-NRPS hybrid metabolites possessing a 13-membered lactam-bearing macrolactone, thermolides A-F (1-6) from a thermophilic fungus Talaromyces thermophilus. We showed that 1 and 2 displayed potent inhibitory activity against three notorious nematodes with LC(50) values of 0.5-1 µg/mL, as active as commercial avermectins. This work provided a new class of promising lead compounds for nematocide discovery.