Phosphine-catalyzed dearomative [3+2] cycloaddition of 4-nitroisoxazoles with allenoates or Morita-Baylis-Hillman carbonates.

An efficient phosphine-catalyzed dearomative [3+2] annulation of 4-nitroisoxazoles with allenoates or Morita-Baylis-Hillman carbonates has been established for the convenient synthesis of bicyclic isoxazoline derivatives. This reaction approach showed a broad substrate scope, high functional group compatibility, and excellent regioselectivity and diastereoselectivity. Furthermore, the success at the gram-scale and synthetic applications of the obtained compound 3a demonstrate the great potential of this methodology for practical applications in organic synthesis.

Phosphine-Catalyzed Ring-Opening Regioselective Addition of Cyclopropenones with Amides.

A series of amides, including α-bromo hydroxamates, N-alkoxyamides, and N-aryloxyamides, were subjected to phosphine-catalyzed ring-opening O-selective addition with cyclopropenones, producing various special α,ß-unsaturated esters containing oxime ether motif, in moderate to excellent yields, with high regioselectivity, and exclusive O-selectivity. The methodology is highly atom-economical, with simple operation procedures, and compatible with a wide substrate scope (more than 44 examples).

Association Between CYP24A1 Polymorphisms and Bladder Cancer Risk in the Chinese Han Population.

In this cohort of 217 bladder cancer patients and 484 healthy controls, we explored the association between CYP24A1 variants (rs2762934, rs1570669, rs6068816, rs2296241) and bladder cancer risk in the Chinese Han population. Utilizing the Agena MassARRAY system, we genotyped four selected CYP24A1 polymorphisms. Logistic regression revealed a significant association of rs2762934 and rs1570669 with elevated bladder cancer risk, while rs6068816 exhibited a protective effect. Bioinformatics analysis of CYP24A1 expression in normal and cancerous bladder tissues indicated higher expression in normal tissue. In conclusion, our findings highlight the potential role of CYP24A1 variants in bladder cancer susceptibility.

Genome-wide characterization of SmZHD gene family and the role of SmZHD12 in regulating anthocyanin biosynthesis in eggplant (Solanum melongena L.).

KEY MESSAGE: SmZHDs was highly expressed in anthocyanin-rich parts of eggplant. SmZHD12 can activate the expression of SmCHS, SmANS, SmDFR and SmF3H. Overexpression of SmZHD12 promotes anthocyanin biosynthesis in Arabidopsis. The Zinc finger-homeodomain (ZHD) proteins family genes are known to play a significant role in plant development and physiological processes. However, the evolutionary history and function of the ZHD gene family in eggplant remain largely unexplored. This study categorizes a total of 15 SmZHD genes into SmMIF and SmZHD subfamilies based on conserved domains. The phylogeny, gene structure, conserved motifs, promoter elements, and chromosomal locations of the SmZHD genes were comprehensively analyzed. Tissue expression profiles indicate that the majority of SmZHD genes are expressed in anthocyanin-rich areas. qRT-PCR assays revealed distinct expression patterns of SmZHD genes in response to various treatments, indicating their potential involvement in multiple signaling pathways. Analysis of transcriptomic data from light-treated eggplant peel identified SmZHD12 as the most light-responsive gene among the 15 SmZHD genes. Consequently, this study provides further evidence that SmZHD12 facilitates anthocyanin accumulation in Arabidopsis leaves by upregulating the expression of anthocyanin biosynthesis structural genes, as confirmed by dual-luciferase assays and Arabidopsis genetic transformation. Our study will lay a solid foundation for the in-depth study of the involvement of SmZHD genes in the regulation of anthocyanin biosynthesis.

A Predefined-Time Adaptive Zeroing Neural Network for Solving Time-Varying Linear Equations and Its Application to UR 5 Robot.

Time-varying linear equations (TVLEs) play a fundamental role in the engineering field and are of great practical value. Existing methods for the TVLE still have issues with long computation time and insufficient noise resistance. Zeroing neural network (ZNN) with parallel distribution and interference tolerance traits can mitigate these deficiencies and thus are good candidates for the TVLE. Therefore, a new predefined-time adaptive ZNN (PTAZNN) model is proposed for addressing the TVLE in this article. Unlike previous ZNN models with time-varying parameters, the PTAZNN model adopts a novel error-based adaptive parameter, which makes the convergence process more rapid and avoids unnecessary waste of computational resources caused by large parameters. Moreover, the stability, convergence, and robustness of the PTAZNN model are rigorously analyzed. Two numerical examples reflect that the PTAZNN model possesses shorter convergence time and better robustness compared with several variable-parameter ZNN models. In addition, the PTAZNN model is applied to solve the inverse kinematic solution of UR 5 robot on the simulation platform CoppeliaSim, and the results further indicate the feasibility of this model intuitively.

HVS-Unsup: Unsupervised cervical cell instance segmentation method based on human visual simulation.

Instance segmentation plays an important role in the automatic diagnosis of cervical cancer. Although deep learning-based instance segmentation methods can achieve outstanding performance, they need large amounts of labeled data. This results in a huge consumption of manpower and material resources. To solve this problem, we propose an unsupervised cervical cell instance segmentation method based on human visual simulation, named HVS-Unsup. Our method simulates the process of human cell recognition and incorporates prior knowledge of cervical cells. Specifically, firstly, we utilize prior knowledge to generate three types of pseudo labels for cervical cells. In this way, the unsupervised instance segmentation is transformed to a supervised task. Secondly, we design a Nucleus Enhanced Module (NEM) and a Mask-Assisted Segmentation module (MAS) to address problems of cell overlapping, adhesion, and even scenarios involving visually indistinguishable cases. NEM can accurately locate the nuclei by the nuclei attention feature maps generated by point-level pseudo labels, and MAS can reduce the interference from impurities by updating the weight of the shallow network through the dice loss. Next, we propose a Category-Wise droploss (CW-droploss) to reduce cell omissions in lower-contrast images. Finally, we employ an iterative self-training strategy to rectify mislabeled instances. Experimental results on our dataset MS-cellSeg, the public datasets Cx22 and ISBI2015 demonstrate that HVS-Unsup outperforms existing mainstream unsupervised cervical cell segmentation methods.

Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore-Penrose Inverse.

This article proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute the time-varying tensor Moore-Penrose (MP) inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional time-varying parameters that increase over time, the adaptive parameter is proportional to the error norm, thereby better allocating computational resources and improving efficiency. To further enhance efficiency, the ET-PTANN model combines an event trigger with the evolution formula, resulting in the adjustment of step size and reduction of computation frequency compared to the PTANN model. By conducting mathematical derivations, the article derives the upper bound of convergence time for the proposed neural network models and determines the minimum execution interval for the event trigger. A simulation example demonstrates that the PTANN and ET-PTANN models outperform other related neural network models in terms of computational efficiency and convergence rate. Finally, the practicality of the PTANN and ET-PTANN models is demonstrated through their application for mobile sound source localization.

Identification of Six Pathogenic Genes for Tibetan Familial Ventricular Septal Defect by Whole Exome Sequencing.

INTRODUCTION: Ventricular septal defect (VSD) is the most common congenital heart malformation in children. This study aimed to investigate potential pathogenic genes associated with Tibetan familial VSD. METHODS: Whole genomic DNA was extracted from eight Tibetan children with VSD and their healthy parents (a total of 16 individuals). Whole-exome sequencing was performed using the Illumina HiSeq platform. After filtration, detection, and annotation, single nucleotide variations and insertion-deletion markers were examined. Comparative evaluations using the Sorting Intolerant from Tolerant, PolyPhen V2, Mutation Taster, and Combined Annotation Dependent Depletion databases were conducted to predict harmful mutant genes associated with the etiology of Tibetan familial VSD. RESULTS: A total of six missense mutations in genetic disease-causing genes associated with the development of Tibetan familial VSD were identified: activin A receptor type II-like 1 (c.652 C > T: p.R218 W), ATPase cation transporting 13A2 (c.1363 C > T: p.R455 W), endoplasmic reticulum aminopeptidase 1 (c.481 G > A: p.G161 R), MRI1 (c.629 G > A: p.R210Q), tumor necrosis factor receptor-associated protein 1 (c.224 G > A: p.R75H), and FBN2 (c.2260 G > A: p.G754S). The Human Gene Mutation Database confirmed activin A receptor type II-like 1, MRI1, and tumor necrosis factor receptor-associated protein 1 as pathogenic mutations, while FBN2 was classified as a probable pathogenic mutation. CONCLUSIONS: This novel study directly screens genetic variations associated with Tibetan familial VSD using whole-exome sequencing, providing new insights into the pathogenesis of VSD.

New energy vehicle battery recycling strategy considering carbon emotion from a closed-loop supply chain perspective.

The negative impact of used batteries of new energy vehicles on the environment has attracted global attention, and how to effectively deal with used batteries of new energy vehicles has become a hot issue. This paper combines the rank-dependent expected utility with the evolutionary game theory, constructs an evolutionary game model based on the interaction mechanism between decision makers' emotions and decision making, and studies the recycling strategy of new energy automobile trams under the heterogeneous combination of emotions. The study shows that: (1) In addition to the establishment of effective external norms, the subjective preference of decision makers can also positively affect the recycling strategy of new energy vehicle batteries. (2) Fairness preferences can have a significant nonlinear effect on new energy vehicle battery recycling strategies by changing the utility function of decision makers. (3) When new energy vehicle manufacturers remain optimistic and new energy vehicle demanders remain rational or pessimistic, the new energy vehicle battery recycling strategy can reach the optimal steady state.

Impact of missense TSBP1 variants on the susceptibility to coronary heart disease.

BACKGROUND: A genome-wide association study has recognized C6orf10-BTNL2 polymorphism in coronary artery disease. The goal of this study was to explore the potential correlation of nine missense TSBP1 variants with coronary heart disease (CHD) risk in the Chinese Han population. METHODS: Nine TSBP1 missense single nucleotide polymorphisms (SNPs) were selected for genotyping by the Agena MassARRAY platform. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to analyze the contribution of TSBP1 SNPs to CHD predisposition by logistic regression models adjusted by age, sex, drinking, and smoking. The correlation of TSBP1 variants with clinical data in CHD patients was examined by Kruskal-Wallis test. RESULTS: rs9268368-C (p = 0.039, OR = 1.18, 95 % CI: 1.01-1.38) was related to an increased risk of CHD, while rs3749966-C (p = 0.032, OR = 0.49, 95 % CI: 0.25-0.96) and rs3129941-A (p = 0.011, OR = 0.74, 95 % CI: 0.59-0.93) might be protective factors against CHD occurrence in the Chinese Han population. We also observed the effects of demographic characteristics (age, sex, alcohol consumption, and smoking) and complications (hypertension and diabetes) on the interactive association of TSBP1 polymorphisms with CHD susceptibility. rs139993810 was related to the levels of high-density lipoprotein cholesterol (HDL-C, p = 0.030). CONCLUSIONS: Our findings determined the association of TSBP1 rs9268368, rs3749966, and rs3129941 with CHD occurrence in the Chinese Han population, and highlighted the influence of demographic characteristics and complications on the interactive association of TSBP1 polymorphisms with CHD risk.

Preparation of emulsion hydrogels encapsulating extractant by the Pickering emulsion template method to recover lanthanum ions in aqueous solutions.

To solve the problem of liquid-liquid extraction of La(III), the oil-in-water Pickering emulsions were prepared by utilizing the aqueous solution of sodium alginate as the continuous phase, kerosene-diluted extractant di-(2-ethylhexyl) phosphate (P204) as the dispersed phase, and modified silica as an emulsifier. Then the emulsions were added to a calcium chloride solution to prepare the Pickering emulsion hydrogels (PEHGs) to better remove La(III). The PEHGs were characterized using Fourier transform infrared, thermogravimetric analysis, and scanning electron microscopy. The adsorption properties of PEHGs for La(III) in the aqueous solution were investigated using a UV-vis spectrophotometer. The study found that P204 was successfully coated by hydrogels and reached the highest adsorption capacity of 48 mg/g at pH 4. The amount of adsorption increased with the rise in temperature from 298 to 318 K. La(III) adsorption experimental data were more consistent with the pseudo-second-order kinetic model and the Langmuir isotherm model. Thermodynamic parameters showed that the adsorption of La(III) by PEHGs was a spontaneous endothermic process. The internal diffusion model revealed a linear relationship, indicating that internal diffusion played a role in the adsorption process. The encapsulating property of PEHGs indicated its potential usefulness in industrial wastewater for treating La(III).

Confusing finding of quantitative fluorescent polymerase chain reaction analysis in invasive prenatal genetic diagnosis: A case report.

BACKGROUND: Quantitative fluorescent polymerase chain reaction (QF-PCR) is a rapid prenatal diagnostic method for abnormalities on chromosomes 21, 18, and 13 and sex chromosomal aneuploidy. However, the value of QF-PCR in diagnosing chromosomal structural abnormalities is limited. In this article, we report a confusing QF-PCR finding in a pregnant woman who underwent amniocentesis. CASE SUMMARY: The short tandem repeat marker AMXY (Xp22.2/Yp11.2) located on the sex chromosome exhibited a trisomic biallelic pattern, indicating that the karyotype of the fetus might be 47,XYY. Chromosome analysis performed on cultured amniocytes showed a normal male karyotype of the fetus. Copy number variation sequencing confirmed a 500 kb duplication at Yp11.2-Yp11.2 (chrY:6610001_ 7110000) and a 250 kb duplication at Yp11.2-Yp11.2 (chrY:7110001_7360000). CONCLUSION: In conclusion, the comprehensive application of different methods could achieve a higher detection rate and accuracy for the prenatal diagnosis of chromosomal disorders through chromosomal testing.

A Dynamic Gain Fixed-Time Robust ZNN Model for Time-Variant Equality Constrained Quaternion Least Squares Problem With Applications to Multiagent Systems.

A dynamic gain fixed-time (FXT) robust zeroing neural network (DFTRZNN) model is proposed to effectively solve time-variant equality constrained quaternion least squares problem (TV-EQLS). The proposed approach surmounts the shortcomings of conventional numerical algorithms which fail to address time-variant problems. The DFTRZNN model is constructed with a novel dynamic gain parameter and a novel activation function (NAF), which differs from previous zeroing neural network (ZNN) models. Moreover, the comprehensive theoretical derivation of the FXT stability and robustness of the DFTRZNN model is presented in detail. Simulation results further confirm the availability and superiority of the DFTRZNN model for solving TV-EQLS. Finally, the consensus protocols of multiagent systems are presented by utilizing the design scheme of the DFTRZNN model, which further demonstrates its practical application value.

Synthesis of CF3-Containing 2-Imidazolines and Imidazoles via a Iodide-Promoted [3 + 2] Cyclization Reaction.

We report herein a general and effective system achieving cyclization of ß-trifluoromethyl enones with amidines in the presence of 1,3-diiodo-5,5-dimethylhydantoin (DIH), which affords a range of trifluoromethylated 2-imidazolines in synthetically useful yields with good diastereoselectivities (up to 95% yield, up to 98:2 dr) and good functional group tolerance. Furthermore, the one-pot synthesis of trifluoromethylated imidazoles via sequential cyclization and oxidation is demonstrated. More significantly, the reaction mechanism was verified by ESI-MS studies of possible intermediates, and a reasonable reaction mechanism was proposed.

CNSeg: A dataset for cervical nuclear segmentation.

BACKGROUND AND OBJECTIVE: Nuclear segmentation in cervical cell images is a crucial technique for automatic cytopathology diagnosis. Experimental evaluation of nuclear segmentation methods with datasets is helpful in promoting the advancement of nuclear segmentation techniques. However, public datasets are not enough for a reasonable and comprehensive evaluation because of insufficient quantity, single data source, and low segmentation difficulty. METHODS: Therefore, we provide the largest dataset for cervical nuclear segmentation (CNSeg). It contains 124,000 annotated nuclei collected from 1,530 patients under different conditions. The image styles in this dataset cover most practical application scenarios, including microbial infection, cytopathic heterogeneity, overlapping nuclei, etc. To evaluate the performance of segmentation methods from different aspects, we divided the CNSeg dataset into three subsets, namely the patch segmentation dataset (PatchSeg) with nuclei images collected under complex conditions, the cluster segmentation dataset (ClusterSeg) with cluster nuclei, and the domain segmentation dataset (DomainSeg) with data from different domains. Furthermore, we propose a post-processing method that processes overlapping nuclei single ones. RESULTS AND CONCLUSION: Experiments show that our dataset can comprehensively evaluate cervical nuclear segmentation methods from different aspects. We provide guidelines for other researchers to use the dataset. https://github.com/jingzhaohlj/AL-Net.

Genetic polymorphisms of very important pharmacogene variants in the Chinese Lisu population.

Aim: Interindividual and interethnic differences in drug efficacy drive the development and progress of pharmacogenomics and precision medicine. This study was performed to enrich the pharmacogenomic information for the Lisu population from China. Methods: 54 very important pharmacogene variants were selected from PharmGKB and genotyped in 199 Lisu individuals. The genotype distribution data of 26 populations were downloaded from the 1000 Genomes Project and analyzed with the χ2 test. Results: Among the 26 populations in the 1000 Genomes Project, African Caribbeans in Barbados; Esan in Nigeria; Gambian in Western Divisions, The Gambia; Luhya in Webuye, Kenya; Yoruba in Ibadan; Finnish in Finland; Toscani in Italy and Sri Lankan Tamil in the UK were the top eight nationalities with the most significant differences in genotype distribution from the Lisu population. The loci of CYP3A5 rs776746, KCNH2 rs1805123, ACE rs4291, SLC19A1 rs1051298 and CYP2D6 rs1065852 were significantly different in the Lisu. Conclusion: The results showed that there were substantial differences in SNPs of very important pharmacogene variants, which can provide a theoretical basis for individualized drug use for the Lisu.

The Association of Methylation Level in the CYP39A1 Gene with High Altitude Pulmonary Edema in the Chinese Population.

Background: High altitude pulmonary edema (HAPE) is still the most common fatal disease at high altitudes. DNA methylation proceeds with an important role in HAPE progression. This study was designed to investigate the association between CYP39A1 methylation and HAPE. Methods: Peripheral blood samples were enrolled from 106 participants (53 HAPE patients and 53 healthy subjects) to study the association of CYP39A1 methylation with HAPE. DNA methylation site in the promoter region of CYP39A1 was detected by Sequenom MassARRAY EpiTYPER platform. Results: Probability analysis showed that the methylation probabilities of CYP39A1_1_CpG_5 and CYP39A1_3_CpG_21 are significant differences between the cases and controls (p< 0.05). The methylation level analysis indicated that CYP39A1_1_CpG_2.3.4, CYP39A1_5_CpG_6.7, and CYP39A1_5_CpG_9.10 were higher methylation in HAPE compared to the controls (p< 0.05). CYP39A1_3_CpG_21 and CYP39A1_4_CpG_3 exhibited a lower methylation level in HAPE than that in the controls (p< 0.05). The association analysis given that CYP39A1_1_CpG_2.3.4 (OR 2.56, p= 0.035), CYP39A1_5_CpG_6.7 (OR 3.99, p= 0.003), CYP39A1_5_CpG_9.10 (OR 3.99, p= 0.003), CYP39A1_5_CpG_16.17.18 (OR 2.53, p= 0.033), and CYP39A1_5_CpG_20 (OR 3.05, p= 0.031) are associated with an increased risk of HAPE. Whereas CYP39A1_1_CpG_5 (OR 0.33, p= 0.016) and CYP39A1_3_CpG_21 (OR 0.18, p= 0.005) have a protective role in HAPE. Besides, age-stratification analysis showed that CYP39A1_1_CpG_5 (OR 0.16, p= 0.014) and CYP39A1_3_CpG_21 (OR 0.08, p= 0.023) had a protective impact on HAPE in people aged ≤32 years. CYP39A1_5_CpG_6.7 (OR 6.70, p= 0.008) and CYP39A1_5_CpG_9.10 (OR 6.70, p= 0.008) were related to an increased susceptibility to HAPE aged >32 years. Moreover, the diagnostic value of CYP39A1_3_CpG_21 (AUC = 0.712, p< 0.001) was significantly better than other CpG sites. Conclusion: The methylation level of CYP39A1 was associated with a risk of HAPE in the Chinese population, which provided new perspective for preventing and diagnosing of HAPE.

A Dynamic-Varying Parameter Enhanced ZNN Model for Solving Time-Varying Complex-Valued Tensor Inversion With Its Application to Image Encryption.

Time-varying complex-valued tensor inverse (TVCTI) is a public problem worthy of being studied, while numerical solutions for the TVCTI are not effective enough. This work aims to find the accurate solution to the TVCTI using zeroing neural network (ZNN), which is an effective tool in terms of solving time-varying problems and is improved in this article to solve the TVCTI problem for the first time. Based on the design idea of ZNN, an error-adaptive dynamic parameter and a new enhanced segmented signum exponential activation function (ESS-EAF) are first designed and applied to the ZNN. Then a dynamic-varying parameter-enhanced ZNN (DVPEZNN) model is proposed to solve the TVCTI problem. The convergence and robustness of the DVPEZNN model are theoretically analyzed and discussed. In order to highlight better convergence and robustness of the DVPEZNN model, it is compared with four varying-parameter ZNN models in the illustrative example. The results show that the DVPEZNN model has better convergence and robustness than the other four ZNN models in different situations. In addition, the state solution sequence generated by the DVPEZNN model in the process of solving the TVCTI cooperates with the chaotic system and deoxyribonucleic acid (DNA) coding rules to obtain the chaotic-ZNN-DNA (CZD) image encryption algorithm, which can encrypt and decrypt images with good performance.

NOTCH2, ATIC, MRI1, SLC6A13, ATP13A2 Genetic Variations are Associated with Ventricular Septal Defect in the Chinese Tibetan Population Through Whole-Exome Sequencing.

Background: Ventricular septal defect (VSD) is the most common congenital cardiac abnormality in children and the second most common in adults. This study aimed to explore the potentially causative genes in VSD patients in the Chinese Tibetan population, and to provide a theoretical basis for the genetic mechanism of VSD. Methods: Peripheral venous blood was collected from 20 VSD subjects, and whole-genome DNA was extracted. High-throughput sequencing was performed on qualified DNA samples using whole-exome sequencing (WES) technology. After filtering, detecting, and annotating qualified data, single nucleotide variations (SNVs) and insertion-deletion (InDel) markers were analyzed, and data processing software such as GATK, SIFT, Polyphen, and MutationTaster were used for comparative evaluation and prediction of pathogenic deleterious variants associated with VSD. Results: A total of 4793 variant loci, including 4168 SNVs, 557 InDels and 68 unknown loci and 2566 variant genes were obtained from 20 VSD subjects through bioinformatics analysis. According to the screening of the prediction software and database, the occurrence of VSD was predicted to be associated with five inherited pathogenic gene mutations, all of which were missense mutations, including NOTCH2 (c.1396C >A:p.Gln466Lys), ATIC (c.235C >T:p.Arg79Cys), MRI1 (c.629G >A:p.Arg210Gln), SLC6A13 (c.1138G >A:p.Gly380Arg), ATP13A2 (c.1363C >T:p.Arg455Trp). Conclusion: This study demonstrated that NOTCH2, ATIC, MRI1, SLC6A13, ATP13A2 gene variants were potentially associated with VSD in Chinese Tibetan population.

Predefined-Time Zeroing Neural Networks With Independent Prior Parameter for Solving Time-Varying Plural Lyapunov Tensor Equation.

As an extension of the Lyapunov equation, the time-varying plural Lyapunov tensor equation (TV-PLTE) can carry multidimensional data, which can be solved by zeroing neural network (ZNN) models effectively. However, existing ZNN models only focus on time-varying equations in field of real number. Besides, the upper bound of the settling time depends on the value of ZNN model parameters, which is a conservative estimation for existing ZNN models. Therefore, this article proposes a novel design formula for converting the upper bound of the settling time into an independent and directly modifiable prior parameter. On this basis, we design two new ZNN models called strong predefined-time convergence ZNN (SPTC-ZNN) and fast predefined (FP)-time convergence ZNN (FPTC-ZNN) models. The SPTC-ZNN model has a nonconservative upper bound of the settling time, and the FPTC-ZNN model has excellent convergence performance. The upper bound of the settling time and robustness of the SPTC-ZNN and FPTC-ZNN models are verified by theoretical analyses. Then, the effect of noise on the upper bound of settling time is discussed. The simulation results show that the SPTC-ZNN and FPTC-ZNN models have better comprehensive performance than existing ZNN models.