Estimation of multiple networks with common structures in heterogeneous subgroups.

Network estimation has been a critical component of high-dimensional data analysis and can provide an understanding of the underlying complex dependence structures. Among the existing studies, Gaussian graphical models have been highly popular. However, they still have limitations due to the homogeneous distribution assumption and the fact that they are only applicable to small-scale data. For example, cancers have various levels of unknown heterogeneity, and biological networks, which include thousands of molecular components, often differ across subgroups while also sharing some commonalities. In this article, we propose a new joint estimation approach for multiple networks with unknown sample heterogeneity, by decomposing the Gaussian graphical model (GGM) into a collection of sparse regression problems. A reparameterization technique and a composite minimax concave penalty are introduced to effectively accommodate the specific and common information across the networks of multiple subgroups, making the proposed estimator significantly advancing from the existing heterogeneity network analysis based on the regularized likelihood of GGM directly and enjoying scale-invariant, tuning-insensitive, and optimization convexity properties. The proposed analysis can be effectively realized using parallel computing. The estimation and selection consistency properties are rigorously established. The proposed approach allows the theoretical studies to focus on independent network estimation only and has the significant advantage of being both theoretically and computationally applicable to large-scale data. Extensive numerical experiments with simulated data and the TCGA breast cancer data demonstrate the prominent performance of the proposed approach in both subgroup and network identifications.

Isotropic multi-scale neuronal reconstruction from high-ratio expansion microscopy with contrastive unsupervised deep generative models.

BACKGROUND AND OBJECTIVE: Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures. METHODS: We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. RESULTS: The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. CONCLUSION: With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures.

miR-130b regulates B cell proliferation via CYLD-mediated NF-κB signaling.

Previous studies have revealed that B cell activation is regulated by various microRNAs(miRNAs). However, the role of microRNA-130b regulating B cell activation and apoptosis is still unclear. In the present study, we first found that the expression of miR-130b was the lowest in Pro/Pre-B cells and the highest in immature B cells. Besides, the expression of miR-130b decreased after activation in B cells. Through the immuno-phenotypic analysis of miR-130b transgenic and knockout mice, we found that miR-130b mainly promoted the proliferation of B cells and inhibited B cell apoptosis. Furthermore, we identified that Cyld, a tumor suppressor gene was the target gene of miR-130b in B cells. Besides, the Cyld-mediated NF-κB signaling was increased in miR-130b overexpressed B cells, which further explains the enhanced proliferation of B cells. In conclusion, we propose that miR-130b promotes B cell proliferation via Cyld-mediated NF-κB signaling, which provides a new theoretical basis for the molecular regulation of B cell activation.

Competitive ratiometric fluorescent lateral flow immunoassay based on dual emission signal for sensitive detection of chlorothalonil.

In this study, we develop a competitive ratiometric fluorescent lateral flow immunoassay (CRF-LFIA) based on dual emission fluorescence signal, which has great advantage in visual and quantitative detection of Chlorothalonil (CTN). Red-emitted fluorescent magnetic nanobeads (FMNBs) and green-emitted aggregation-induced emission fluorescent microsphere (AIEFM) are synthesized and conjugated to antibodies and antigens respectively, resulting in competitive binding with the analyte. The ratiometric fluorescence signal which comes from the overlap of these two fluorescence emissions. FMNBs probes also provide immunomagnetic separation (IMS) to enrich the analysts and resist complex matrix effects. This strip generates a visually discernible yellow-to-green fluorescence color change in the presence of CTN (2 ng/mL), which could be incisively observed by naked eye. Moreover, the limit of detection (LOD) reached 0.152 ng/mL by measurement of color (Red-Green-Blue, RGB) signals. Method validation shows a good correlation between CRF-LFIA and LC-MS/MS.

Establishment of Biliary Atresia Prognostic Classification System via Survival-Based Forward Clustering - A New Biliary Atresia Classification.

OBJECTIVES: To develop a machine learning algorithm with prognosis data to identify different clinical phenotypes of biliary atresia (BA) and provide instructions for choosing treatment schemes. METHODS: Six hundred thirty-nine cases of type III BA were retrospectively collected from the Children's Hospital of Fudan University from Jan 1st, 2017 to Dec 1st, 2019 as a training dataset, and a survival-based forward clustering method, which can also be used to predict the subtype of a new patient was developed to identify BA subtypes. RESULTS: A total of 2 clusters were identified (cluster 1 = 324 and cluster 2 = 315), where cluster 2 had a lower 2 y native liver survival post-Kasai rate. The infant patients in cluster 2 have higher weight, liver, and spleen volume, wider portal vein width, and older operative age; worse coagulation and liver function results; higher grade of liver fibrosis and detection rate of hepatic portal fibrous mass, and higher recent infection detection rate of herpes simplex virus type I. With the proposed prognostic classification system, the authors predicted the subtypes of the 187 cases of type III BA in a testing dataset collected from the whole year of 2020. The p-value computed from the log-rank testing for the Kaplan-Meier survival curves of the predicted two testing groups was 0.0113. CONCLUSIONS: This classification system would be a convenient tool to choose appropriate treatment and accelerate the choice-making between clinicians and infant patients.

Executive pharmaceutical background, R&D investment and corporate innovation performance.

BACKGROUND: In the context of China's innovation-driven economy, the competitive advantage for pharmaceutical companies increasingly depends on their level of innovation. The executives, as the highest decision-makers in the company, directly influence corporate research and development (R&D) investment and innovation performance. Additionally, government subsidies, as an external factor alleviating corporate financial constraints, also impact a company's R&D investment. According to a survey by the Journal of China Securities, at the end of 2020, among 119 pharmaceutical companies listed on the A-share market, only 53.3% had Chief Executive Officer (CEOs) with pharmaceutical backgrounds. This study constructs a theoretical model of executive pharmaceutical backgrounds, R&D investment, government subsidies, and corporate innovation performance in pharmaceutical companies, aiming to reveal the mechanism and boundary condition between executive pharmaceutical backgrounds and corporate innovation performance in the pharmaceutical industry. METHODS: This study uses Chinese pharmaceutical companies listed from 2015 to 2022 as the research sample. The selected sample was processed as follows: companies with significant issues and risks of Special Treatment (ST) or *ST were excluded, companies with missing data were excluded, and companies for which executive background data could not be obtained were excluded. After these exclusions, the study obtained data for 223 listed pharmaceutical companies over 8 years. Then we constructed a three-stage regression model to test the impact of executive pharmaceutical background on R&D investment, the impact of R&D investment on corporate innovation performance, and the mediating effect of R&D investment. Considering that some scholars raised questions about it, this study simultaneously uses the Bootstrap analysis method in the SPSS PROCESS to test the mediating effect of R&D and the moderated mediating effect of government subsidies. RESULTS: The empirical research results reveal that executive pharmaceutical backgrounds have a significant positive impact on corporate R&D investment. R&D investment contributes to the innovation performance of pharmaceutical companies and plays an intermediary role between executive pharmaceutical backgrounds and corporate innovation performance. Government subsidies act as a moderator in the relationship between executive pharmaceutical backgrounds and R&D investment, and they also moderate the mediating effect of executive pharmaceutical backgrounds, R&D investment, and corporate innovation performance. CONCLUSIONS: This study provides insight from a dual perspective of internal and external factors, revealing the internal mechanism and boundary conditions influencing the innovation performance of pharmaceutical companies. In conclusion, pharmaceutical companies should employ executives with pharmaceutical backgrounds to manage the company. This will help increase the company's R&D investment, subsequently improving R&D performance and enhancing the pharmaceutical company's innovation competitiveness. This study not only expands the theories in the fields of executive characteristics, R&D investment, and corporate innovation performance but also has important policy implications for the appointment and selection of executives in pharmaceutical companies.

HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.

This study has been motivated by cancer research, in which heterogeneity analysis plays an important role and can be roughly classified as unsupervised or supervised. In supervised heterogeneity analysis, the finite mixture of regression (FMR) technique is used extensively, under which the covariates affect the response differently in subgroups. High-dimensional molecular and, very recently, histopathological imaging features have been analyzed separately and shown to be effective for heterogeneity analysis. For simpler analysis, they have been shown to contain overlapping, but also independent information. In this article, our goal is to conduct the first and more effective FMR-based cancer heterogeneity analysis by integrating high-dimensional molecular and histopathological imaging features. A penalization approach is developed to regularize estimation, select relevant variables, and, equally importantly, promote the identification of independent information. Consistency properties are rigorously established. An effective computational algorithm is developed. A simulation and an analysis of The Cancer Genome Atlas (TCGA) lung cancer data demonstrate the practical effectiveness of the proposed approach. Overall, this study provides a practical and useful new way of conducting supervised cancer heterogeneity analysis.

ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data.

Clustering is a critical component of single-cell RNA sequencing (scRNA-seq) data analysis and can help reveal cell types and infer cell lineages. Despite considerable successes, there are few methods tailored to investigating cluster-specific genes contributing to cell heterogeneity, which can promote biological understanding of cell heterogeneity. In this study, we propose a zero-inflated negative binomial mixture model (ZINBMM) that simultaneously achieves effective scRNA-seq data clustering and gene selection. ZINBMM conducts a systemic analysis on raw counts, accommodating both batch effects and dropout events. Simulations and the analysis of five scRNA-seq datasets demonstrate the practical applicability of ZINBMM.

DTX3L Enhances Type I Interferon Antiviral Response by Promoting the Ubiquitination and Phosphorylation of TBK1.

Studies already revealed that some E3 ubiquitin ligases participated in the immune response after viral infection by regulating the type I interferon (IFN) pathway. Here, we demonstrated that type I interferon signaling enhanced the translocation of ETS1 to the nucleus and the promoter activity of E3 ubiquitin ligase DTX3L (deltex E3 ubiquitin ligase 3L) after virus infection and thus increased the expression of DTX3L. Further experiments suggested that DTX3L ubiquitinated TBK1 at K30 and K401 sites on K63-linked ubiquitination pathway. DTX3L was also necessary for mediating the phosphorylation of TBK1 through binding with the tyrosine kinase SRC: both together enhanced the activation of TBK1. Therefore, DTX3L, being an important positive-feedback regulator of type I interferon, exerted a key role in antiviral response. IMPORTANCE Our present study evaluated DTX3L as an antiviral molecule by promoting IFN production and establishing an IFN-ß-ETS1-DTX3L-TBK1 positive-feedback loop as a novel immunomodulatory step to enhance interferon signaling and inhibit respiratory syncytial virus (RSV) infection. Our finding enriches and complements the biological function of DTX3L and provides a new strategy to protect against lung diseases such as bronchiolitis and pneumonia that develop with RSV.

A multiple lateral flow immunoassay based on AuNP for the detection of 5 chemical contaminants in milk.

Melamine (MEL), enrofloxacin (ENR), sulfamethazine (SMZ), tetracycline (TC), and aflatoxin M1 (AFM1) are the main chemical contaminants in milk. It is necessary to detect these miscellaneous chemical contaminants in milk synchronously to ensure the safety of the milk. In this study, a multiple lateral flow immunoassay (LFIA) was developed for the detection of MEL, ENR, SMZ, TC, and AFM1 in milk. Under optimal experimental conditions, the cutoff values were 25 ng/mL for MEL, 1 ng/mL for ENR, 2.5 ng/mL for SMZ, 2.5 ng/mL for TC, and 0.25 ng/mL for AFM1 in milk samples. The limits of detection of LFIA were 0.173 ng/mL for MEL, 0.078 ng/mL for ENR, 0.059 ng/mL for SMZ, 0.082 ng/mL for TC, and 0.0064 ng/mL for AFM1. The recovery rates of LFIA in milk were 83.2-104.4% for MEL, 76.5-127.3% for ENR, 96.8-113.5% for SMZ, 107.1-166.6% for TC, and 93.5-130.3% for AFM1. The coefficients of variation were all less than 15%. As a whole, the developed multiple lateral flow immunoassay showed potential as a highly reliable and excellent tool for the rapid and sensitive screening of MEL, ENR, SMZ, TC, and AFM1 in milk.

Bi-level structured functional analysis for genome-wide association studies.

Genome-wide association studies (GWAS) have led to great successes in identifying genotype-phenotype associations for complex human diseases. In such studies, the high dimensionality of single nucleotide polymorphisms (SNPs) often makes analysis difficult. Functional analysis, which interprets SNPs densely distributed in a chromosomal region as a continuous process rather than discrete observations, has emerged as a promising avenue for overcoming the high dimensionality challenges. However, the majority of the existing functional studies continue to be individual SNP based and are unable to sufficiently account for the intricate underpinning structures of SNP data. SNPs are often found in groups (e.g., genes or pathways) and have a natural group structure. Additionally, these SNP groups can be highly correlated with coordinated biological functions and interact in a network. Motivated by these unique characteristics of SNP data, we develop a novel bi-level structured functional analysis method and investigate disease-associated genetic variants at the SNP level and SNP group level simultaneously. The penalization technique is adopted for bi-level selection and also to accommodate the group-level network structure. Both the estimation and selection consistency properties are rigorously established. The superiority of the proposed method over alternatives is shown through extensive simulation studies. A type 2 diabetes SNP data application yields some biologically intriguing results.

How Do Repeated Viewings in Forest Landscapes Influence Young People's Visual Behaviors and Cognitive Evaluations?

BACKGROUND: With the spread of the COVID-19 epidemic, it has gradually become normal to periodically visit and enjoy forest landscape resources in the suburbs of cities. For designers and managers of forest landscapes, exploring change in the visual behaviors and cognitive evaluations of people who repeatedly view forest landscapes and the characteristics of this change will aid the design and sustainable utilization of forest landscape resources in the suburbs of cities. PURPOSE: From the perspective of users' preferences for forest landscape space, this study explored the changes in visual behavior characteristics and psychological preference characteristics for individuals who repeatedly view forest landscapes and their drivers under different preferences. METHODS: This study collected data from 52 graduate and undergraduate students. We used a difference test to compare the differences in the visual behavior coincidence degree and the changes in psychological evaluations; a descriptive statistical analysis to explore young peoples' likes and dislikes of landscape elements; and Spearman correlation analysis to explore the correlation between the psychological evaluations and visual behaviors. MAIN RESULTS: 1. At the second viewing, the participants' regression behavior tended to decrease for various spaces, and they were more inclined to view areas that they had not viewed before. In addition, at the second viewing, the degree of fixation behavior coincidence was generally low, and there were obvious differences across spaces; 2. The participants' feature evaluations and comprehensive evaluations for landscapes did not change significantly with their increased familiarity with the spaces; 3. There was a significant positive correlation between the participants' psychological evaluations of landscape stimuli and the degree of fixation coincidence when viewing the spaces, among which the rate of distant clarity and the degree of fixation behavior coincidence were significantly and positively correlated. Meanwhile, at the second viewing, the number of favorite elements in the lookout space, which belongs to high-preference spaces, noticeably increased.

Facile synthesis strategy for cesium tin halide perovskite crystals toward light emitting devices and anti-counterfeiting flexible fiber.

All-inorganic metal halide perovskites are widely studied because of their excellent photoelectric properties. However, due to the toxicity of CsPbX3 (X = Cl, Br, I) perovskites, it is difficult to apply them on a large scale. The lead-free nature and air stability make Cs2SnX6 (X = Cl, Br, I) perovskites possible candidates to replace CsPbX3 perovskites. Herein, we report the perovskite crystals (PCs) based on Te(IV)-doped Cs2SnCl6: Cs2Sn1-xTexCl6. Cs2Sn1-xTexCl6 PCs showed yellow emission under a 365 nm ultraviolet lamp. The photoluminescence quantum yield (PLQY) of Cs2Sn0.94Te0.06Cl6 PCs was 57.09%, which was proposed to be from the triplet Te(IV) ion 3P1 → 1S0 self-trapping excitons (STE) recombination. The perovskite crystals can be used to fabricate light-emitting diodes (LEDs). The fiber paper prepared from aramid chopped fibers (ACFs) and polyphenylene sulfide (PPS) fibers showed a bright yellow light under 365 nm ultraviolet light after being post-processed with Cs2Sn1-xTexCl6 PCs solution. The ACFs/PPS compound fiber paper modified with Cs2Sn1-xTexCl6 PCs maintained exceptional optical properties and could be stored in air for more than 4500 h. The fluorescence performance of the modified ACFs/PPS compound fiber paper could be applied to fluorescence anti-counterfeiting. The modification strategy and the applications in this work will provide a good choice for studying the optical performance of perovskites and broaden the application of ACFs/PPS compound fiber paper.

Cbl-b inhibited CD4+ T cell activation by regulating the expression of miR-99a/miR-125b.

The molecular regulation of T cell activation has always been a hot topic in immunology. It has been reported that Cbl-b inhibits T cell activation, but the specific molecular mechanism especially for transcriptional regulation has not been very clear so far. Our present study showed that ablation of Cbl-b resulted in the increased expression of miR-99a and miR-125b, and the antagonism of miR-99a or miR-125b could inhibit the Cbl-b-/- T cell over-activation partly. Further study demonstrated that Cbl-b could bind and ubiquitinate SHP-2 in the activated T cells. The activation of SHP-2 deficient T cells was significantly inhibited. Western blot showed that SHP-2 could dephosphorylate HOXA10, and HOXA10 could enter the nucleus under the stimulation of anti-CD3 antibody alone in Cbl-b deficient T cells. Luciferase reporter assay and CUT&Tag qPCR showed that HOXA10 could regulate the expression of miR-99a/miR-125b. Real-time PCR and western blot further indicated that miR-99a/miR-125b functioned on PI3K/AKT pathway to regulate T cell activation. In conclusion, our study demonstrated that Cbl-b ubiquitinated SHP-2 to arrest HOXA10-mediated CD4+ T cell activation by regulating the expression of miR-99a/miR-125b and their function on PI3K/AKT pathway, which might providing a new explanation for the regulation of T cell activation and potential new idea for autoimmune diseases and tumor immunotherapies.

Sensitive lateral flow immunoassay strips based on Fe3+-chelated polydopamine nanospheres for the detection of kanamycin.

As kanamycin (KAN) residue in animal products is harmful to consumers, a rapid and sensitive method for KAN detection needs to be established. KAN monoclonal antibody (KAN-mAb, 1D11) with the half maximal inhibitory concentration of 1.16 ng/mL was prepared in this study. A one-pot method was used to synthesize Fe3+-chelated polydopamine nanospheres (Fe@PDANs) with excellent characteristics of strong light absorption. The novel label of Fe@PDANs and KAN-mAb was used to develop a lateral flow immunoassay (LFIA) for the sensitive detection of KAN. The limit of detection of the Fe@PDANs-based LFIA (Fe@PDANs-LFIA) for KAN was 0.0191 ng/mL, which was 2.75 times lower than PDANs-based LFIA. Furthermore, the Fe@PDANs-LFIA was successfully applied to detect KAN in pork, milk, and honey samples, with recoveries ranging from 93.75% to 113.80% (coefficient of variation < 10%). Therefore, Fe@PDANs have potential for the detection of analytes in LFIA.

Two-level Bayesian interaction analysis for survival data incorporating pathway information.

Genetic interactions play an important role in the progression of complex diseases, providing explanation of variations in disease phenotype missed by main genetic effects. Comparatively, there are fewer studies on survival time, given its challenging characteristics such as censoring. In recent biomedical research, two-level analysis of both genes and their involved pathways has received much attention and been demonstrated as more effective than single-level analysis. However, such analysis is usually limited to main effects. Pathways are not isolated, and their interactions have also been suggested to have important contributions to the prognosis of complex diseases. In this paper, we develop a novel two-level Bayesian interaction analysis approach for survival data. This approach is the first to conduct the analysis of lower-level gene-gene interactions and higher-level pathway-pathway interactions simultaneously. Significantly advancing from the existing Bayesian studies based on the Markov Chain Monte Carlo (MCMC) technique, we propose a variational inference framework based on the accelerated failure time model with effective priors to accommodate two-level selection as well as censoring. Its computational efficiency is much desirable for high-dimensional interaction analysis. We examine performance of the proposed approach using extensive simulation. The application to TCGA melanoma and lung adenocarcinoma data leads to biologically sensible findings with satisfactory prediction accuracy and selection stability.

Baseline retinal nerve fiber layer thickness as a predictor of multiple sclerosis progression: New insights from the FREEDOMS II study.

BACKGROUND AND PURPOSE: The aim was to evaluate the potential of retinal nerve fiber layer thickness (RNFLT) measured with optical coherence tomography in predicting disease progression in relapsing-remitting multiple sclerosis (RRMS). METHODS: Analyses were conducted post hoc of this 24-month, phase III, double-blind study, in which RRMS patients were randomized (1:1:1) to once daily oral fingolimod 0.5 mg, 1.25 mg or placebo. The key outcomes were the association between baseline RNFLT and baseline clinical characteristics and clinical/imaging outcomes up to 24 months. Change of RNFLT with fingolimod versus placebo within 24 months and time to retinal nerve fiber layer (RNFL) thinning were evaluated. RESULTS: Altogether 885 patients were included. At baseline, lower RNFLT was correlated with higher Expanded Disability Status Scale score (r = -1.085, p = 0.018), lower brain volume (r = 0.025, p = 0.006) and deep gray matter volume (r = 0.731, p < 0.0001), worse visual acuity (r = -19.846, p < 0.0001) and longer duration since diagnosis (r = -0.258, p = 0.018). At month 12, low baseline RNFLT (<86 µm) versus high baseline RNFLT (≥99 µm) was associated with a greater brain volume loss (percentage change -0.605% vs. -0.315%, p = 0.035) in patients without optic neuritis history. At month 24, low baseline RNFLT versus high baseline RNFLT was associated with a higher number of new or newly enlarged T2 lesions (mean number 4.0 vs. 2.8, p = 0.014) and a higher risk of subsequent RNFL thinning (hazard ratio 2.55; 95% confidence interval 1.84-3.53; p < 0.001). The atrophy of the RNFL in the inferior quadrant was alleviated with fingolimod 0.5 mg versus placebo at month 24 (Δ(least squares mean) = 1.8, p = 0.047). CONCLUSION: Retinal nerve fiber layer thickness could predict disease progression in RRMS. TRIAL REGISTRATION: Clinicaltrials.gov identifier: NCT00355134, https://clinicaltrials.gov/ct2/show/NCT00355134.

The influence of differential leadership on employees' deviant innovation behavior: An outsider subordinate perspective.

Given the complex business environment worldwide and rapid development of information technologies, employees' deviant innovation behavior has attracted increasing attention. However, few scholars have explored the internal mechanism of the influence of differential leadership on the deviant innovation behavior of outsider subordinates from a positive perspective. Based on relative deprivation theory and attribution theory, we construct a theoretical model to study the influence of differential leadership in family businesses on the deviant innovation behavior of outsider subordinates, and hypothesis testing is conducted based on 243 questionnaire responses. The result shows that: differential leadership has a positive impact on the deviant innovation behavior of outsider subordinates; relative deprivation plays a mediating role; and internal control personality enhances the moderation effect between relative deprivation and outsider subordinates' deviant innovation behavior. This study provides a reference point for the promotion of the innovation performance both of employees and organizations.

Analysis of Concept Construction and Scale Development of Employee Zhengchong Behaviour in Family Firms in Jiangsu Province of China.

Purpose: Some scholars have explored the connotation and structural elements of employee zhengchong behaviour based on Taiwan's local enterprises, providing results with reference significance. However, there is a lack of accurate measurement scales. How to treat employee zhengchong behaviour (striving for a favour) and effectively deconstruct it is very important to the sustainable development of family firms. Methods: Semistructured interviews were conducted with 62 employees of private enterprises, and the structural dimension of employee zhengchong behaviour was explored with the help of grounded theory. The researchers designed two questionnaires, collected 278 and 331 valid questionnaires in the two surveys, compiled the corresponding measurement scale, and tested it. Results: Employee zhengchong behaviour under differential leadership was a multidimensional structure with rich connotations consisting of four dimensions: showing abilities, collaborating and sharing, excluding outsiders, and ingratiating upwards. The scale includes 16 items. Conclusion: This study enriches the relevant theories while providing a decision reference for family firm leaders to guide employee zhengchong behaviour to reasonably improve firm performance.

Influence of differential leadership behavior on employees' deviant innovation: Based on dual perspectives of insider and outsider subordinates.

Differential leadership as a localized leadership style gradually developed on the basis of the Pattern of Differential Sequence. It plays a dual role in stimulating "insider subordinates" and "outsider subordinates" through the dynamic transformation of the roles. Using the process of game reasoning, the study identifies the differing principles used by insider subordinates and outsider subordinates in implementing deviant innovative behaviors. The simulation graph presents the perceived benefits of employees performing or not performing deviant innovative behaviors as clues during the reasoning process, and implements deviant innovative behaviors for the high risk-taking trait of insider subordinates and the high internal control personality of outsider subordinates who implement deviant innovation. The theoretical derivation of behavior provides relevant references, and provides counter measures for effectively promoting employees' deviant innovative behavior in the context of differential leadership.