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Abstract Background Mesonephric carcinoma (MC) is a very rare tumor with less than 70 cases had been reported. The rarity of MC has restricted its research, resulting in the lack of published guidelines. Objective To summarize the characteristics and construct an external‐validated nomogram to predict the survival of MC patients. Method Sixty‐four qualified patients derived from the Surveillance, Epidemiology, and End Results Plus database, and one patient from the Guangzhou Red Cross Hospital were enrolled. The entire cohort was randomly divided into a development (70%) and a validation cohort (30%). The Kaplan–Meier method and univariate and multivariate Cox regression analyses were applied. Two nomograms were established to predict the 3‐to‐8‐year survival probability of MC patients, which were evaluated by C‐index, ROC curves, DCA curves, and calibration plots. Results The average survival time of MC patients was 84.22 ± 50.66 months. No significant difference was shown among different groups of race, primary site, tumor differentiated grade, and FIGO stages, while different SEER stages did distinguish patients' survival time, which indicated that the SEER stage standards might be a better staging system in the MC patients than FIGO stage (p = .0835). Additional survival analyses showed that MC patients benefited from shorter waiting times to begin treatment, accepting surgery, regional lymph node examination, radiotherapy, and chemotherapy. Two nomograms were established, both of which got satisfied scores in C‐index, ROC curves, DCA curves, and calibration plots. Conclusion Sufficient regional lymph nodes examined, and applying radiotherapy in high‐risk patients are recommended in MC patients. Nomograms established in the present study had good predicting and discriminating capabilities, which would be helpful in patients' individual risk estimation, management, counseling, and follow‐up.

AbstractBackground Delayed diagnosis and inadequate treatment caused by limited biomarkers are associated with the outcomes of COVID-19 patients. It is necessary to identify other promising biomarkers and candidate targets for defining dysregulated inflammatory states.Methods The triggering receptors expressed on myeloid cell (TREM)-1 and TREM-2 expression from hospitalized COVID-19 patients were characterized using ELISA and flow cytometry, respectively. Their correlation with disease severity and contrast with the main clinical indicators were evaluated.Results Increased expression of soluble TREM-1 and TREM-2 in the plasma of COVID-19 patients was found compared to the control group. Moreover, membrane-bound TREM-1 and TREM-2 expression was upregulated on the cell surface of circulating blood T cells from COVID-19 patients. Correlation analysis showed that sTREM-2 levels were negatively correlated with PaO2/FiO2, but positively correlated with C-reactive protein (CRP), procalcitonin (PCT) and interleukin (IL)-6 levels. Receiver operating characteristic curve analysis indicated that the predictive efficacy of sTREM-1 and sTREM-2 was equivalent to CRP and IL-6, and a little better than absolute leukocyte or neutrophil count and PCT in distinguishing disease severity.Conclusion TREM-2 and TREM-1 are critical host immune factors that response to SARS-COV-2 infection and could serve as potential diagnostic biomarkers and therapeutic targets for COVID-19.

Influenza A virus still represents a noticeable epidemic risk to international public health at present, despite the extensive use of vaccines and anti-viral drugs. In the fight against pathogens, the immune defence lines consisting of diverse lymphocytes are indispensable for humans. However, the role of virus infection of lymphocytes and subsequent abnormal immune cell death remains to be explored. Different T cell subpopulations have distinct characterizations and functions, and we reveal the high heterogeneity of susceptibility to viral infection and biological responses such as apoptosis in various CD4+ T and CD8+ T cell subsets through single-cell transcriptome analyses. Effector memory CD8+ T cells (CD8+ TEM) that mediate protective memory are identified as the most susceptible subset to pandemic influenza A virus infection among primary human T cells. Non-productive infection is established in CD8+ TEM and naïve CD8+ T cells, which indicate the mechanism of intracellular antiviral activities for inhibition of virus replication such as abnormal viral splicing efficiency, incomplete life cycles and up-regulation of interferon-stimulated genes in human T cells. These findings provide insights into understanding lymphopenia and the infectious mechanisms of pandemic influenza A virus and broad immune host–pathogen interactional atlas in primary human T cells.

Abstract Scientific publications present biological relationships but are structured for human reading, making it difficult to use this resource for semantic integration and querying. Existing databases, on the other hand, are well structured for automated analysis, but do not contain comprehensive biological knowledge. We devised an approach for constructing comprehensive knowledge graphs from these two types of resources and applied it to investigate relationships between pre-/probiotics and microbiota–gut–brain axis diseases. To this end, we created (i) a knowledge base, dubbed ppstatement, containing manually curated detailed annotations, and (ii) a knowledge base, called ppconcept, containing automatically annotated concepts. The resulting Pre-/Probiotics Knowledge Graph (PPKG) combines these two knowledge bases with three other public databases (i.e. MeSH, UMLS and SNOMED CT). To validate the performance of PPKG and to demonstrate the added value of integrating two knowledge bases, we created four biological query cases. The query cases demonstrate that we can retrieve co-occurring concepts of interest, and also that combining the two knowledge bases leads to more comprehensive query results than utilizing them separately. The PPKG enables users to pose research queries such as “which pre-/probiotics combinations may benefit depression?”, potentially leading to novel biological insights.

Abstract Background T cell lymphopenia was a significant characteristic of severe influenza infection and it was associated with the functional changes of T cells. It is necessary to clarify the T cells characteristics of kinetic changes and their correlation with disease severity. Methods In a cohort of hospitalized influenza patients with varying degrees of severity, we characterized lymphocyte populations using flow cytometry. Results The numbers of cycling (Ki67+) T cells at the acute phase of severe influenza were higher, especially in the memory (CD45RO+) T cell subsets. T cells from hospitalized influenza patients also had significantly higher levels of the exhausted marker PD-1. Cycling status of T cells was associated with T cell activation during the acute phase of influenza infection. The recruitment of cycling and activated (CD38+HLA-DR+) CD8+ T cells subset is delayed in severe influenza patients. Conclusions The increased numbers of cycling memory (Ki67+CD45RO+) T cells subsets and delayed kinetics of activated (CD38+HLA-DR+) CD8+ T cells, could serve as possible biological markers for disease severity.

BackgroundResident phenotypic memory CD8+ T cells are crucial for immune defense against pathogens. However, little is known about the potential transitions and regulation mechanisms of their function after influenza virus infection and reinfection. In this study, we utilized integrated transcriptome data and in vivo experiments to investigate the key characteristics behind it.MethodsTwo single-cell RNA sequencing (scRNA-seq) datasets of lung CD8+ T cells and one RNA-seq dataset of lung tissue after infection or reinfection were included. After Seurat procedures classifying CD8+ T subsets, the scCODE algorithm was used to identify the differentially expressed genes for GSVA, GO, and KEGG pathway enrichment. Monocle 3 and CellChat were used to infer pseudotime cell trajectory and cell interactions. The ssGSEA method was used to estimate the relative proportions of immune cells. The findings were confirmed with a mouse model via flow cytometry and RT-PCR analysis.ResultsOur study refined the landscape of CD8+ T-cell subsets in the lung, showing that CD8+ Trm cells accumulated in the lung within 14 days after influenza infection. The classical CD8+ Trm cells co-expressed a high level of CD49a and even maintained 90 days after primary infection. The ratio of CD8+ Trm cells decreased 1 day after influenza reinfection, which may be parallel with their potential transition into effector types, as observed in trajectory inference analysis. KEGG analysis suggested that PD-L1 expression and PD-1 checkpoint pathway were upregulated in CD8+ Trm cells on day 14 after infection. GO and GSVA analyses revealed that PI3K-Akt-mTOR and type I interferon signaling pathways were enriched in CD8+ Tem and Trm cells after reinfection. Additionally, CCL signaling pathways were involved in cell interaction between CD8+ Trm cells and other cells, with Ccl4-Ccr5 and Ccl5-Ccr5 ligand/receptor pairs being important between CD8+ Trm and other memory subsets after infection and reinfection.ConclusionOur data suggest that resident memory CD8+ T cells with CD49a co-expression account for a large proportion after influenza infection, and they can be rapidly reactivated against reinfection. Function differences exist in CD8+ Trm and Tem cells after influenza infection and reinfection. Ccl5-Ccr5 ligand/receptor pair is important in cell interactions between CD8+ Trm and other subsets.

BackgroundMale breast cancer (MBC) is rare, which has restricted prospective research among MBC patients. With effective treatments, the prognosis of MBC patients has improved and developing a second primary malignancy (SPM) has become a life-threatening event for MBC survivors. However, few studies have focused on the prognosis of MBC patients and looked into the SPM issue in MBC survivors.MethodWe reviewed MBC patients diagnosed between 1990 and 2016 from the latest Surveillance, Epidemiology, and End Results (SEER) Plus database. Competing risk models and nomograms were conducted for predicting the risk of cancer-specific death and SPM occurrence. C-indexes, calibration curves, ROC curves, and decision curve analysis (DCA) curves were applied for validation.ResultA total of 1,843 MBC patients with complete information were finally enrolled and 60 (3.26%) had developed an SPM. Prostate cancer (40%) was the most common SPM. The median OS of all the enrolled patients was 102.41 months, while the median latency from the initial MBC diagnosis to the subsequent diagnosis of SPM was 67.2 months. The patients who suffered from an SPM shared a longer OS than those patients with only one MBC (p = 0.027). The patients were randomly divided into the development cohort and the validation cohort (at a ratio of 7:3). The Fine and Gray competing risk model was used to identify the risk factors. Two nomograms were constructed and validated to predict the 5-year, 8-year, and 10-year survival probability of MBC patients, both of which had good performance in the C-index, ROC curves, calibration plots, and DCA curves, showing the ideal discrimination capability and predictive value clinically. Furthermore, we, for the first time, constructed a nomogram based on the competing risk model to predict the 5-year, 8-year, and 10-year probability of developing an SPM in MBC survivors, which also showed good discrimination, calibration, and clinical effectiveness.ConclusionWe, for the first time, included treatment information and clinical parameters to construct a nomogram to predict not only the survival probability of MBC patients but also the probability of developing an SPM in MBC survivors, which were helpful in individual risk estimation, patient follow-up, and counseling in MBC patients.

Abstract Semantic web (SW) technology has been widely applied to many domains such as medicine, health care, finance, geology. At present, researchers mainly rely on their experience and preferences to develop and evaluate the work of SW technology. Although the general architecture (e.g., Tim Berners-Lee’s Semantic Web Layer Cake) of SW technology was proposed many years ago and has been well-known, it still lacks a concrete guideline for standardizing the development of SW technology. In this paper, we propose an SW technology index to standardize the development for ensuring that the work of SW technology is designed well and to quantitatively evaluate the quality of the work in SW technology. This index consists of 10 criteria that quantify the quality as a score of $$0{-}10$$ 0 - 10 . We address each criterion in detail for a clear explanation from three aspects: (1) what is the criterion? (2) why do we consider this criterion and (3) how do the current studies meet this criterion? Finally, we present the validation of this index by providing some examples of how to apply the index to the validation cases. We conclude that the index is a useful standard to guide and evaluate the work in SW technology.

Quantum key distribution (QKD) has rapidly developed recently. The repetition frequency of the QKD system increases from tens or hundreds of MHz to GHz. A laser diode (LD) operating in gain-switched mode is widely used as a weak coherent light source in a QKD system. We present an LD driver circuit fabricated in 0.18 μm SiGe heterojunction bipolar transistor bipolar complementary metal oxide semiconductor technology. The circuit can operate at frequencies up to 2.5 GHz, satisfying the requirements of high-speed laser drives in practical QKD systems. The output current of the driver circuit can reach 100 mA when driving an LD whose input equivalent resistance is ∼21 Ω. The extinction ratio of the 1550-nm distributed feedback LD light source driven by our driver circuit reaches 23 dB at the operating frequency of 2.5 GHz, meeting the requirements of QKD systems. This circuit will be used in miniaturized QKD systems.

Abstract A nonlinear parabolic equation from a two-phase problem is considered in this paper. The existence of weak solutions is proved by the standard parabolically regularized method. Different from the related papers, one of diffusion coefficients in the equation, b ( x ) $b(x)$ , is degenerate on the boundary. Then the Dirichlet boundary value condition may be overdetermined. In order to study the stability of weak solution, how to find a suitable partial boundary value condition is the foremost work. Once such a partial boundary value condition is found, the stability of weak solutions will naturally follow.

Family with sequence similarity 83 (FAM83) is a newly identified family of oncogenes whose members play important roles in signaling and cancer progression. However, a thorough understanding of the FAM83 family in tumors is still lacking. Here, we performed a comprehensive analysis of the expression levels of the FAM83 family across cancers and patient prognoses using bioinformatics methods. We found that the expression levels of FAM83 family genes were upregulated in most tumors, and importantly, high expression levels of FAM83 family genes were related to poor prognosis in most tumors. In addition, we analyzed the relationship of FAM83 family genes with immune subtypes and the tumor microenvironment (TME). The results showed that FAM83 family genes were significantly associated with immune infiltrative subtypes and to varying degrees with the level of stromal cell infiltration and tumor stem cells. Finally, our study also showed the relationship between FAM83 family genes and drug sensitivity. Therefore, this pan-cancer analysis demonstrates the critical role of FAM83 family genes in tumor development and provides new clues for therapeutic strategies for cancer.

BackgroundInfluenza and COVID-19 are respiratory infectious diseases that are characterized by high contagiousness and high mutation and pose a serious threat to global health. After Influenza A virus (IAV) and SARS-CoV-2 infection, severe cases may develop into acute lung injury. Immune factors act as an important role during infection and inflammation. However, the molecular immune mechanisms still remain unclear. We aimed to explore immune-related host factors and core biomarker for severe infection, to provide a new therapeutic target of host factor in patients.MethodsGene expression profiles were obtained from Gene Expression Omnibus and the Seurat R package was used for data process of single-cell transcriptome. Differentially expressed gene analysis and cell cluster were used to explore core host genes and source cells of genes. We performed Gene Ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, and gene set enrichment analysis to explore potential biological functions of genes. Gene set variation analysis was used to evaluate the important gene set variation score for different samples. We conduct Enzyme-linked immunosorbent assay (ELISA) to test plasma concentrations of Lipocalin 2 (LCN2).ResultsMultiple virus-related, cytokine-related, and chemokine-related pathways involved in process of IAV infection and inflammatory response mainly derive from macrophages and neutrophils. LCN2 mainly in neutrophils was significantly upregulated after either IAV or SARS-CoV-2 infection and positively correlated with disease severity. The plasma LCN2 of influenza patients were elevated significantly compared with healthy controls by ELISA and positively correlated with disease severity of influenza patients. Further bioinformatics analysis revealed that LCN2 involved in functions of neutrophils, including neutrophil degranulation, neutrophil activation involved in immune response, and neutrophil extracellular trap formation.ConclusionThe neutrophil-related LCN2 could be a promising biomarker for predicting severity of patients with IAV and SARS-CoV-2 infection and may as a new treatment target in severe patients.

BackgroundPeople with suicidal ideation post suicide-related information on social media, and some may choose collective suicide. Sina Weibo is one of the most popular social media platforms in China, and “Zoufan” is one of the largest depression “Tree Holes.” To collect suicide warning information and prevent suicide behaviors, researchers conducted real-time network monitoring of messages in the “Zoufan” tree hole via artificial intelligence robots.ObjectiveTo explore characteristics of time, content and suicidal behaviors by analyzing high suicide risk comments in the “Zoufan” tree hole.MethodsKnowledge graph technology was used to screen high suicide risk comments in the “Zoufan” tree hole. Users' level of activity was analyzed by calculating the number of messages per hour. Words in messages were segmented by a Jieba tool. Keywords and a keywords co-occurrence matrix were extracted using a TF-IDF algorithm. Gephi software was used to conduct keywords co-occurrence network analysis.ResultsAmong 5,766 high suicide risk comments, 73.27% were level 7 (suicide method was determined but not the suicide date). Females and users from economically developed cities are more likely to express suicide ideation on social media. High suicide risk users were more active during nighttime, and they expressed strong negative emotions and willingness to end their life. Jumping off buildings, wrist slashing, burning charcoal, hanging and sleeping pills were the most frequently mentioned suicide methods. About 17.55% of comments included suicide invitations. Negative cognition and emotions are the most common suicide reason.ConclusionUsers sending high risk suicide messages on social media expressed strong suicidal ideation. Females and users from economically developed cities were more likely to leave high suicide risk comments on social media. Nighttime was the most active period for users. Characteristics of high suicide risk messages help to improve the automatic suicide monitoring system. More advanced technologies are needed to perform critical analysis to obtain accurate characteristics of the users and messages on social media. It is necessary to improve the 24-h crisis warning and intervention system for social media and create a good online social environment.

Photoelectrochemical immunoassays incorporating specific antigen-antibody recognition reactions with the photon-electron conversion capabilities of photocatalysts have been developed for biomarker detection, but most involve bulky and expensive equipment and are unsuitable for point-of-care testing. Herein, a portable smartphone-based photoelectrochemical immunoassay was innovatively designed for the on-site detection of breast cancer biomarkers (human epidermal growth factor receptor 2; HER2). The system consists of a split-type immunoassay mode, disposable screen-printed electrode covered with hierarchical Co9S8@ZnIn2S4 heterostructures, an integrated circuit board, and a Bluetooth smartphone equipped with a specially designed app. Using alkaline phosphatase (ALP) catalytic strategy to in situ generate ascorbic acid (AA) for electron-donating toward Co9S8@ZnIn2S4 heterostructures, an immunoreaction was successfully constructed for the HER2 detection in the real sample due to the positive correlation of the photocurrent signal to electron donor concentration. Differential charge density indicates that the formation of Co9S8@ZnIn2S4 heterojunction can facilitate the flow of charges in the interface and enhance the photocurrent of the composite. More importantly, the measured photocurrent signal can be wirelessly transmitted to the software and displayed on the smartphone screen to obtain the corresponding HER2 concentration value. The photocurrent values linearly with the logarithm of HER2 concentrations range spanned from 0.01 ng/mL to 10 ng/mL with a detection limit of 3.5 pg/mL. Impressively, the clinical serum specimen results obtained by the proposed method and the wireless sensing device are in good agreement with the enzyme-linked immunosorbent assay (ELISA).

“Zou Fan” is currently the largest “tree hole” on Weibo, where people having suicidal ideation often express their thoughts and use this channel to seek support. Therefore, early suicide monitoring and timely crisis intervention based on artificial intelligence technology are needed for this social media user group. This research was based on the knowledge graph technology, whereby “Tree Hole Intelligent Agent” (i.e., Artificial Intelligence Program) was used to identify “Zou Fan Tree Hole” users at high risk for suicide, and then, the “Tree Hole Action” carried out proactive suicide crisis intervention with them. The “Tree Hole Action” has temporarily prevented 3,629 potential suicides. The “Tree Hole Action” plays a significant role in suicide risk monitoring and crisis intervention for social media users and has been seen to have an important social impact.

BackgroundWeb-based social media provides common people with a platform to express their emotions conveniently and anonymously. There have been nearly 2 million messages in a particular Chinese social media data source, and several thousands more are generated each day. Therefore, it has become impossible to analyze these messages manually. However, these messages have been identified as an important data source for the prevention of suicide related to depression disorder. ObjectiveWe proposed in this paper a distant supervision approach to developing a system that can automatically identify textual comments that are indicative of a high suicide risk. MethodsTo avoid expensive manual data annotations, we used a knowledge graph method to produce approximate annotations for distant supervision, which provided a basis for a deep learning architecture that was built and refined by interactions with psychology experts. There were three annotation levels, as follows: free annotations (zero cost), easy annotations (by psychology students), and hard annotations (by psychology experts). ResultsOur system was evaluated accordingly and showed that its performance at each level was promising. By combining our system with several important psychology features from user blogs, we obtained a precision of 80.75%, a recall of 75.41%, and an F1 score of 77.98% for the hardest test data. ConclusionsIn this paper, we proposed a distant supervision approach to develop an automatic system that can classify high and low suicide risk based on social media comments. The model can therefore provide volunteers with early warnings to prevent social media users from committing suicide.

Natural disasters have frequently occurred and caused great harm. Although the remote sensing technology can effectively provide disaster data, it still needs to consider the relevant information from multiple aspects for disaster analysis. It is hard to build an analysis model that can integrate the remote sensing and the large-scale relevant information, particularly at the sematic level. This paper proposes a disaster prediction knowledge graph for disaster prediction by integrating remote sensing information, relevant geographic information, with the expert knowledge in the field of disaster analysis. This paper constructs the conceptual layer and instance layer of the knowledge graph by building a common semantic ontology of disasters and a unified spatio-temporal framework benchmark. Moreover, this paper represents the disaster prediction model in the forms of knowledge of disaster prediction. This paper demonstrates experiments and cases studies regarding the forest fire and geological landslide risk. These investigations show that the proposed method is beneficial to multi-source spatio-temporal information integration and disaster prediction.

Background: Traditional Chinese Medicine is complementary and an alternative to modern medicine. The combination therapies of herbal products with disease-modifying anti-rheumatic drugs are gradually and widely adopted in the management of rheumatoid arthritis (RA) in China.Purpose: To evaluate the efficacy and safety of Huayu-Qiangshen-Tongbi (HQT) decoction, a Chinese medicine formula, combined with methotrexate (MTX) in the treatment of patients with active RA, in comparison with the combination therapy of MTX with leflunomide (LEF).Methods: This pilot study was a monocenter, open-label, randomized controlled trial with two parallel arms. Ninety patients with active RA were randomly allocated to receive either HQT at a dose of 250 ml twice daily or LEF at a dose of 20 mg once daily, and all participants received MTX at a dose of 10–15 mg once weekly. The primary efficacy endpoint was the proportion of patients who achieved a 20% improvement in the American College of Rheumatology criteria (ACR20) after a 24-week treatment.Results: 84.4% (76/90) patients completed the 24-week observation. In the intention-to-treat analysis, the percentage values of patients achieving the ACR20 response criteria were 72.1% (31/43) in MTX + HQT group and 74.4% (32/43) in MTX + LEF group (p = 0.808). No significant difference was observed in other parameters, including ACR50, ACR70, clinical disease activity index good responses, European League Against Rheumatism good response, remission rate, and low disease activity rate. The results of the per-protocol analysis showed consistency with those of the intention-to-treat analysis. The mean change from baseline at week 24 for the van der Heijde modified total sharp score had no significant difference between two groups (3.59 ± 4.75 and 1.34 ± 8.67 in the MTX + HQT group and MTX + LEF group, respectively, p = 0.613). The frequency of adverse events was similar in both groups (11 cases in the MTX + HQT and 17 cases in the MTX + LEF, p > 0.05).Conclusions: In patients with active RA, treatment with the combination of HQT and MTX was associated with improvement in signs, symptoms, and physical function. With a beneficial clinical response and acceptable tolerability, HQT or other Chinese medicine formula may be a good therapeutic option in combination with MTX for RA treatment.Trial registration: Chinese Clinical Trails Registry, ChiCTR-INR-16009031, Registered on 15th August 2016, http://www.chictr.org.cn/enindex.aspx.

With the rapid development of wireless communications and mobile devices with location capability, location-based services (LBS) have been extensively used in almost all social and business domains. In terms of privacy and efficiency, there are two main limitations in the existing work that have to be addressed. First, they fail to preserve the location and content privacy simultaneously. Second, they do not support the range queries. In this paper, we propose a secure and efficient privacy-preserving range query protocol in the LBS, which satisfies the three requirements of the location privacy and content privacy protection, query efficiency, and scalable index size. To achieve location privacy and content privacy, we adopt the prefix membership verification scheme to encode index elements and a bloom filter to store index elements. To achieve query efficiency and scalable index size, a balanced dynamic binary tree structure called $DBtree$ is proposed. Furthermore, to improve the query efficiency, we propose both the index element encoding optimization and traversal optimization algorithms. Finally, we evaluate our scheme both on the simulated and real-world datasets. The experimental results demonstrate the security and efficiency of our scheme.

Monitoring systems for the safety of building structure (SBS) can provide people with important data related to main supporting points in a building and then help people to make a reasonable maintenance schedule. However, more and more data bring a challenge for data management and data mining. In order to meet this challenge, under the framework of Wisdom Web of Things (W2T), we design a monitoring system for the SBS by using the semantic and the multisource data fusion technologies. This system establishes a dynamical data cycle among the physical world (buildings), the social world (humans), and the cyber world (computers) and provides various services in the monitoring process to alleviate engineers’ workload. Furthermore, all data in the cyber world are organized as the raw data, the semantic information, and the multisource knowledge. Based on this organization, we can concentrate on the data fusion from the viewpoints of time, space, and multisensor. At last, a prototype system powered by the semantic platform LarKC is tested from the aspects of sample performance and time consumption. In particular, noisy data (i.e., inconsistent, abnormal, or error data) are detected through the fusion of multisource knowledge, and some rule-based reasoning is conducted to provide personalized service.