Latent Function Research Articles

MicroRNA-146a-5p induces cell cycle arrest and enhances apoptosis in gastric cancer via targeting CDC14A.

Objective: The present study was designed to investigate the expression of miRNA-146a-5p in gastric cancer (GC) tissues and the paired nonmalignant counterparts, to explore the influences of miRNA-146a-5p on the cell biological behavior of MKN-28 cells (highly metastatic human gastric cancer cells), and to identify the function of abnormal expression of its target gene cell division cycle 14 homolog A (CDC14A) in GC. Methods: We detected the expression of miRNA-146a-5p in formalin-fixed and paraffin-embedded (FFPE) GC tissues through microarray and quantitative real-time polymerase chain reaction (qRT-PCR). Then, we employed cell counting kit-8 (CCK-8) assays, cell cycle assays, and apoptosis analysis to uncover the latent function of miRNA-146a-5p in MKN-28 human GC cells. We also validated the target of miRNA-146a-5p via luciferase reporter assays. Results: miRNA-146a-5p levels were examined in the majority of primary GC tissues and several GC cell lines. As a result, miRNA-146a-5p levels were significantly declined in the GC tissues and cells. In addition, miRNA-146a-5p demonstrated a straight act on its 3'-untranslated region (3'-UTR) of CDC14A mRNA, accordingly decreasing the contents of CDC14A mRNA as well as its protein expression. An inverse correlation between CDC14A and miRNA-146a-5p was observed. Conclusion: The data suggest miRNA-146a-5p may contribute to inducing cell cycle arrest as well as prompting GC cell apoptosis via directly targeting CDC14A. Therefore, miRNA-146a-5p may be a potential indicator of the occurrence and development of GC.

Hierarchical regulatory module GENOMES UNCOUPLED1-GOLDEN2-LIKE1/2-WRKY18/40 modulates salicylic acid signaling.

Chloroplast-to-nucleus retrograde signaling (RS) pathways are critical in modulating plant development and stress adaptation. Among chloroplast proteins mediating RS pathways, GENOMES UNCOUPLED1 (GUN1) represses the transcription of the nuclear transcription factors GOLDEN2-LIKE1 (GLK1) and GLK2 that positively regulate chloroplast biogenesis. Given the extensive exploration of the function of GUN1 in biogenic RS carried out in previous years, our understanding of its role in plant stress responses remains scarce. Here, we revealed that GUN1 contributes to the expression of salicylic acid (SA)-responsive genes (SARGs) through transcriptional repression of GLK1/2 in Arabidopsis (Arabidopsis thaliana). Loss of GUN1 significantly compromised the SA responsiveness in plants, concomitant with the upregulation of GLK1/2 transcripts. In contrast, knockout of GLK1/2 potentiated the expression of SARGs and led to enhanced stress responses. Chromatin immunoprecipitation, coupled with quantitative PCR and related reverse genetic approaches, unveiled that in gun1, GLK1/2 might modulate SA-triggered stress responses by stimulating the expression of WRKY18 and WRKY40, transcriptional repressors of SARGs. In summary, we demonstrate that a hierarchical regulatory module, consisting of GUN1-GLK1/2-WRKY18/40, modulates SA signaling, opening a research avenue regarding a latent GUN1 function in plant-environment interactions.

Structural equation modeling (SEM) of kidney function markers and longitudinal CVD risk assessment.

Lower kidney function is known to enhance cardiovascular disease (CVD) risk. It is unclear which estimated glomerular filtration rate (eGFR) equation best predict an increased CVD risk and if prediction can be improved by integration of multiple kidney function markers. We performed structural equation modeling (SEM) of kidney markers and compared the performance of the resulting pooled indexes with established eGFR equations to predict CVD risk in a 10-year longitudinal population-based design. We split the study sample into a set of participants with only baseline data (n = 647; model-building set) and a set with longitudinal data (n = 670; longitudinal set). In the model-building set, we fitted five SEM models based on serum creatinine or creatinine-based eGFR (eGFRcre), cystatin C or cystatin-based eGFR (eGFRcys), uric acid (UA), and blood urea nitrogen (BUN). In the longitudinal set, 10-year incident CVD risk was defined as a Framingham risk score (FRS)>5% and a pooled cohort equation (PCE)>5%. Predictive performances of the different kidney function indexes were compared using the C-statistic and the DeLong test. In the longitudinal set, a SEM-based estimate of latent kidney function based on eGFRcre, eGFRcys, UA, and BUN showed better prediction performance for both FRS>5% (C-statistic: 0.70; 95% CI: 0.65-0.74) and PCE>5% (C-statistic: 0.75; 95%CI: 0.71-0.79) than other SEM models and different eGFR formulas (DeLong test p-values<3.21×10-6 for FRS>5% and <1.49×10-9 for PCE>5%, respectively). However, the new derived marker could not outperform eGFRcys (DeLong test p-values = 0.88 for FRS>5% and 0.20 for PCE>5%, respectively). SEM is a promising approach to identify latent kidney function signatures. However, for incident CVD risk prediction, eGFRcys could still be preferrable given its simpler derivation.

Activation of the CA2-ventral CA1 pathway reverses social discrimination dysfunction in Shank3B knockout mice

Mutation or deletion of the SHANK3 gene, which encodes a synaptic scaffolding protein, is linked to autism spectrum disorder and Phelan-McDermid syndrome, conditions associated with social memory impairments. Shank3B knockout mice also exhibit social memory deficits. The CA2 region of the hippocampus integrates numerous inputs and sends a major output to the ventral CA1 (vCA1). Despite finding few differences in excitatory afferents to the CA2 in Shank3B knockout mice, we found that activation of CA2 neurons as well as the CA2-vCA1 pathway restored social recognition function to wildtype levels. vCA1 neuronal oscillations have been linked to social memory, but we observed no differences in these measures between wildtype and Shank3B knockout mice. However, activation of the CA2 enhanced vCA1 theta power in Shank3B knockout mice, concurrent with behavioral improvements. These findings suggest that stimulating adult circuitry in a mouse model with neurodevelopmental impairments can invoke latent social memory function.

Intersectional Dynamics and Academic Advancement in the United States

ABSTRACT I use the intersectionality framework to understand how processes of tenure and promotion operate as a system that systematically advantages members of some groups while systematically disadvantaging members of other groups. Empirically, I examine how gender, race, ethnicity, and nativity combine to structure the institution of tenure and promotion in US universities. Consistent with original conceptualizations of intersectionality as a lens that illuminates social structure, this empirical work demonstrates that foreign-born White men are the most advantaged members in the institution of tenure and promotion. Only by accounting for all bases simultaneously does the latent function of the promotion and tenure institution come to light: One that especially advantages White men while disadvantaging women and people of color, both foreign and domestic.

Latent deformation models for multivariate functional data and time-warping separability.

Multivariate functional data present theoretical and practical complications that are not found in univariate functional data. One of these is a situation where the component functions of multivariate functional data are positive and are subject to mutual time warping. That is, the component processes exhibit a common shape but are subject to systematic phase variation across their domains in addition to subject-specific time warping, where each subject has its own internal clock. This motivates a novel model for multivariate functional data that connect such mutual time warping to a latent-deformation-based framework by exploiting a novel time-warping separability assumption. This separability assumption allows for meaningful interpretation and dimension reduction. The resulting latent deformation model is shown to be well suited to represent commonly encountered functional vector data. The proposed approach combines a random amplitude factor for each component with population-based registration across the components of a multivariate functional data vector and includes a latent population function, which corresponds to a common underlying trajectory. We propose estimators for all components of the model, enabling implementation of the proposed data-based representation for multivariate functional data and downstream analyses such as Fréchet regression. Rates of convergence are established when curves are fully observed or observed with measurement error. The usefulness of the model, interpretations, and practical aspects are illustrated in simulations and with application to multivariate human growth curves and multivariate environmental pollutiondata.

A Second-Order Symmetric Non-Negative Latent Factor Model for Undirected Weighted Network Representation

Precise representation to undirected weighted network (UWN) is the foundation of understanding connection patterns inside a massive node set. It can be addressed via a Symmetric Non-negative Latent Factor (SNLF) model with a non-convex learning objective. However, existing SNLF models commonly adopt a first-order learning algorithm that cannot well handle such a non-convex objective, thereby leading to inaccurate UWN representation. Aiming at addressing this issue, this study incorporates an efficient second-order learning algorithm into an SNLF model, thereby establishing a Second-order Symmetric Non-negative Latent Factor (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NLF) model with two-fold ideas: a) applying the single latent factor-related mapping function to the non-negativity constrained optimization parameters to achieve an unconstrained learning objective, and b) optimizing this learning objective with its optimization parameters through an efficient second-order learning algorithm to achieve accurate representation to the target UWN with affordable computational burden. Empirical studies indicate that owing to its efficient incorporation of the second-order optimization technique, the proposed S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> NLF model outperforms state-of-the-art SNLF models when they are used to gain highly accurate representation to UWNs emerging from real applications.

Misuse of the Function of the Government's Cash Social Assistance Program (BST) During the Covid-19 Pandemic

The purpose of this study is to find out the obstacles in the process of implementing cash social assistance in Simpang Village, Pasirkuda District, Cianjur Regency, to find out the latent and manifest functions of Cash Social Assistance (BST), to find out the forms of abuse from the recipient community of Cash Social Assistance (BST) in Simpang Village, Pasirkuda District, Cianjur Regency. This study used a qualitative approach with a descriptive method used by analyzing and describing events at the research location. The results of this study indicate that the distribution of Cash Social Assistance (BST) has been distributed to communities affected by the Covid-19 pandemic. However, there was misuse in the utilization of Social Cash Assistance (BST) funds by some beneficiary communities in Simpang Village. This is a latent function found in this study. In addition, the reality of the community's dependence on Cash Social Assistance (BST) in Simpang Village which can result in a feeling of laziness, consumptive community behavior and the community becoming not self-sufficient.

“You Don’t Need 55 Forms That Say the Same Thing”: The Burden on Police to Produce Extra-Institutional Knowledge

Central to the police role and function are the inter- and extra-institutional demands for “knowledge” on crime and frontline policing. However, this subject has failed to generate the required empirical attention. The current study thus draws on Ericson and Haggerty’s conceptualization of police as “knowledge workers” to reveal the extent to which knowledge production for other institutions remains salient and, as a latent function, burdens policing. To do so, we employ results from the analysis of two ethnographic studies of police paperwork. Results revealed significant extra-institutional information needs that have considerable effects on police work with seemingly little use-value, and a consensus that much of police paperwork represents a misplaced burden due to repetition. We conclude with a critical discussion on the policy implications of these findings.

On training non-uniform fuzzy partitions for function approximation using differential evolution: A study on fuzzy transform and fuzzy projection

This paper focuses on the use of differential evolution to improve the approximation properties of function approximation models based on fuzzy partitions. Two cases are considered: Fuzzy transform and Fuzzy projection, and the design of hybrid evolutionary fuzzy systems, is studied. Even though function approximation techniques based on fuzzy partitions have been well studied, few papers consider the problem of centroid selection of the basis functions. Thus, in most cases uniform fuzzy partitions are considered. By using an evolutionary algorithm a systematic approach on the selection of the partition, is provided. The optimisation problem involves the determination of the model parameters, which in our case are the fuzzy partition’s membership functions’ locations. The proposed method is tested on the scattered data approximation problem, in a regression sense, that is given a set of sparse data the latent function is approximated. Numerical studies on one and two-dimensional test functions demonstrate that the evolutionary algorithm based fuzzy projection displays high performance in terms of approximation error. Moreover, the proposed approach shows high approximation capabilities with a small number of basis functions. Comparison results, with uniform fuzzy partition models, neural networks and support vector machines, are provided.

Examination of morphological traits of children's faces related to perceptions of cuteness using Gaussian process ordinal regression.

Konrad Lorenz, an ethologist, proposed that certain physical elements are perceived as cute and induce caretaking behavior in other individuals, with the evolutionary function of enhancing offspring survival. He called these features Kindchenschema, baby schema. According to his introspection, these include a large forehead, chubby round features, and chubby cheeks. Previous studies are limited to examining the effects of these facial features on perceived cuteness. However, other morphological factors may be related to perceived cuteness. This study uses Bayesian optimization, one of the global sequential optimization methods for estimating unknown functions, to search for facial morphological features that enhance the perceptions of facial cuteness. We applied Bayesian optimization incorporating Gaussian process ordinal regression (GPOR), which allows an estimation of the latent cuteness function based on evaluations using the Likert scale. A total of 96 preschool children provided the facial images used in this study. We summarized the facial shape variations using methodologies of geometric morphometrics and principal component analysis (PCA) up to the third principal component (PC), which we refer to as the face space. A total of 40 participants evaluated the images created by warping the average facial texture of the children's faces with randomly generated parameters in the face space. Facial traits related to perceived cuteness were estimated based on the averaged cuteness function. Perceived cuteness was linked to the relative lower position of facial components and narrower jawline but not to the forehead height.

Bank Keliling Function for Society at Pasir Ipis Lembang in West Bandung Districts

In this research, author took the case of Bank Keliling function for society at Pasir Ipis Lembang. Bank Keliling is one of business entity that provide loans to people in the form of credit. The practice of Bank Keliling due to economy conditions society having income be inadequate to fulfill life need. Pasir Ipis Lembang is one of the village is in the village Jayagiri Bandung regency west where Bank Keliling system is a business entities that are attractive to people because of the villages that are distant from urban and living standard remain low make the community had no choice and the community borrowed through Bank Keliling system. The theory of functionalism structural Merton, there are manifest function and latent function who become review for an author in analyze research. The author use the qualitative method with descriptive analysis. The data obtained from the results of the collection of primary data and also secondary data by interviews. Data obtained form the results of the interview noted and collected. All the data obtained analyzed qualitatively so as to what lies behind such a reality can be unearthed. The result of research is that manifest function of Bank Keliling on community increase business capital of accessibility given a Bank Keliling while latent function through high interest and the Bank Keliling who did the violence when their billing loans. The conclusion of the research is that Bank Keliling having the function to community.

A generic physics-informed neural network-based framework for reliability assessment of multi-state systems

In this paper, we develop a generic physics-informed neural network (PINN)-based framework to assess the reliability of multi-state systems (MSSs). The proposed framework follows a two-step procedure. In the first step, we recast the reliability assessment of MSS as a machine learning problem using the framework of PINN. A feedforward neural network with two individual loss groups is constructed to encode the initial condition and the state transitions governed by ordinary differential equations in MSS, respectively. Next, we tackle the problem of high imbalance in the magnitudes of back-propagated gradients from a multi-task learning perspective and establish a continuous latent function for system reliability assessment. Particularly, we regard each element of the loss function as an individual learning task and project a task’s gradient onto the norm plane of any other task with a conflicting gradient by taking the projecting conflicting gradients (PCGrad) method. We demonstrate the applications of the proposed framework for MSS reliability assessment in a variety of scenarios, including time-independent or dependent state transitions, where system scales increase from small to medium. The computational results indicate that PINN-based framework reveals a promising performance in MSS reliability assessment and incorporation of PCGrad into PINN substantially improves the solution quality and convergence speed of the algorithm.

Predictors of perceived neighbourhood comfort in urban and suburban areas in Finland

BACKGROUND AND AIM: The quality of residential environment, or, neighbourhood, is one determinant of overall quality of life. International studies have observed that neighbourhood perceptions are associated with socio-demographic and dwelling-related factors, and subjective and objective neighbourhood qualities such as safety or low noise levels. However, only a few studies have examined all these attributes at once and assessed the role of inclusion in neighbourhood decision-making processes; these were the aims of this study. METHODS: A survey was conducted among adults (aged 18-97) in urban centres and suburbs in five Finnish cities (n=2057; response rate 34%) in 2021. Ordinal mixed regression models (using the latent probit link function) were specified to assess the importance of socio-demographic, dwelling, and neighbourhood attributes for perceived neighbourhood comfort. RESULTS: Almost 80% of the respondents perceived their neighbourhood as comfortable or very comfortable. Apart from age and relationship status, socio-demographic factors were not associated with perceived neighbourhood comfort. Concerning dwelling, neighbourhood comfort was related to being satisfied with the dwelling (vs unsatisfied/neither: b=.77), having a green view and looking at it often (vs seldom/no green view: b=.48) or sometimes (b=.16), and living in a detached house (vs a flat: b=.27). Of subjective neighbourhood attributes, the strongest predictors of neighbourhood comfort were being satisfied (vs unsatisfied/neither) with neighbourhood safety (b=.66), green areas (b=.40), blue areas (b=.32) and maintenance of traffic routes (b=.31); and good/very good (vs poor/very poor) possibilities to influence the decisions regarding the neighbourhood (b=.42). CONCLUSIONS: Supporting previous studies, perceived neighbourhood comfort was most strongly explained by satisfaction with dwelling and neighbourhood qualities -especially safety and natural environments- and, as a novel result, being able to influence neighbourhood decisions. In upcoming analyses, we will add objective neighbourhood attributes and examine whether selected factors moderate these relationships. KEYWORDS: neighbourhood satisfaction, suburbs, green space, residential satisfaction

A Laplacian‐regularized dual‐phase Gaussian process technique for semi‐supervised response surface modeling of black‐box functions

AbstractNumerous semi‐supervised learning strategies are designed to reduce the number of experiments to model expensive black‐box functions. However, most of the existing methods do not utilize the information of the estimated responses and the associated gradients in an effective manner. In this paper, we proposed a semi‐supervised learning configuration for the Gaussian process which utilizes the estimated responses and their gradients in a dual‐phase framework to improve the accuracy of estimation and reduce the number of expensive experiments required to represent a latent function. We also explore the incorporation of Laplacian regularization for information transfer using a combination of design vector and response surface elements between measured and unmeasured data points. Through several simulation studies and a case study on the evaluation of concrete strength prediction accuracy, we assess the effectiveness of the proposed framework and compare its performance against some of the existing Laplacian‐regularized methods in the Literature.

NT5E upregulation in head and neck squamous cell carcinoma: A novel biomarker on cancer-associated fibroblasts for predicting immunosuppressive tumor microenvironment.

Despite tremendous progress made in the diagnosis and managements, head and neck squamous cell carcinoma (HNSC) remains a global medical dilemma with dismal clinical prognosis and high mortality. Gene NT5E encodes the ecto-5’-nucleotidase (CD73), which facilitates the formation of immunosuppressive tumor microenvironment (TME) permissive for tumor progression in various malignancies. Nevertheless, the cell subsets NT5E expressed on and the potential function of NT5E in the TME of HNSC remain virgin lands in HNSC. In this study, we comprehensively performed integrated prognostic analysis and elucidated that NT5E was an independent prognostic indicator for HNSC, for which a high NT5E level predicted poor overall survival (OS), disease-specific survival (DSS) and progression-free interval (PFI) in HNSC patients (p<0.05). Enrichment analyses revealed the close correlation between NT5E and ECM remodeling, and the latent function of NT5E may involve in epithelial-to-mesenchymal transition (EMT) and metastasis during HNSC progression. HNSC-related immune infiltration analysis and single-cell type analysis demonstrated that NT5E expression was significantly positively associated with cancer-associated fibroblasts (CAFs) in HNSC (p<0.01). NT5E-related TME analysis revealed that NT5E-high group are characterized by low neoantigen loads (NAL, p<0.001) and tumor mutation burden (TMB, p<0.01), indicating high-NT5E-expression HNSC patients may be recalcitrant to immunotherapy. In-situ multicolor immunofluorescence staining was later conducted and the results further verified our findings. Taken together, NT5E could be a novel biomarker in HNSC. Predominantly expressed on CAFs, the upregulation of NT5E might predict an immunosuppressive TME for HNSC patients who may benefit little from immunotherapy. Targeting CAFs with high NT5E expression might be a novel therapeutic strategy for HNSC patients.

Extracellular Vesicle-Encapsulated microRNAs as Novel Biomarkers of Lung Health.

Rationale: Early detection of respiratory diseases is critical to facilitate delivery of disease-modifying interventions. Extracellular vesicle-enriched microRNAs (EV-miRNAs) may represent reliable markers of early lung injury. Objectives: Evaluate associations of plasma EV-miRNAs with lung function. Methods: The prospective NAS (Normative Aging Study) collected plasma EV-miRNA measurements from 1996-2015 and spirometry every 3-5 years through 2019. Associations of EV-miRNAs with baseline lung function were modeled using linear regression. To complement the individual miRNA approach, unsupervised machine learning was used to identify clusters of participants with distinct EV-miRNA profiles. Associations of EV-miRNA profiles with multivariate latent longitudinal lung function trajectories were modeled using log binomial regression. Biological functions of significant EV-miRNAs were explored using pathway analyses. Results were replicated in an independent sample of NAS participants and in the HEALS (Health Effects of Arsenic Longitudinal Study). Measurements and Main Results: In the main cohort of 656 participants, 51 plasma EV-miRNAs were associated with baseline lung function (false discovery rate-adjusted P value < 0.05), 28 of which were replicated in the independent NAS sample and/or in the HEALS cohort. A subset of participants with distinct EV-miRNA expression patterns had increased risk of declining lung function over time, which was replicated in the independent NAS sample. Significant EV-miRNAs were shown in pathway analyses to target biological pathways that regulate respiratory cellular immunity, the lung inflammatory response, and airway structural integrity. Conclusions: Plasma EV-miRNAs may represent a robust biomarker of subclinical lung injury and may facilitate early identification and treatment of patients at risk of developing overt lung disease.

Latent transforming growth factor β binding protein 3 controls adipogenesis

Transforming growth factor-beta (TGFβ) is released from cells as part of a trimeric latent complex consisting of TGFβ, the TGFβ propeptides, and either a latent TGFβ binding protein (LTBP) or glycoprotein-A repetitions predominant (GARP) protein. LTBP1 and 3 modulate latent TGFβ function with respect to secretion, matrix localization, and activation and, therefore, are vital for the proper function of the cytokine in a number of tissues. TGFβ modulates stem cell differentiation into adipocytes (adipogenesis), but the potential role of LTBPs in this process has not been studied. We observed that 72 h post adipogenesis initiation Ltbp1, 2, and 4 expression levels decrease by 74–84%, whereas Ltbp3 expression levels remain constant during adipogenesis. We found that LTBP3 silencing in C3H/10T1/2 cells reduced adipogenesis, as measured by the percentage of cells with lipid vesicles and the expression of the transcription factor peroxisome proliferator-activated receptor gamma (PPARγ). Lentiviral mediated expression of an Ltbp3 mRNA resistant to siRNA targeting rescued the phenotype, validating siRNA specificity. Knockdown (KD) of Ltbp3 expression in 3T3-L1, M2, and primary bone marrow stromal cells (BMSC) indicated a similar requirement for Ltbp3. Epididymal and inguinal white adipose tissue fat pad weights of Ltbp3−/- mice were reduced by 62% and 57%, respectively, compared to wild-type mice. Inhibition of adipogenic differentiation upon LTBP3 loss is mediated by TGFβ, as TGFβ neutralizing antibody and TGFβ receptor I kinase blockade rescue the LTBP3 KD phenotype. These results indicate that LTBP3 has a TGFβ-dependent function in adipogenesis both in vitro and possibly in vivo. SignificanceUnderstanding the control of mesenchymal stem cell fate is crucial for the potential use of these cells for regenerative medicine.

Job Attainment Characteristics: Genetic Basis Shared with Other SES Indices

Job attainment is an important component of socioeconomic status (SES). There is currently a paucity of genomic research on an individuals’ job attainment, as well as how it is related to other SES variables and overall well-being. By incorporating O*NET occupational information into the UK Biobank database, we performed GWAS analyses of six major job attainment characteristics – complexity, autonomy, innovation, information demands, emotional demands, and physical demands – on 219,483 individuals of European ancestry. The job attainment had moderate to high pairwise genetic correlations, manifested by three latent factors: cognitive, emotional, and physical functions. The latent job function underlying job attainment traits represented a critical genetic path from educational attainment to income (P < 0.001). The higher job attainment was genetically correlated with better health outcomes (i.e., subject well-being, overall health rating, number of non-cancer illnesses etc. (|rg|: 0.14 to 0.51), similar to other SES indices; however, the genetic correlations exhibited opposite directions for physical demands (|rg|: 0.14 to 0.51) and were largely negligible for emotional demands. By adopting a fine-grained approach to capture specific job attainment phenotypes, our study represents an important step forward in understanding the shared genetic architecture among job attainment characteristics, other SES indices, and potential role in health outcomes.

The interval grey QFD method for new product development: Integrate with LDA topic model to analyze online reviews

As the development of the consuming environment, the way consumers give feedback on product experience changes from passive feedback to active reviews, and the development of artificial intelligence technology brings new possibilities for companies to obtain information on Customer Requirements (CRs) and Market Competition Information (CIs) needed for new product development. In the previous New Product Development (NPD) process, CRs and CIs often need to be corrected through market surveys and questionnaires. To acquire the corrected data from experienced experts without user and designer cognitive bias is very laborious and more subjective. Under the situation of key information shortage for business developers and NPD efficiency & accuracy cannot be guaranteed, online reviews provided a good source for data analysis to enhance the market competition. This study integrates Latent Dirichlet Allocation (LDA), Apriori algorithm, interval grey number, and Quality Function Deployment(QFD) techniques to propose the Latent Dirichlet Allocation-Interval Grey Number Quality Function Deployment (LDA-IGQFD) method, which can use text mining and analysis methods to obtain objective information about CRs and CIs contained in users’ online reviews, transformed them into data and input into Interval Grey Number Quality Function Deployment (IGQFD) to drive product development. LDA-IGQFD can help product developers identify CRs and Engineering Characteristics (ECs) important information and provide suggestions for product development. The aim of the research process under the LDA-IGQFD method is explained with the design of a dishwasher as an example, and the effectiveness and practicality of the proposed method is scientifically verified.