Principle Component Analysis Method Research Articles

An explorative study comparing skin surface lipids in the West Highland white terrier dog with and without atopic dermatitis

Background: The skin barrier is important in the pathogenesis of atopic dermatitis and stratum corneum lipids have a critical role. Skin surface lipids have been largely overlooked but also contribute to barrier function. An untargeted approach was used to compare the skin surface lipids from atopic and non-atopic West Highland White terrier dogs (WHWT). Objectives: The primary hypothesis was that a difference in the lipidome would exist. The secondary hypothesis was that affected and unaffected skin lipids would differ. Animals and methods: This prospective, cross-sectional, case-controlled study included thirty-nine privately owned WHWTs. Dogs were assigned to one of four disease status groups based on strict criteria. Samples for lipid analysis were collected from the skin surface of unaffected and affected sites. Lipid analysis was by untargeted liquid chromatography/mass spectrometry and utilised lipid identification software packages. Principle component analysis (PCA) and partial least-squares discriminant analysis (sPLS-DA) statistical methods analysed the association between the relative lipid abundance and disease status and affected and unaffected skin. Results: Samples for lipid analysis found 421 lipid soluble features of which ten lipids were positively identified. Statistical analysis could not distinguish between non-atopic and atopic dog but did reveal a statistically significant difference in the lipid profiles from affected and non-affected skin irrespective of disease status. Conclusions: A large array of unidentified lipids from the skin surface were found with a difference between affected and unaffected skin unrelated to disease status. Investigation into the lipidome of the skin surface is an emerging area of research with clinical and therapeutic applications.

Research on Stock Price Prediction Based on PCA-LSTM Model

When technical methods are used to establish LSTM stock prediction model, traditional methods often lead to poor generalization and poor prediction effect due to many input data variables selected, data information overlap, outliers have a great influence on training and other factors. To solve these problems, this paper proposes to use principle component analysis method to reduce the dimension of basic data, combine with stock related technical indicators KDJ and MACD as input data, and make prediction after adjusting the model according to stock characteristics. The experimental results show that the PCA-LSTM model reduces the average error of prediction, greatly reduces the running time, improves the stability of prediction, and more accurately predicts the closing price of Yaxing, which has application value.

Study of Active and Fossil Hydrothermal Systems in Ijen Caldera Complex and Merubetiri Mountains, East Java

Ore deposits formed at subduction zone are associated with magmatism activities that are represented by volcanic activities at the surface. In the Eastern Sunda Arc, one of active volcanic activity can be found in Ijen Caldera Complex. The complex has hydrothermal volcanic manifestations with a very acidic pH. Meanwhile in the south of Ijen Complex, there is ancient volcanic complex called Merubetiri Mountains that is proven to be well-mineralized at Tumpangpitu as high sulfidation and porphyry-style mineralizations. An understanding of hydrothermal activity and volcanic landforms in active volcanic systems can be used as a powerful tool to understanding hydrothermal systems. This paper presents the distinction between active hydrothermal systems and fossil hydrothermal systems as an exploration’s tool in ancient volcanic systems. The method used in this study is remote sensing with focus on volcanic landforms, geological structures, and distribution of alteration minerals. ASTER satellite imagery, Landsat 8 satellite imagery, and DEMNAS are used in this study. ASTER and Landsat 8 images are processed with Principle Component Analysis (PCA) and Direct Principle Component (DPC) methods to determine the distribution of alteration minerals that are associated with propylithic, argillic, advanced argillic, and silisic alterations. Semi-quantitative method is used to identify geological structures by automatic lineament detection. Meanwhile, qualitative method is used by manual lineaments delineation on the DEM imagery. Delineations of volcanic landforms in active and ancient volcanic complex use semi-quantitative methods include ridge lineaments and flow pattern. Then, volcanic landform is manually delineated by determining the distribution pattern of ridges, flow pattern, morphological texture, and cross-cutting relationship of volcanic products as a key in determine the eruption centers. The results show a relationship between volcanic distribution and the main stresses in the Ijen Caldera Complex with NE-SW direction, which is represented by the elliptic and elongation of volcanic depression zone, monogenetic volcano, and intrusion distribution. Geological structures that are found in the Ijen Caldera Complex show E-W and NE-SW directions. Meanwhile, Merubetiri complex shows E-W, NW-SE, and N-S structural direction patterns. The distribution of alteration minerals associated with silisic, argillic, and advance argillic in the Ijen Caldera Complex are found in the central zone of stratovolcano, intra-caldera zone, and structural zone that intersects the caldera. This shows that active hydrothermal system is related to volcanic activity and geological structures. Meanwhile, in the Merubetiri complex, alteration minerals are associated with the eruption centers, diorite/granodiorite intrusions, and NW-SE strike slip fault. The understandings of volcanic setting and volcanic landforms are very important in the early stages of exploration to determine the prospect of mineral deposits related to hydrothermal system.

Measuring Technical Efficiency of Hospitals affiliated with Shahrekord University of Medical Sciences, Using a Combination Method Data Envelopment Analysis (DEA) – Principle Component Analysis (PCA)

Background: Measuring the efficiency of hospitals due to the high proportion of budget allocated to them on the one hand, and the need to ensure the best practices regarding the use of scarce resources on the other hand, is of particular importance. The purpose of this study is to evaluate the technical efficiency of the affiliated hospitals of Shahrekord University of Medical Sciences by using a combination of Principal Component Analysis and (PCA) & Data Envelopment Analysis (DEA). Methods: This was an analytical and cross-sectional study measuring the technical efficiency of all 8 hospitals affiliated to Shahrekord University of Medical Sciences. The required information was collected from the medical records unit of each hospital. For better differentiation between efficient and inefficient units, and the increase of research accuracy and further differentiation between hospitals in terms of efficiency, at first, 17 indicators were selected to assess and adjust these parameters to 3 components proportional to the number of the hospitals by using PCA and SPSS 16 software. After doing the PCA, 7 studied input variables became 7 principal components among which the first input component reflecting the 83 % of scattering data was selected as principal input component, and for being more influenced by human resource variables, it was named as a human resource index. Furthermore, among the output variables, the first 2 output components, which represented 76% of the variance of the data, were selected as the 2 principal components of the output for the study, which were mostly affected by these variables, respectively, the number of admissions and length of stay. Then, the modified input and output components were entered into the software Windeap 2.1 and the technical efficiency of hospitals and their rank were calculated by assuming constant and variable efficiency with respect to the scale. In order to evaluate the effect of using the combined method instead of the conventional method of efficiency measurement, the results of the PCA - DEA method were compared with the results of the conventional DEA method. Results: The result of DEA on the selected components showed the capacity to upgrade the Technical Efficiency (TE) of hospitals is 15 % (TE: 0.852). Moreover, out of 8 hospitals, 1 hospital was increasing return to scale, 3 decreasing returns to scale and 4 constant returns to scale. The technical efficiency of 3 hospitals was 1 (TE = 1), 2 hospitals had the technical efficiency between 0.80 to 1 (1 > TE > 0.80) and that in 3 hospitals was less than 0.80 (TE < 0.80). The scale efficiency for 50 % of hospitals and the management efficiency for 62/5 % of them were equal 1. Conclusion: The average of total technical efficiency, management efficiency and scale efficiency were calculated to be 0.999, 1 and 0.999, respectively based on the usual comprehensive analysis method; while using the combined method, the average total technical efficiency, management efficiency and scale efficiency were 0.852, 0.947 and 0.902 respectively. The results confirm that the use of PCA method, due to its important role in reducing alignments, increases research accuracy and better differentiates between hospitals in terms of efficiency.

AUTENTIKASI CEPAT MADU HUTAN KALIMATAN TIMUR DENGAN ATR-FTIR SPEKTROSKOPI KOMBINASI ANALISIS KEMOMETRIKA

Honey adulteration is mostly conducted by the addition of sucrose. In this study, the authentication of honey was conducted using ATR-FTIR and chemometrics. Pure honey samples (MA) were collected from nine regions in East Kalimantan. The ATR-FTIR spectra of these samples were then compared to sucrose-adulterated honey (MS), which were prepared in the sucrose concentration from 2.5 to 50% (v / v).The data analysis was performed using chemometrics techniques: 1) Principle Component Analysis (PCA) method, 2) classification with Discriminant Analysis (DA), and 3) regression with (PCR) and (PLS). As a result, PCA was able to visualize the differences between MS and MA. DA analysis was able to distinguish MS and MA at wave numbers from 1200 to 800 cm-1 with 92.5% performance index. Quantitative calibration models of the sucrose-adulterated honey could be obtained from PLS and PCR, while the best calibration model was obtained with the PLS method from the 2nd derivative spectra. In summary, sucrose-adulterated honey from East Kalimantan can be authenticated using ATR-FTIR method in combination with chemometric analysis.

Simplification of point cloud data for large-scale ellipsoidal complex surface

Aiming at the issue that the current point cloud data simplification algorithm cannot accurately retain the subtle features of ellipsoidal complex surface, simplification algorithm of a point cloud data feature preservation based on principle component analysis (PCA) and adaptive mean shift (AMS) methods is proposed. First, K-means spatial clustering is used to make preliminary clustering and simplification of point cloud data. Second, the representative value of the subtle features of the point cloud data is calculated based on the PCA method. Then, according to the proposed improved adaptive mean shift method, the simplification of the point cloud data is developed. Finally, the point cloud data obtained by the denoising and smoothing of a large spherical crown workpiece collected at a certain equipment manufacturing site is used as the experimental object, and a comparative experiment is conducted between the proposed simplification algorithm and the k-means clustering algorithm, which proves the proposed algorithm can effectively maintain the subtle feature points of the ellipsoidal point cloud data.

Factors Contributing to Financial Literacy and Financial Inclusion among Women in Indian SHGs

The purpose of the study is to analyse the factors contributing to financial literacy and financial inclusion among women of self-help groups (SHGs) of Sisilo village in Balianta block of Bhubaneswar, India. A survey questionnaire was designed and administered to a sample of 100 women respondents in the age group of 18 to 59 years. The survey data were analysed using factorial analysis. Factor extraction was done by the method of principle component analysis. The three factors extracted which influenced financial literacy were identified as education level, awareness of bank and insurance products and household income. The four factors extracted which influenced financial inclusion were identified as product knowledge, risk level of products, insurance schemes for security, and availability of bank branch. A few of the factors are hindrances to financial literacy and financial inclusion which are gender difference, access to education, ethnicity, personal finance, lack of trust in financial institutions, distance and lack of proper guidance. The study will help bankers and policymakers to be aware of the factors that influence financial inclusion and financial literacy amongst women, and incorporate the same for implementing successful interventions for women SHGs in India.

Social inequalities in meal skipping patterns among children and adolescents: The CASPIAN-V study.

Background & AimThe appropriate meal consumption affects the children's and adolescents' health. Few studies have shown an association between the socioeconomic inequality and the eating behavior among children and adolescents. Therefore, the aim of this study was to assess the socioeconomic inequality in meal skipping patterns among children and adolescents.Materials & MethodsThe data were accessible through the fifth round of a school‐based program in Iran in 2015. In this cross‐sectional nationwide study, 14,286 students aged 7–18 years were selected via the multistage cluster sampling from 30 provinces of Iran. The Global School‐based Health Survey validated questionnaire was used to assess the socioeconomic variables and meal consumption patterns among children and adolescents. Socioeconomic status (SES) was computed using principle component analysis method. Multivariate logistic regression analysis was used to assess the socioeconomic inequality in meal skipping patterns.ResultsThe frequency of breakfast, lunch, and dinner skipping were 13.8% (95% CI: 13.3–14.5), 6.8% (95% CI: 6.4–7.2), and 7.5% (95% CI: 7.1–7.9), respectively. In multivariate model, living in a two‐parent family compared to living in a single‐parent family decreased the odds of breakfast skipping (OR: 0.53, 95% CI: 0.42–0.67)). Low SES level was associated with higher odds of skipping breakfast (OR: 1.79, 95% CI: 1.50–2.14) and dinner (OR: 1.80, 95% CI: 1.42–2.28). Moreover, maternal illiteracy and unemployment were associated with skipping breakfast, lunch, and dinner (all p values < 0.05).ConclusionSome demographic and socioeconomic characteristics, such as maternal illiteracy and unemployment, low SES level, and living in a single‐parent family were suggested as the main predictors of meal skipping patterns in children and adolescents.

Measuring national financial development: indexes and methods

Financial development of a country is a broad concept and can be measured in different ways. The article presents the indicators of financial development applicable for the development of the Financial Development Index (FDI) of a particular country. Financial development index could be used to rank and compare the level of financial development among countries or to test the relationship between financial development and economic growth. The paper points out the advantages and limitations of several methods including standardized method, Principle Component Analysis (PCA) method, Data Envelopment Analysis (DEA) method. The author also suggests to use Data Envelopment Analysis as an updated and advanced method to construct Financial Development Index. It is expected that this method will strengthen the quantitative approach in the research of financial development and economic growth.

Metabolomic profiling of active and inactive liver cystic echinococcosis

Cystic Echinococcosis (CE) is one of the life-threatening diseases worldwide. It is a parasitic zoonosis caused by tapeworms of the species Echinococcus granulosus sensu lato (s.l). The treatment options of CE vary from simple “watch and wait” approach to invasive treatment, based on the type and especially the nature of the cyst (active/inactive). Serological tests are inadequate to distinguish between active and inactive CE. A diagnostic reference that can determine whether the cyst is active or inactive can easily guide the treatment strategy.We aimed to test whether gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-quadropole time of flight mass spectrometry (LC-qTOF-MS) based metabolomics can establish a plasma metabolic fingerprint of CE patients and identify a diagnostic reference to discriminate active and inactive CE cysts.Metabolite concentrations were measured in plasma samples of 36 active CE patients, 17 inactive CE patients and 31 healthy controls. Multivariate statistical analysis on 232 identified metabolites obtained from two analytical platforms was performed by using principle component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) methods. The PLS-DA scores plot of the combined data set demonstrated a good separation between the groups. Compared to the healthy control group, decreased levels of squalene and increased levels of glyceric acid, 3-phosphoglycerate, glutamic acid, palmitoleic acid and oleic acid were determined in the CE patients. However, decreased levels of 3-phosphoglycerate and increased levels of 4-hydroxyphenylacetylglutamine, docosahexanoic acid were determined in active CE patients compared to the inactive CE patients.Determination of differences in metabolites may provide detailed understandings of potential metabolic process associated with active and inactive CE patients, and altered specific metabolic changes may provide some clues to obtain diagnostic reference for CE. This study has certain limitations: a. various factors affecting results of metabolomic studies such as lifestyle and dietary habits of the patients could not be fully controlled b. other infectious or malignant diseases of the liver should also be included as a positive control to evaluate the specificity of the diagnostic references.

Lanthanide Metal-Organic Framework-Based Fluorescent Sensor Arrays to Discriminate and Quantify Ingredients of Natural Medicine.

The discrimination and quantification of the ingredients from natural medicines are a challenging issue due to their complicated and various structures. Metal-organic frameworks (MOFs) have shown great promise in sensing applications. Here, we report a fluorescent sensor array for rapid identification of some natural compounds using a sensor array composed of four kinds of lanthanide (Eu3+ and Tb3+) fluorescent MOFs (Ln-MOFs), which have diversified fluorescent responses to 26 active/toxic compounds including 12 saponins, 7 flavonoids, 3 stilbenes, and 4 anthraquinones. The fluorescence of the Ln-MOFs after reaction with the compounds was summarized as datasets and processed by principle component analysis (PCA) and hierarchical cluster analysis (HCA) methods. The corresponding responses of the 4 types of compounds are well separated on 2D/3D PCA score plots and HCA dendrograms. We have also tested typical blind samples by concentration-dependent PCA, and an accuracy of 100% was obtained. In addition, the response mechanisms of the Ln-MOFs to the compounds were also studied. Compared with traditional methods using liquid chromatography-mass spectrometry, the developed fluorescent sensor array provides a more efficient and economic strategy to discriminate various active/toxic ingredients in natural medicine.

Pengenalan Wajah Menggunakan PCA dengan Memperhatikan Jumlah Data Latih dan Vektor Eigen

To find out if an employee is present, attendance is usually used. Attendance can be done in several ways, one of which is by filling in the attendance list that has been provided (manual attendance). However, this method is less effective because there is a possibility that employees who are not present will entrust attendance to employees who are present. Therefore, other ways are needed so that this does not happen. In this study, attendance was carried out using facial recognition. Face recognition is one of the fields used to recognize someone. A person's face usually has special characteristics that are easily recognized by people. These special characteristics are also called features. In this study, these features can be searched using the Principle Component Analysis (PCA) method. The PCA method is one of the methods used to produce features by reducing dimensions using eigenvectors from facial images (eigenface). The facial image used in this study consisted of 40 people with each person having 10 facial images with various expressions. Image data is divided into two parts, namely training data and test data. In this study, it is proposed to pay attention to the amount of training data and the number of eigenvectors used to get the best level of accuracy. From the research results, the highest level of accuracy occurs when the training data for each person is 7 and the test data for each person is 3 with an accuracy rate of 96.67%.

Evaluation of source water quality and the influencing factors: A case study of Macao

Macao's source water mainly comes from the West branch of Pearl River of China. Based on the monitored water quality data of raw water in Macao, this study adopted the National Sanitation Foundation Water Quality Index (NSFWQI) model to calculate the water quality index (WQI) from 2002 to 2019. The Principle Component Analysis (PCA) method was employed to select water quality parameters. The results show that the quality of raw water in Macao has been relatively stable in the period of interest and appeared an upward trend overall. The impact of influencing factors, including natural factors and human activities, on Macao source water has been discussed based on the acquired statistical results. This study can provide key intellectual and technical support for understanding Macao water quality data and their impacting factors. The potential benefits include early prediction of water quality issues and enhanced operational management in drinking water treatment plant.

Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models.

This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO3 (SN). Principle component analysis (PCA) and correlation analysis (CA) methods are integrated to assess the importance of the applied predictors and their relationship with the target. Research findings approved the potential of the grid-RF model as a marginal superior predictive model against the grid-SVM in terms of MAE, i.e., 3.29 and 3.34, respectively; moreover, the md scored the same, i.e., 0.93, which reveals the potential predictability for both. Nonetheless, grid-MARS and standalone MARS models remained likewise in their predictability. IC parameter demonstrated the highest influential among all the predictors with the highest value of importance in the case of all three evaluators. The solution pH and dose stands together with marginal differences in case of PCA method; however, solution pH and CT appeared with similarity impact using the PCA method.

Spatio-temporal changes of ecological vulnerability across the Qinghai-Tibetan Plateau

The Qinghai-Tibetan Plateau (QTP) has the most fragile ecosystems in the world. Over the past decades, QTP is suffering from increasing external pressures of climate change, human activities, and natural hazards, thus ecological vulnerability assessment is crucial for its sustainable development. This study proposes an objective and automatic framework to assess the ecological vulnerability in the QTP under the threats of mountain hazards, ecosystem degradation and human economic activities and then analyze its spatio-temporal patterns from 2000 to 2015. An ecological vulnerability index (EVI) is established by integrating natural and anthropic factors based on sub-systems of land resources, hydro-meteorology, topography, and social economics. Seventeen indicators are selected to reflect ecological conditions and their weights are determined by principle component analysis and entropy weighting methods. Then, the EVI values are automatically categorized into five vulnerability levels of potential, light, moderate, heavy, and very heavy to illustrate their spatio-temporal patterns across the QTP. Results indicated that spatial distributions of EVI across the QTP exhibited similar patterns during the study period at an overall heavy level. Among all the indicators, vegetation was the dominant driver for ecological vulnerability. Based on trend analyses during the study period, approximately 10.43% of the QTP, mainly distributed in Tibet Autonomous Region, experienced significant increase in ecological vulnerability, while 7.38%, mainly distributed in Qinghai Province, experienced significant ecological vulnerability declination. However, more detailed analyses showed that after the implementation of several ecological protection programs, the increasing trend of ecological vulnerability was eased and more regions experienced significantly decreasing vulnerability. This indicated the ecological restoration projects conducted by the government were efficient in reducing ecological vulnerability.

Classification and evaluation of National Marine Parks in the East China Sea coast

National ocean parks are important resources of China’s ecological civilization and environmental planning for sustainable development. In order to properly examine the protection and sustainable development of national ocean parks, an index of various elements (area, location, resources, economy, and environment) was created to evaluate the sustainable development conditions of 16 national ocean parks in the East China Sea coast between 2011 and 2017. Using the principle component analysis and cluster methods, this study comprehensively investigated the different characteristics and attributes of these parks. The results showed that a three-level classification system for national ocean parks is proposed based on the evaluation indicator system, which classifies these parks into three categories: ecosystem protection, tourism, and sustainable fisheries. Each category of park is featured by its own particular characteristics. Last, suggestions are given for future ecological protection and appropriate development of national ocean parks.

Does financial inclusion affect financial stability: Evidence from BRICS nations?

Looking the global trends of financial crises, it is obscure to draw any conclusion that whether greater financial inclusion is a threat or a safeguard for financial stability. In order to demystify the relationship the present study aims to examine the impact of financial inclusion on financial stability among the BRICS countries over the period 2005 through 2015. In order to accomplish this study we gathered data from various international data sources. Taking six different indicators of financial availability, accessibility and usability, this paper construct a single financial inclusion index for BRICS countries through Principle Component Analysis (PCA) method. Furthermore to know the causality between financial inclusion and financial stability, this study uses the Dumitrescue and Hurlin (2012) panel granger causality test. Additionally, to know the impact of financial inclusion on financial stability the system Generalized Method of Moments (GMM) estimator has been applied. The empirical findings of this study depicts that financial inclusion has a negative and significant effect on financial stability. The major reasons of adverse effects of financial inclusion on financial stability is due to rapid expansion of credit to the private sectors, erosion of credit standards of the banks, difficulties in credit assessment, increase in non-performing assets, credit defaults of the borrowers, and inadequate supervision of the banking sector. With context to the control variables we have used inflation (INF) and GDP growth rate (GDPGR) as our control variables. The results of the control variables show that inflation has negative and significant effect on financial stability, whereas GDP growth rate has positive and significant effect on financial stability of the BRICS countries. Based on our empirical findings this study recommends the policymakers to take appropriate policy measures before implementing pro financial inclusive policies. The financial institutions are advised to strength their banking efficiency to supervise the credit standard and to improve the credit assessment procedure and to provide credit to its customers based on their strict vigilance. The countries are advised to adopt stringent lawsuit and legal procedures for credit default of the borrowers.

A Nonstationary Process Monitoring Based on Mutual Information among Process Variables

Abstract In practical chemical industrial processes, the feed valve is automatically adjusted in response to the control systems and the production load will be adjusted with market situation and administrative regulations. Therefore, process data display nonstationary statistics in practical operation condition and cannot satisfy the ideal assumptions of traditional multivariate statistical methods that process is assumed operating around one preset steady state. Under normal operating conditions, fluctuations or adjustments will only affect the mean and standard deviation of process variables, but the correlation among process variables should follow its inherent mechanism model, whose feature can be statistically captured within certain range. In this paper, a nonstationary process monitoring based on mutual information among process variables is proposed. The Euclidean distance (ED) of eigenvalues of the mutual information matrix under normal operation conditions is calculated to obtain a statistic. Once a fault occurs, the changes in correlation among process variables will be reflected in the mutual information matrix and corresponding ED will exceed the threshold, by which process monitoring can be implemented. A numerical simulation example and a practical cracking process are applied as case studies. The results show a better performance on monitoring nonstationary process than traditional principle component analysis method.

The indicators and methods used for measuring urban liveability: a scoping review.

Liveability is a multi-dimensional and hierarchical concept which consists of various criteria and sub-criteria and may be evaluated in different ways. The aim of this study was to systematically review indicators and methods used for the evaluation of urban liveability in literature. The five-stage methodological framework of Arksey and O'Malley was used to conduct this scoping review. A systematic search of electronic databases, including Scopus, Medline (via PubMed), Embase, Web of Science and EBSCO was done until May 29, 2019. Web searching, searching reference lists and hand searching was also conducted to retrieve more relevant articles. Two reviewers screened the papers for eligibility based on the inclusion criteria and extracted their key data and reported them descriptively. Sixty seven (67) out of 3,599 papers met the selection criteria. This review showed five distinct domains considered to be important components of liveability. These were Economical, Environmental, Institutional, Social, and Governance (Political) domains. The most important subdomains (indices) which were frequently applied in various studies were Environmental friendliness and Sustainability, Socio-Cultural Conditions and Economic Vibrancy and Competitiveness. We also identified seven different methodologies and six ranking tools used for assessing urban liveability. Among the quantitative methods, three methods accounted for 89.6% of the articles. These methods were the Analytical hierarchy process and entropy (AHP; n=24; 50%), Factor analysis & Principle Component Analysis (FA & PCA; n=12; 25%) and Spatial Multi-criteria Decision-making Method (Spatial; n=7; 14.6%). Among the ranking tools used, three ranking tools accounted for 65.4% of the articles. These tools were the Livable City Scientific Evaluation Standards (LCSES; n=9; 34.6%), The Global Liveable Cities Index (GLCI; n=4; 15.4%) and the Economist Intelligence Unit (EIU; n=4; 15.4%). This paper discusses and summarizes the latest indicators and methods used for determining urban liveability. The information offered in the review can help future investigators to decide which method suits their purpose and situation better and measure urban liveability more systematically than before.

An Immune Gene-Related Five-lncRNA Signature for to Predict Glioma Prognosis.

BackgroundThe tumor immune microenvironment is closely related to the malignant progression and treatment resistance of glioma. Long non-coding RNA (lncRNA) plays a regulatory role in this process. We investigated the pathological mechanisms within the glioma microenvironment and potential immunotherapy resistance related to lncRNAs.MethodWe downloaded datasets derived from glioma patients and analyzed them by hierarchical clustering. Next, we analyzed the immune microenvironment of glioma, related gene expression, and patient survival. Coexpressed lncRNAs were analyzed to generate a model of lncRNAs and immune-related genes. We analyzed the model using survival and Cox regression. Then, univariate, multivariate, receiver operating characteristic (ROC), and principle component analysis (PCA) methods were used to verify the accuracy of the model. Finally, GSEA was used to evaluate which functions and pathways were associated with the differential genes.ResultsNormal brain tissue maintains a low-medium immune state, and gliomas are clearly divided into three groups (low to high immunity). The stromal, immune, and estimate scores increased along with immunity, while tumor purity decreased. Further, human leukocyte antigen (HLA), programmed cell death-1 (PDL1), T cell immunoglobulin and mucin domain 3 (TIM-3), B7-H3, and cytotoxic T lymphocyte-associated antigen-4 (CTLA4) expression increases concomitantly with immune state, and the patient prognosis worsens. Five immune gene-related lncRNAs (AP001007.1, LBX-AS1, MIR155HG, MAPT-AS1, and LINC00515) were screened to construct risk models. We found that risk scores are related to patient prognosis and clinical characteristics, and are positively correlated with PDL1, TIM-3, and B7-H3 expression. These lncRNAs may regulate the tumor immune microenvironment through cytokine–cytokine receptor interactions, complement, and coagulation cascades, and may promote CD8 + T cell, regulatory T cell, M1 macrophage, and infiltrating neutrophils activity in the high-immunity group. In vitro, the abnormal expression of immune-related lncRNAs and the relationship between risk scores and immune-related indicators (PDL1, CTLA4, CD3, CD8, iNOS) were verified by q-PCR and immunohistochemistry (IHC).ConclusionFor the first time, we constructed immune gene-related lncRNA risk models. The risk score may be a new biomarker for tumor immune subtypes and provide molecular targets for glioma immunotherapy.