Complementary to Multiple Labels: A Correlation-Aware Correction Approach.

Complementary label learning (CLL) requires annotators to give irrelevant labels instead of relevant labels for instances. Currently, CLL has shown its promising performance on multi-class data by estimating a transition matrix. However, current multi-class CLL techniques cannot work well on multi-labeled data since they assume each instance is associated with one label while each multi-labeled instance is relevant to multiple labels. Here, we show theoretically how the estimated transition matrix in multi-class CLL could be distorted in multi-labeled cases as they ignore co-existing relevant labels. Moreover, theoretical findings reveal that calculating a transition matrix from label correlations in multi-labeled CLL (ML-CLL) needs multi-labeled data, while this is unavailable for ML-CLL. To solve this issue, we propose a two-step method to estimate the transition matrix from candidate labels. Specifically, we first estimate an initial transition matrix by decomposing the multi-label problem into a series of binary classification problems, then the initial transition matrix is corrected by label correlations to enforce the addition of relationships among labels. We further show that the proposal is classifier-consistent, and additionally introduce an MSE-based regularizer to alleviate the tendency of BCE loss overfitting to noises. Experimental results have demonstrated the effectiveness of the proposed method.

The Efficacy of Entomopathogenic Nematodes Plus an Adjuvant against Helicoverpa zea and Chrysodeixis includens in Aboveground Applications.

In the southern United States, corn earworm, Helicoverpa zea (Boddie), and soybean looper, Chrysodeixis includens (Walker) are economically important crop pests. Although Bt crops initially provided effective control of target pests such as H. zea, many insect pests have developed resistance to these Bt crops. Alternative approaches are needed, including biological control agents such as entomopathogenic nematodes (EPNs). However, the effectiveness of EPNs for aboveground applications can be limited due to issues such as desiccation and ultraviolet radiation. Effective adjuvants are needed to overcome these problems. Ten strains of EPNs were tested for virulence against eggs, first to fourth instars, fifth instars, and pupae of H. zea and C. includens in the laboratory. These 10 EPN strains were Heterorhabditis bacteriophora (HP88 and VS strains), H. floridensis (K22 strain), Hgkesha (Kesha strain), Steinernema carpocapsae (All and Cxrd strains), S. feltiae (SN strain), S. rarum (17c+e strain), and S. riobrave (355 and 7-12 strains). EPNs could infect eggs of H. zea or C. includens in the laboratory, but the infection was low. The mortality caused by 10 EPN strains in seven days was significantly higher for the first to fourth instars of H. zea compared to the control, as was the fifth instars of H. zea. Similarly, for the first to fourth and fifth instars of C. includens, the mortality was significantly higher compared to the controls, respectively. However, only S. riobrave (355) had significantly higher mortality than the control for the pupae of H. zea. For the pupae of C. includens, except for H. bacteriophora (HP88), S. rarum (17c+e), and H. floridensis (K22), the mortality of the other seven strains was significantly higher than the control. Subsequently, S. carpocapsae (All) and S. riobrave (7-12) were chosen for efficacy testing in the field with an adjuvant 0.066% Southern Ag Surfactant (SAg Surfactant). In field experiments, the SAg Surfactant treatment significantly increased the mortality and EPN infection for S. carpocapsae (All) on first instars of H. zea in corn plant whorls. On soybean plants, with the SAg Surfactant, S. carpocapsae (All) was more effective than S. riobrave (7-12) on fifth instars of C. includens. This study indicates that EPNs can control H. zea and C. includens, and SAg Surfactant can enhance EPN efficacy.

Student Loss: Towards the Probability Assumption in Inaccurate Supervision.

Noisy labels are often encountered in datasets, but learning with them is challenging. Although natural discrepancies between clean and mislabeled samples in a noisy category exist, most techniques in this field still gather them indiscriminately, which leads to their performances being partially robust. In this paper, we reveal both empirically and theoretically that the learning robustness can be improved by assuming deep features with the same labels follow a student distribution, resulting in a more intuitive method called student loss. By embedding the student distribution and exploiting the sharpness of its curve, our method is naturally data-selective and can offer extra strength to resist mislabeled samples. This ability makes clean samples aggregate tightly in the center, while mislabeled samples scatter, even if they share the same label. Additionally, we employ the metric learning strategy and develop a large-margin student (LT) loss for better capability. It should be noted that our approach is the first work that adopts the prior probability assumption in feature representation to decrease the contributions of mislabeled samples. This strategy can enhance various losses to join the student loss family, even if they have been robust losses. Experiments demonstrate that our approach is more effective in inaccurate supervision. Enhanced LT losses significantly outperform various state-of-the-art methods in most cases. Even huge improvements of over 50% can be obtained under some conditions.

Towards Enabling Binary Decomposition for Partial Multi-Label Learning.

Partial multi-label learning (PML) is an emerging weakly supervised learning framework, where each training example is associated with multiple candidate labels which are only partially valid. To learn the multi-label predictive model from PML training examples, most existing approaches work by identifying valid labels within candidate label set via label confidence estimation. In this paper, a novel strategy towards partial multi-label learning is proposed by enabling binary decomposition for handling PML training examples. Specifically, the widely used error-correcting output codes (ECOC) techniques are adapted to transform the PML learning problem into a number of binary learning problems, which refrains from using the error-prone procedure of estimating labeling confidence of individual candidate label. In the encoding phase, a ternary encoding scheme is utilized to balance the definiteness and adequacy of the derived binary training set. In the decoding phase, a loss weighted scheme is applied to consider the empirical performance and predictive margin of derived binary classifiers. Extensive comparative studies against state-of-the-art PML learning approaches clearly show the performance advantage of the proposed binary decomposition strategy for partial multi-label learning.

The mechanisms of metabolic resistance to pyrethroids and neonicotinoids fade away without selection pressure in the tarnished plant bug Lygus lineolaris.

BACKGROUND: Heavy selection pressure prompted the development of resistance in a serious cotton pest tarnished plant bug (TPB), Lygus Lineolaris in the mid-southern United States. Conversely, a laboratory resistant TPB strain lost its resistance to five pyrethroids and two neonicotinoids after 36 generations without exposure to any insecticide. It is worthwhile to examine why the resistance diminished in this population and determine whether the resistance fade away has practical value for insecticide resistance management in TPB populations. RESULTS: A field-collected resistant TPB population in July (Field-R1) exhibited 3.90-14.37-fold resistance to five pyrethroids and two neonicotinoids, while another field-collected TPB population in April (Field-R2) showed much lower levels of resistance (0.84-3.78-fold) due to the absence of selection pressure. Interestingly, after 36 generations without exposure to insecticide, the resistance levels in the same population [laboratory resistant strain (Lab-R)] significantly decreased to 0.80-2.09-fold. The use of detoxification enzyme inhibitors had synergistic effects on permethrin, bifenthrin and imidacloprid in resistant populations of Lygus lineolaris. The synergism was more pronounced in Field-R2 than laboratory susceptible (Lab-S) and Lab-R TPB population. Moreover, esterase, glutathione S-transferase (GST), and cytochrome P450-monooxygenases (P450) enzyme activities increased significantly by approximately 1.92-, 1.43-, and 1.44-fold in Field-R1, respectively, and 1.38-fold increased P450 enzyme activities in Field-R2 TPB population, compared to the Lab-S TPB. In contrast, the three enzyme activities in the Lab-R strain were not significantly elevated anymore relative to the Lab-S population. Additionally, Field-R1 TPB showed elevated expression levels of certain esterase, GST and P450 genes, respectively, while Field-R2 TPB overexpressed only P450 genes. The elevation of these gene expression levels in Lab-R expectedly diminished to levels close to those of the Lab-S TPB populations. CONCLUSION: Our results indicated that the major mechanism of resistance in TPB populations was metabolic detoxification, and the resistance development was likely conferred by increased gene expressions of esterase, GST, and P450 genes, the fadeaway of the resistance may be caused by reversing the overexpression of esterase, GST and P450. Without pesticide selection, resistant gene (esterase, GST, P450s) frequencies declined, and detoxification enzyme activities returned to Lab-S level, which resulted in the recovery of the susceptibility in the resistant TPB populations. Therefore, pest's self-purging of insecticide resistance becomes strategically desirable for managing resistance in pest populations. Published 2023. This article is a U.S. Government work and is in the public domain in the USA.

Variational Label Enhancement.

Multi-label learning focuses on the ambiguity at the label side, i.e., one instance is associated with multiple class labels, where the logical labels are always adopted to partition class labels into relevant labels and irrelevant labels rigidly. However, the relevance or irrelevance of each label corresponding to one instance is essentially relative in real-world tasks and the label distribution is more fine-grained than the logical labels by denoting one instance with a certain number of the description degrees of all class labels. As the label distribution is not explicitly available in most training sets, a process named label enhancement emerges to recover the label distributions in training datasets. By inducing the generative model of the label distribution and adopting the variational inference technique, the approximate posterior density of the label distributions should maximize the variational lower bound. Following the above consideration, LEVI is proposed to recover the label distributions from the training examples. In addition, the multi-label predictive model is induced for multi-label learning by leveraging the recovered label distributions along with a specialized objective function. The recovery experiments on fourteen label distribution datasets and the predictive experiments on fourteen multi-label learning datasets validate the advantage of our approach over the state-of-the-art approaches.

Lethal Concentration and Sporulation by Contact and Direct Spray of the Entomopathogenic Fungus Beauveria bassiana on Different Stages of Nezara viridula (Heteroptera: Pentatomidae).

The southern green stink bug, Nezara viridula (L.) (Heteroptera: Pentatomidae) is the most significant pest of soybean worldwide. The present study was conducted to compare the effectiveness of a Delta native strain NI8 of Beauveria bassiana by contact and direct spray on nymphs (2nd to 5th instar) and adults of N. viridula. Water control and four concentrations of B. bassiana were used to evaluate the survival, mortality, and molting percentage and to estimate median lethal concentration (LC50), median lethal sporulation (LS50), and resistance ratio (RR50). Direct spray at all concentrations observed the greatest reduction in survival on all life stages. Mortality and sporulation were positively correlated by concentration, while molting was highly variable with a significantly lower negative correlation on insects that were directly sprayed. Pathogenicity exhibited reduction as young stages developed and emerged to adult. The LC50 (Contact: 612 spores/mm2; Direct spray: 179 spores/mm2) and LS50 (Contact: 1960 spores/mm2 Spray: 3.3 × 106) values showed that adults of N. viridula were highly resistant than any other life stage when exposed to either contact or direct spray. Fourth instar was the most susceptible (LC50: Contact: 18 spores/mm2; Direct spray: 23 spores/mm2) (LS50: Contact: 53 spores/mm2; Direct spray: 26 spores/mm2) followed by second, third, and fifth instars.

GluN1 antibody causes behavioral deficits in prepulse inhibition and memory through CaMKIIß signaling.

Accumulating evidence suggests that some patients with schizophrenia have high production of autoantibodies against the N-methyl-d-aspartate receptor (NMDAR) subunit GluN1 and that these antibodies lead to cognitive impairment. However, the molecular mechanisms of the deficits seen in these patients are largely unknown. In the present study, we found that passive infusion of GluN1 antibody into the hippocampus of mice for 7 days led to decreased expression of GluN1, phosphor-Ser897-GluN1, and EphrinB2 receptor (EphB2R); deficits in long-term potentiation (LTP) and synaptic transmission in the hippocampal CA1 area; impairment in prepulse inhibition (PPI); and deterioration of recognition memory in novel object recognition test. We also found decreased expression of CaMKIIß, ERK1/2, CREB, and NF-κB after 7 days of GluN1 antibody exposure, as was the phosphorylation of these signaling molecules. The decrease in GluN1 and phosphor-Ser897-GluN1 expression and the deficits in LTP, PPI, and recognition memory were ameliorated by CaMKIIß overexpression. These results suggest that downregulation of CaMKIIß-ERK1/2-CREB-NF-κB signaling is responsiable for GluN1 antibody-associated impairment in PPI and memory and that GluN1 antibody-induced NMDAR hypofunction is the underlying mechanism of this impairment. Our findings indicate possible strategies to ameliorate NMDAR antibody-associated cognitive impairment in neuropsychiatric disease. They also provide evidence that NMDAR hypofunction is an underlying mechanism for cognitive impairment in schizophrenia.

Multi-Label Classification With Label-Specific Feature Generation: A Wrapped Approach.

Label-specific features serve as an effective strategy to learn from multi-label data, where a set of features encoding specific characteristics of each label are generated to help induce multi-label classification model. Existing approaches work by taking the two-stage strategy, where the procedure of label-specific feature generation is independent of the follow-up procedure of classification model induction. Intuitively, the performance of resulting classification model may be suboptimal due to the decoupling nature of the two-stage strategy. In this paper, a wrapped learning approach is proposed which aims to jointly perform label-specific feature generation and classification model induction. Specifically, one (kernelized) linear model is learned for each label where label-specific features are simultaneously generated within an embedded feature space via empirical loss minimization and pairwise label correlation regularization. Comparative studies over a total of sixteen benchmark data sets clearly validate the effectiveness of the wrapped strategy in exploiting label-specific features for multi-label classification.

Towards Class-Imbalance Aware Multi-Label Learning.

Multi-label learning deals with training examples each represented by a single instance while associated with multiple class labels. Due to the exponential number of possible label sets to be considered by the predictive model, it is commonly assumed that label correlations should be well exploited to design an effective multi-label learning approach. On the other hand, class-imbalance stands as an intrinsic property of multi-label data which significantly affects the generalization performance of the multi-label predictive model. For each class label, the number of training examples with positive labeling assignment is generally much less than those with negative labeling assignment. To deal with the class-imbalance issue for multi-label learning, a simple yet effective class-imbalance aware learning strategy called cross-coupling aggregation (COCOA) is proposed in this article. Specifically, COCOA works by leveraging the exploitation of label correlations as well as the exploration of class-imbalance simultaneously. For each class label, a number of multiclass imbalance learners are induced by randomly coupling with other labels, whose predictions on the unseen instance are aggregated to determine the corresponding labeling relevancy. Extensive experiments on 18 benchmark datasets clearly validate the effectiveness of COCOA against state-of-the-art multi-label learning approaches especially in terms of imbalance-specific evaluation metrics.

Maximum Margin Multi-Dimensional Classification.

Multi-dimensional classification (MDC) assumes heterogeneous class spaces for each example, where class variables from different class spaces characterize semantics of the example along different dimensions. The heterogeneity of class spaces leads to incomparability of the modeling outputs from different class spaces, which is the major difficulty in designing MDC approaches. In this article, we make a first attempt toward adapting maximum margin techniques for MDC problem and a novel approach named M3MDC is proposed. Specifically, M3MDC maximizes the margins between each pair of class labels with respect to individual class variable while modeling relationship across class variables (as well as class labels within individual class variable) via covariance regularization. The resulting formulation admits convex objective function with nonlinear constraints, which can be solved via alternating optimization with quadratic programming (QP) or closed-form solution in either alternating step. Comparative studies on the most comprehensive real-world MDC datasets to date are conducted and it is shown that M3MDC achieves highly competitive performance against state-of-the-art MDC approaches.

Adaptive Graph Guided Disambiguation for Partial Label Learning.

In partial label learning, a multi-class classifier is learned from the ambiguous supervision where each training example is associated with a set of candidate labels among which only one is valid. An intuitive way to deal with this problem is label disambiguation, i.e., differentiating the labeling confidences of different candidate labels so as to try to recover ground-truth labeling information. Recently, feature-aware label disambiguation has been proposed which utilizes the graph structure of feature space to generate labeling confidences over candidate labels. Nevertheless, the existence of noises and outliers in training data makes the graph structure derived from original feature space less reliable. In this paper, a novel partial label learning approach based on adaptive graph guided disambiguation is proposed, which is shown to be more effective in revealing the intrinsic manifold structure among training examples. Other than the sequential disambiguation-then-induction learning strategy, the proposed approach jointly performs adaptive graph construction, candidate label disambiguation and predictive model induction with alternating optimization. Furthermore, we consider the particular human-in-the-loop framework in which a learner is allowed to actively query some ambiguously labeled examples for manual disambiguation. Extensive experiments clearly validate the effectiveness of adaptive graph guided disambiguation for learning from partial label examples.

Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification.

In multi-label classification, the strategy of label-specific features has been shown to be effective to learn from multi-label examples by accounting for the distinct discriminative properties of each class label. However, most existing approaches exploit the semantic relations among labels as immutable prior knowledge, which may not be appropriate to constrain the learning process of label-specific features. In this paper, we propose to learn label semantics and label-specific features in a collaborative way. Accordingly, a deep neural network (DNN) based approach named CLIF, i.e. Collaborative Learning of label semantIcs and deep label-specific Features for multi-label classification, is proposed. By integrating a graph autoencoder for encoding semantic relations in the label space and a tailored feature-disentangling module for extracting label-specific features, CLIF is able to employ the learned label semantics to guide mining label-specific features and propagate label-specific discriminative properties to the learning process of the label semantics. In such a way, the learning of label semantics and label-specific features interact and facilitate with each other so that label semantics can provide more accurate guidance to label-specific feature learning. Comprehensive experiments on 14 benchmark data sets show that our approach outperforms other well-established multi-label classification algorithms.

Chronic administration of ketamine induces cognitive deterioration by restraining synaptic signaling.

The discovery of the rapid antidepressant effects of ketamine has arguably been the most important advance in depression treatment. Recently, it was reported that repeated long-term ketamine administration is effective in preventing relapse of depression, which may broaden the clinical use of ketamine. However, long-term treatment with ketamine produces cognitive impairments, and the underlying molecular mechanisms for these impairments are largely unknown. Here, we found that chronic in vivo exposure to ketamine for 28 days led to decreased expression of the glutamate receptor subunits GluA1, GluA2, GluN2A, and GluN2B; decreased expression of the synaptic proteins Syn and PSD-95; decreased dendrite spine density; impairments in long-term potentiation (LTP) and synaptic transmission in the hippocampal CA1 area; and deterioration of learning and memory in mice. Furthermore, the reduced glutamate receptor subunit and synaptic protein expression and the LTP deficits were still observed on day 28 after the last injection of ketamine. We found that the expression and phosphorylation of CaMKIIß, ERK1/2, CREB, and NF-κB were inhibited by ketamine. The reductions in glutamate receptor subunit expression and dendritic spine density and the deficits in LTP, synaptic transmission, and cognition were alleviated by overexpression of CaMKIIß. Our study indicates that inhibition of CaMKIIß-ERK1/2-CREB/NF-κB signaling may mediate chronic ketamine use-associated cognitive impairments by restraining synaptic signaling. Hypofunction of the glutamatergic system might be the underlying mechanism accounting for chronic ketamine use-associated cognitive impairments. Our findings may suggest possible strategies to alleviate ketamine use-associated cognitive deficits and broaden the clinical use of ketamine in depression treatment.

Partial Multi-Label Learning via Credible Label Elicitation.

Partial multi-label learning (PML) deals with the problem where each training example is associated with an overcomplete set of candidate labels, among which only some candidate labels are valid. The task of PML naturally arises in learning scenarios with inaccurate supervision, and the goal is to induce a multi-label predictor which can assign a set of proper labels for unseen instance. The PML training procedure is prone to be misled by false positive labels concealed in the candidate label set, which serves as the major modeling difficulty for partial multi-label learning. In this paper, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction. In the first stage, the labeling confidence of candidate label for each PML training example is estimated via iterative label propagation. In the second stage, by utilizing credible labels with high labeling confidence, multi-label predictor is induced via pairwise label ranking coupled with virtual label splitting or maximum a posteriori (MAP) reasoning. Experimental studies show that the proposed approach can achieve highly competitive generalization performance by excluding most false positive labels from the training procedure via credible label elicitation.

Mating Type Idiomorphs, Heterothallism, and High Genetic Diversity in Venturia carpophila, Cause of Peach Scab.

Scab (caused by Venturia carpophila) is a major disease affecting peach in the eastern United States. The aims of the study were to characterize the mating-type loci in V. carpophila, determine whether they are in equilibrium, and assess the population genetic diversity and structure of the pathogen. The mating-type gene MAT1-1-1 was identified in isolate JP3-5 in an available genome sequence, and the MAT1-2-1 gene was PCR amplified from isolate PS1-1, thus indicating a heterothallic structure. Mating-type loci structures were consistent with those of other Venturia spp. (V. effusa and V. inaequalis): the mating-type gene is positioned between APN2 encoding a DNA lyase and a gene encoding a Pleckstrin homology domain. Primers designed to each of the mating-type genes and a reference gene TUB2 were used as a multiplex PCR to screen a population (n = 81) of V. carpophila from various locations in the eastern United States. Mating types in five of the nine populations studied were in equilibrium. Among the 81 isolates, there were 69 multilocus genotypes. A population genetic analysis of the populations with >10 individuals (four populations) showed them to be genetically diverse. Linkage disequilibrium was found in five of nine populations with ≥4 isolates. A discriminant analysis of principal components indicated three genetic clusters, although extensive admixture was observed. Mating-type identification in V. carpophila provides a basis for understanding reproductive methods of the pathogen and can be a basis for further studies of the genetics of the peach scab pathogen.

Preliminary comparative analysis of kynurenine pathway metabolites in chronic ketamine users, schizophrenic patients, and healthy controls.

OBJECTIVE: The serum kynurenine pathway metabolites kynurenic acid (KYNA), kynurenine (KYN), and tryptophan (TRP) were examined in chronic ketamine users and in schizophrenic patients. The correlations of the metabolites with sociodemographic data, clinical characteristics, and drug use status were analyzed. METHODS: Seventy-nine healthy controls, 78 ketamine users, and 80 schizophrenic patients were recruited. Serum TRP, KYN, and KYNA levels were measured by high-performance liquid chromatography following tandem mass spectrometry (MS/MS). Psychotic symptoms were evaluated using the positive and negative syndrome scale (PANSS), the Beck Depression Inventory (BDI), and the Beck Anxiety Inventory (BAI). RESULTS: Serum levels of TRP, KYNA, and KYN (in ketamine users only) were lower in ketamine users and schizophrenic patients than in controls (p < .05). TRP and KYN were lower in ketamine users than in schizophrenic patients (p < .01). KYNA levels were positively correlated with the current frequency of ketamine use in ketamine users (p = .031), and serum KYNA levels were negatively correlated with the duration of schizophrenia (p = .015). CONCLUSION: TRP, KYNA, and KYN were lower in chronic ketamine users than in controls, and the alterations were in the same direction as those observed in schizophrenic patients.

First description of the sexual stage of Venturia effusa, causal agent of pecan scab.

Pecan scab, caused by Venturia effusa, is the most prevalent disease of pecan in the southeastern United States. Recent characterization of the mating type (MAT) distribution of V. effusa revealed that the MAT idiomorphs are in equilibrium at various spatial scales, indicative of regular sexual recombination. However, the occurrence of the sexual stage of V. effusa has never been observed, and the pathogen was previously considered to rely entirely on asexual reproduction. We were able to generate the sexual stage by pairing isolates of opposite mating types on oatmeal culture media. Cultures were incubated at 24 C for 2 mo to allow hyphae from isolates of each mating type to interact. Culture plates were then incubated at 4 C for 4 mo, after which immature pseudothecia were observed. Following exposure to a 12-h photoperiod for 2 wk at 24 C, asci and ascospores readily developed. Pseudothecium and ascospore production was optimal when incubated for 4 mo at 4 C. We utilized progeny from a cross of an albino isolate and wild-type (melanized) isolates to determine that recombination had occurred. Multilocus genotyping using 32 microsatellite markers confirmed that progeny were the result of recombination, which was further supported by segregation of mating types and culture pigmentation. Albino progeny were all confirmed to contain the same mutation in the polyketide synthase (PKS1) melanin biosynthesis gene as the albino parent. The results of this study demonstrate the heterothallic nature of V. effusa. The impact of determining the source of the overwintering ascostroma will aid in management decisions to reduce the primary inoculum in the disease cycle.

GRIN2B Gene Polymorphism in Chronic Ketamine Users.

BACKGROUND AND OBJECTIVES: We examined the allelic variants of N-methyl- d-aspartate receptor 2B (GRIN2B) and analyzed the associations between GRIN2B gene polymorphism with ketamine use conditions and psychopathological symptoms in chronic ketamine users. METHODS: A total of 231 subjects were recruited. Four single nucleotide polymorphisms of GRIN2B, rs1805502, rs7301328, rs890, and rs1806201 were examined in 151 male chronic ketamine users and 80 controls. Psychopathological symptoms in chronic ketamine users were evaluated using the Positive and Negative Syndrome Scale, the Beck Depression Inventory, and the Beck Anxiety Inventory. RESULTS: The genotype CC of rs1806201 had a lower frequency in ketamine users than that in control subjects (χ2 = 8.167, P = .004) and the T allele frequency of rs1806201 in ketamine users was higher than that in the control subjects (P = .009, odds ratio = 2.019 [1.196-3.410]). Ketamine users of genotype TT and CC of rs1806201 had an earlier onset of ketamine use than subjects of genotype TC (P = .038, P = .049, respectively). The dose of ketamine consumption per day of use was higher in genotype GG of rs7301328 than that in those with CG in ketamine users (P = .026). There were no significant differences of the severity of psychopathologic symptoms among different genotypes tested. CONCLUSION AND SCIENTIFIC SIGNIFICANCE: GRIN2B gene polymorphism may play a role in ketamine abuse. (Am J Addict 2020;29:105-110).

Association Analysis of Neuronal Nitric Oxide Synthase 1 Gene Polymorphism With Psychopathological Symptoms in Chronic Ketamine Users.

Objective: We previously found that chronic ketamine usages were associated with various psychotic and cognitive symptoms mimicking schizophrenia. The blockade of the NMDA receptor and subsequent nitric oxide synthase 1 (NOS1) dysfunction were found to be closely correlated with schizophrenia including NOS1 gene polymorphisms. We examined the allelic variants of the gene coding neuronal nitric oxide synthase 1 (NOS1) in chronic ketamine users in the Chinese population and analyzed the association between NOS1 gene polymorphism and psychopathological symptoms in chronic ketamine users. The association between the NOS1 polymorphism and ketamine use characteristics was also examined. Methods: One hundred ninety seven male chronic ketamine users and 82 controls were recruited. Four common SNPs of the NOS1 gene, rs6490121, rs41279104, rs3782206, and rs3782219, were examined by real-time PCR with the TaqMan® assay system. Psychopathological symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS), Beck Depression Inventory (BDI), and the Beck Anxiety Inventory (BAI). Results: The genotype distribution of rs6490121 and rs41279104 in chronic ketamine users was significantly different from that in the control (p = 0.0001 and p = 0.002). The G allele frequency of rs6490121 in ketamine users was higher than that in the control (p = 2.23 * 10-6, OR = 3.07, 95% CI = 1.93-4.90). The T allele frequency of rs41279104 in chronic ketamine users was higher than that in the control (p = 0.01, OR = 1.76, 95% CI = 1.14-2.72). The BAI score was significantly different among the three genotypic groups of rs6490121 (F = 6.21, p = 0.002) in ketamine users; subjects of genotype AG and GG had a lower score than subjects of genotype AA. The score of the negative symptom subscale of PANSS was significantly different among the three genotypic groups of rs41279104 (F = 5.39, p = 0.005); in ketamine users, subjects of genotype CT and TT had a higher score than subjects of genotype CC. There was no difference in drug use characteristics in different genotypes of the four NOS1 gene polymorphisms tested in ketamine users (p > 0.05).