Principle Component Analysis Research Articles

Retraction Note: A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm

Retraction Note: A cloud-based recommendation service using principle component analysis–scale-invariant feature transform algorithm

Comparative metabolite analysis of Piper sarmentosum organs approached by LC–MS-based metabolic profiling

Piper sarmentosum Roxb. (Piperaceae) is a traditional medicinal and food plant widely distributed in the tropical and subtropical regions of Asia, offering both health and culinary benefits. In this study the secondary metabolites in different organs of P. sarmentosum were identified and their relative abundances were characterized. The metabolic profiles of leaves, roots, stems and fruits were comprehensively investigated by liquid chromatography high-resolution mass spectrometry (LC-HR-MS) and the data subsequently analyzed using multivariate statistical methods. Manual interpretation of the tandem mass spectrometric (MS/MS) fragmentation patterns revealed the presence of 154 tentatively identified metabolites, mostly represented by alkaloids and flavonoids. Principle component analysis and hierarchical clustering indicated the predominant occurrence of flavonoids, lignans and phenyl propanoids in leaves, aporphines in stems, piperamides in fruits and lignan-amides in roots. Overall, this study provides extensive data on the metabolite composition of P. sarmentosum, supplying useful information for bioactive compounds discovery and patterns of their preferential biosynthesis or storage in specific organs. This can be used to optimize production and harvesting as well as to maximize the plant’s economic value as herbal medicine or in food applications.Graphical

In-depth characterization of particulate matter in a highly polluted urban environment at the foothills of Himalaya-Karakorum Region.

In recent years, the rising levels of atmospheric particulate matter (PM) have an impact on the earth's system, leading to undesirable consequences on various aspects like human health, visibility, and climate. The present work is carried out over an insufficiently studied but polluted urban area of Peshawar, which lies at the foothills of the famous Himalaya and Karakorum area, Northern Pakistan. The particulate matter with an aerodynamic diameter of less than 10µm, i.e., PM10 are collected and analyzed for mineralogical, morphological, and chemical properties. Diverse techniques were used to examine the PM10 samples, for instance, Fourier transform infrared spectroscopy, x-ray diffraction, and scanning electron microscopy along with energy-dispersive x-ray spectroscopy, proton-induced x-ray emission, and an OC/EC carbon analyzer. The 24h average PM10 mass concentration along with standard deviation was investigated to be 586.83 ± 217.70µg/m3, which was around 13 times greater than the permissible limit of the world health organization (45µg/m3) and 4 times the Pakistan national environmental quality standards for ambient PM10 (150µg/m3). Minerals such as crystalline silicate, carbonate, asbestiform minerals, sulfate, and clay minerals were found using FTIR and XRD investigations. Microscopic examination revealed particles of various shapes, including angular, flaky, rod-like, crystalline, irregular, rounded, porous, chain, spherical, and agglomeration structures. This proved that the particles had geogenic, anthropogenic, and biological origins. The average value of organic carbon, elemental carbon, and total carbon is found to be 91.56 ± 43.17, 6.72 ± 1.99, and 102.41 ± 44.90µg/m3, respectively. Water-soluble ions K+ and OC show a substantial association (R = 0.71). Prominent sources identified using Principle component analysis (PCA) are anthropogenic, crustal, industrial, and electronic combustion. This research paper identified the potential sources of PM10, which are vital for preparing an air quality management plan in the urban environment of Peshawar.

Principle Component Analysis for Forecasting of Pre-harvest Rapeseed and Mustard Yield Based on Meteorological Parameters

The pre-harvest forecast model was developed using time series data on Rapeseed and Mustard yields as well as weekly data on six weather variables from the 40th SMW of one year to the 8th SMW of the next year, which covers the years 1990–1991 to 2014–2015. Multiple regression and principal component analysis are two statistical techniques that have been reported for creating pre-harvest forecast models. The most accurate model found by applying step-by-step regression analysis to weekly weather data was based on adj R2, RMSE, and%SE. One and a half months prior to harvest, these models can be utilised to obtain a trustworthy forecast of the yield of Rapeseed and Mustard.

GWmodelS: a standalone software to train geographically weighted models

ABSTRACT With the recent increase in studies on spatial heterogeneity, geographically weighted (GW) models have become an essential set of local techniques, attracting a wide range of users from different domains. In this study, we demonstrate a newly developed standalone GW software, GWmodelS using a community-level house price data set for Wuhan, China. In detail, a number of fundamental GW models are illustrated, including GW descriptive statistics, basic and multiscale GW regression, and GW principle component analysis. Additionally, functionality in spatial data management and batch mapping are presented as essential supplementary activities for GW modeling. The software provides significant advantages in terms of a user-friendly graphical user interface, operational efficiency, and accessibility, which facilitate its usage for users from a wide range of domains.

An efficient method for rapid screening of triterpenoid saponins in three Glycyrrhiza species using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry combined with mass defect filtering

Triterpenoid saponins, a major bioactive component of liquorice, possess high hydrophilicity and often co-occur with other impurities of similar polarity. Additionally, subtle structural differences of some triterpenoid saponins bring challenges to comprehensive characterisation. In this study, triterpenoid saponins of three Glycyrrhiza species were systematically analysed using rapid resolution liquid chromatography quadrupole time-of-flight mass spectrometry (RRLC-Q-TOF-MS) coupled with mass defect filtering (MDF). Firstly, comprehensive date acquisition was achieved using RRLC-Q-TOF-MS. Secondly, a polygonal MDF method was established by summarizing known and speculated substituents and modifications based on the core structure to rapidly screen potential triterpenoid saponins. Thirdly, based on the fragmentation patterns of reference compounds, an identification strategy for characterisation of triterpenoid saponins was proposed. The strategy divided triterpenoid saponins into three distinct classes. By this strategy, 98 triterpenoid saponins including 10 potential new ones were tentatively characterised. Finally, triterpenoid saponins of three Glycyrrhiza species were further analysed using principle component analysis (PCA) and orthogonality partial least squares discriminant analysis (OPLS-DA). Among these, 18 compounds with variable importance in projections (VIP) > 1.0 and P values < 0.05 were selected to distinguish three Glycyrrhiza species. Overall, our study provided a reference for quality control and rational use of the three species.

A nonparametric degradation modeling method for remaining useful life prediction with fragment data

Condition-based maintenance (CBM) is an effective way to keep the safety of industrial equipment by predicting the remaining useful life (RUL) and scheduling the maintenance plan before failure. Condition monitoring data is the basis of health state evaluation and RUL prediction. In ideal cases, the monitoring data are collected consecutively starting from the healthy stage until the end of the lifetime. In real industrial cases, however, fragment data often exist due to the interruption of monitoring and/or the loss of sensor readings. The major characteristic of fragment data is that they only record a random period of degradation process. The initial degradation time information is generally lost. Therefore, it is unable to be modeled using the common time-dependent modeling framework. To deal with the above issue, this paper proposes a nonparametric degradation modeling method for RUL prediction with fragment data. In this method, a new state-dependent degradation modeling framework is constructed via two-step axis transform. It formulates the RUL using a function depending on the health state. Based on functional principle component analysis (FPCA), a principal analysis via maximum likelihood estimation (PAMLE) algorithm is developed to recover the missing data of failed units. In addition, a RUL prediction-oriented optimization (POO) algorithm is proposed to predict the RUL of in-service units based on a piece of fragment data. Consequently, the proposed method is capable of dealing with the issues of both data recovery and RUL prediction. The effectiveness of the method is demonstrated using a fatigue crack growth dataset and a lithium-ion (Li-ion) battery degradation dataset.

Trace element concentrations and chemical zoning of spodumene from magmatic and hydrothermal origins

Spodumene is a major lithium ore mineral mined from granitic pegmatites and can crystallize (1) from magma, (2) from hydrothermal fluid in miarolitic cavities or fracture-filling units, or (3) as a product of petalite breakdown. Although spodumene’s purity influences the economic value of lithium ore, its trace-element geochemistry is not entirely understood. This study investigated 24 unaltered spodumene samples from 21 localities formed through each of these three modes of origin. The chemical zoning of each spodumene sample was characterized using cathodoluminescence (CL), micro-X-Ray fluorescence mapping (µXRF), and/or qualitative laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) line scans, whereas the concentrations of trace elements were quantified using LA-ICP-MS point analyses. Intra-crystalline chemical zoning was present in all three spodumene types as either oscillatory growth bands and/or sector zoning. For the four most abundant transition and post-transition metals analyzed (Fe, Mn, Ga, Sn), the concentrations ranged from below detection to 10510 ppm (Fe), 1850 ppm (Mn), 300 ppm (Ga), 320 ppm (Sn), whereas Rb and Cs were below detection. Magmatic spodumene had the highest mean concentrations of Fe (2345 ppm) and Sn (94 ppm), hydrothermal spodumene had the highest mean concentrations of Mn (641 ppm) and Ga (94 ppm), and spodumene formed from the breakdown of petalite consistently had the lowest mean concentrations, though there was significant overlap in the datasets. In addition, texturally similar spodumene-quartz intergrowths of primary and secondary origins were distinguished via trace-element geochemistry. Principle component analysis of Fe, Mn, Ga, Sn, Ti, and Zn values from all 325 LA-ICP-MS measurements showed discrimination by CL color, as well as color in handsample for gem-varieties of hydrothermal spodumene.

Light emitting diode effect of red, blue, and amber light on photosynthesis and plant growth parameters

The visible light spectrum (400–700 nm) powers plant photosynthesis and innumerable other biological processes. Photosynthesis curves plotted by pioneering photobiologists show that amber light (590–620 nm) induces the highest photosynthetic rates in this spectrum. Yet, both red and blue light are viewed superior in their influence over plant growth. Here we report two approaches for quantifying how light wavelength photosynthesis and plant growth using light emitting diodes (LEDs). Resolved quantum yield spectra of tomato and lettuce plants resemble those acquired earlier, showing high quantum utilization efficiencies in the 420–430 nm and 590–620 nm regions. Tomato plants grown under blue (445 nm), amber (595 nm), red (635 nm), and combined red-blue-amber light for 14 days show that amber light yields higher fresh and dry mass, by at least 20%. Principle component analysis shows that amber light has a more pronounced and direct effect on fresh mass, whereas red light has a major effect on dry mass. These data clarify amber light's primary role in photosynthesis and suggest that bandwidth determines plant growth and productivity under sole amber lighting. Findings set precedence for future work aimed at maximizing plant productivity, with widespread implications for controlled environment agriculture.

Ensemble variational Bayesian approximation for the inversion and uncertainty quantification of Darcy flows in heterogeneous porous media with random parameters

The inversion and uncertainty quantification of physical parameters are important for many science and engineering problems. For nonlinear parameter inversion problems of flows in hydrocarbon reservoirs, estimating the true posterior probability distribution function for the random parameter is difficult when the dimension of the parameter is relatively high. Inversion methods such as gradient-based or heuristic optimization methods searching for the maximum a posterior are generally point-estimate, while uncertainty quantification using MCMC is computationally expensive. In the current study, a new ensemble variational Bayesian approximation method is developed to estimate the posterior probability density using an ensemble of realizations regarded as a trial sample distribution. The objective is to optimize the trial sample distribution such that the evidence lower bound is maximized indicating that the distance between the trial and true posterior distribution is minimized. A subspace is built using principle component analysis based on finite samples to obtain a diagonal positive-definite covariance matrix, and ensemble-based data assimilation is used to optimize the trial distribution efficiently. Heterogeneous 2D and 3D test cases of transient single- and two-phase Darcy flows are presented for validation.

Azole resistance mechanisms and population structure of the human pathogen Aspergillus fumigatus on retail plant products.

Aspergillus fumigatus is a ubiquitous saprotroph and human-pathogenic fungus that is life-threatening to the immunocompromised. Triazole-resistant A. fumigatus was found in patients without prior treatment with azoles, leading researchers to conclude that resistance had developed in agricultural environments where azoles are used against plant pathogens. Previous studies have documented azole-resistant A. fumigatus across agricultural environments, but few have looked at retail plant products. Our objectives were to determine if azole-resistant A. fumigatus is prevalent in retail plant products produced in the United States (U.S.), as well as to identify the resistance mechanism(s) and population genetic structure of these isolates. Five hundred twenty-five isolates were collected from retail plant products and screened for azole resistance. Twenty-four isolates collected from compost, soil, flower bulbs, and raw peanuts were pan-azole resistant. These isolates had the TR34/L98H, TR46/Y121F/T289A, G448S, and H147Y cyp51A alleles, all known to underly pan-azole resistance, as well as WT alleles, suggesting that non-cyp51A mechanisms contribute to pan-azole resistance in these isolates. Minimum spanning networks showed two lineages containing isolates with TR alleles or the F46Y/M172V/E427K allele, and discriminant analysis of principle components identified three primary clusters. This is consistent with previous studies detecting three clades of A. fumigatus and identifying pan-azole-resistant isolates with TR alleles in a single clade. We found pan-azole resistance in U.S. retail plant products, particularly compost and flower bulbs, which indicates a risk of exposure to these products for susceptible populations and that highly resistant isolates are likely distributed worldwide on these products.IMPORTANCEAspergillus fumigatus has recently been designated as a critical fungal pathogen by the World Health Organization. It is most deadly to people with compromised immune systems, and with the emergence of antifungal resistance to multiple azole drugs, this disease carries a nearly 100% fatality rate without treatment or if isolates are resistant to the drugs used to treat the disease. It is important to determine the relatedness and origins of resistant A. fumigatus isolates in the environment, including plant-based retail products, so that factors promoting the development and propagation of resistant isolates can be identified.

Contribution of the TGFβ signaling pathway to growth of Pacific oyster (Crassostrea gigas)

Growth mediated by the transforming growth factor β (TGFβ) signaling pathway is a key trait in understanding environmental adaptability and species stability. The aim of the present study was to identify the TGFβ signaling pathway members and their expression patterns in different sized Pacific oysters (Crassostrea gigas). Nine oysters were selected for each of two groups: group L (shell height 77.57 ± 7.55 mm, shell length 49.96 ± 4.79 mm) and group B (shell height 43.58 ± 2.74 mm, shell length 33.25 ± 2.78 mm). Using a bioinformatics approach, 37 protein sequences of TGFβ signaling pathway members were classified in C. gigas, including 19 species for this species, nine ligands, five receptors, and five Smads. The mRNA expression levels of TGFβ pathway members in oysters of different sizes showed that both the TGFβ/activin/Nodal and BMP/GDF/MIS subfamilies are involved in the growth regulation of C. gigas. In the first subfamily, there was one possible signaling pathway: INHBA - TGFBR1/BMPR1B/ACVR1 - BMPR2 - Smad3/Smad1, 5, 8. In the second subfamily, there were three possible signaling pathways: BMP2 - BMPR1B - BMPR2 - Smad1, 5, 8; BMP4 - BMPR1B - BMPR2 - Smad1, 5, 8; and BMP7 - ACVR1/BMPR1B - BMPR2 - Smad1, 5, 8. Principle components analysis identified Bmp3, I-Smad, Bmp4, INHBA, and Smad3 as biomarkers of growth in the TGFβ signaling pathway of C. gigas, implicating the TGFβ pathway in the growth of C. gigas.

A new Bothriembryon from the Nullarbor Plain, Western Australia, with insights on the age of Bothriembryon praecursor from Kangaroo Well, Northern Territory

Fossil Bothriembryon specimens were recovered from the northwestern edges of the Nullarbor Plain, Western Australia, ∼300 km inland from the current southern coastline. This locality is significant because extant species of Bothriembryon from the Nullarbor are confined to within 50 km of the coastline. Principle component analysis (PCA) was used to compare the Nullarbor Bothriembryon species with a morphologically comparable extant species from the Eucla Basin, Bothriembryon barretti, and geographically closest fossil species: Bothriembryon kremnobates and Bothriembryon praecursor. Results showed strong support for a new fossil species herein described as Bothriembryon pilkiensis sp. nov. Specimens were found as internal moulds within pisolitic calcrete overlying the early Miocene Colville Sandstone. The chronology of the pisolitic calcrete is poorly studied; here we discuss the results of palynological and U–Th disequilibrium investigations which attempted to constrain the time of deposition and fossilization, with palynology results suggesting the late Pliocene. The type locality of B. praecursor, Kangaroo Well, Northern Territory, was also re-evaluated in age to the early Miocene, using biocorrelation of two balbarid species to taxa found within Riversleigh World Heritage Area assemblages. http://zoobank.org/urn:lsid:zoobank.org:pub:E33B1587-1023-419D-AFE8-11FD8C443A27 Helen E. Ryan [ helen.ryan@museum.wa.gov.au ], Collection and Research Centre, WA Museum, 49 Kew Street, Welshpool, WA, 6106; Kenny J. Travouillon [ Kenny.travouillon@museum.wa.gov.au ], Collection and Research Centre, WA Museum, 49 Kew Street, Welshpool, WA, 6106; Lynne A. Milne [ L.Milne@curtin.edu.au ], School of Earth and Planetary Sciences, Curtin University, Kent Street, Bentley, WA, 6102; Sarah K. Martin [ sarah.martin@dmirs.wa.gov.au ], Geological Survey of Western Australia, Department of Energy, Mines, Industry Regulation and Safety, 100 Plain Street, East Perth, WA, 6004; Kailah M. Thorn [ Kailah.thorn@museum.wa.gov.au ], Collection and Research Centre, WA Museum, 49 Kew Street, Welshpool, WA, 6106; Corey S. Whisson [ corey.whisson@museum.wa.gov.au ], Collection and Research Centre, WA Museum, 49 Kew Street, Welshpool, WA, 6106.

Assessment of Eco-Environmental Vulnerability Using Remote Sensing and GIS Tools in Maharashtra Region, India

Maharashtra region is prone to various disasters such as drought, floods, cyclones and earthquake and has been exposed to extreme weather events like dry spells. Communities within these dry lands are poor and face extreme conditions of water stress. This study has been carried out to analyze and quantify climatic and anthropogenic effect on eco-environmental vulnerability dynamic change. To achieve that a numerical model is set up, consisting of eight factors that are elevation, land use, drought, slope, NDVI, soil-type, soil erosion (water), and population density index &amp; has been evaluated using the method of spatial principle component analysis (SPCA) on Remote Sensing and GIS platform. The integrated eco-environmental vulnerability index (EVI) of study area is estimated to analyse spatial-temporal dynamic vulnerability changes in the 11 years gap from 2000 and 2011. The results show that the study area has become eco-environmental vulnerable slightly (about 80% of the region) with an increased eco-environmental vulnerability integrated index (EVSI) value by more than 50% (i.e., about 74%) and the driving force of dynamic change is mainly caused by socio-economic activities. In addition the estimation has been regionalized into thirty-four districts to serve as a base for decision-making for eco-environmental recovering and rebuilding. It is found that the most vulnerable district in 2011 is Ratnagiri and the least one is Sangli. There are nine districts which shows more than 100% increase in EVSI value, with the highest increase in Hingoli(100.65%), indicating that the districts have become most environmental vulnerable in the study-period. The research concludes that the method, supported by G.I.S using SPCA can’t only represent distinctly the input spatial distribution of plain-mountain-belt feature, but also respect the whole river-valley as a single unit.

Distance Analysis and Dimensionality Reduction using PCA on Brain Tumour MRI Scans

INTRODUCTION: Compression of MRI images while maintaining essential information, makes it easier to distinguish between different types of brain tumors. It also assesses the effect of PCA on picture representation modification and distance analysis between tumor classes.OBJECTIVES: The objective of this work is to enhance the interpretability and classification accuracy of highdimensional MRI scans of patients with brain tumors by utilising Principle Component Analysis (PCA) to reduce their complexity.METHODS:This study uses PCA to compress high-dimensional MRI scans of patients with brain tumors, focusing on improving classification using dimensionality reduction approaches and making the scans easier to understand.RESULTS: PCA efficiently reduced MRI data, enabling better discrimination between different types of brain tumors and significant changes in distance matrices, which emphasize structural changes in the data.CONCLUSION: PCA is crucial for improving the interpretability of MRI data.

Limitations of clustering with PCA and correlated noise

It is now common to have a modest to large number of features on individuals with complex diseases. Unsupervised analyses, such as clustering with and without preprocessing by Principle Component Analysis (PCA), is widely used in practice to uncover subgroups in a sample. However, in many modern studies features are often highly correlated and noisy (e.g. SNP's, -omics, quantitative imaging markers, and electronic health record data). The practical performance of clustering approaches in these settings remains unclear. Through extensive simulations and empirical examples applying Gaussian Mixture Models and related clustering methods, we show these approaches (including variants of kmeans, VarSelLCM, HDClassifier, and Fisher-EM) can have very poor performance in many settings. We also show the poor performance is often driven by either an explicit or implicit assumption by the clustering algorithm that high variance features are relevant while lower variance features are irrelevant, called the variance as relevance assumption. We develop practical pre-processing approaches that improve analysis performance in some cases. This work offers practical guidance on the strengths and limitations of unsupervised clustering approaches in modern data analysis applications.

Validation and Psychometric Properties of the German Version of the Adolescent Motor Competence Questionnaire

Introduction/Background: In 2016, the Adolescent Motor Competence Questionnaire (AMCQ) was developed in Australia. The AMCQ was developed to assess perceived motor competence in adolescents between the ages of 12 and 18 years. The 26 items represent four factors: participation in physical activity and sports, activities of daily living, public performance, and peer comparison. Aims/Objectives: Currently, no validated German-language self-report screening instrument exists for assessing perceived motor competence among adolescents. This study aimed to validate the German version of the AMCQ (AMCQ-GER). Methods/Approach: The German translation’s factor structure and psychometric properties were analyzed based on a community sample of 635 healthy children and adolescents between 10 and 18 years of age (12.9 ± 2.31 years, 321 boys and 312 girls) and 100 healthy young adults between 19 and 30 years of age (23.0 ± 2.92 years, 49 men and 51 women). The previously validated cutoff score of ≤83 out of 104 was used to group the sample into high and low motor competence. Results: The mean AMCQ-GER score was 84.9 (SD = 8.59), and 38.8% of the participants identified with low motor competence. The principle component analysis revealed clear evidence for a three-factorial structure comprising physical activity and sports, general clumsiness, and activities of daily living (with an explained variance of 29.2%). Reliability for all scales was excellent, with a McDonald’s ϖ of at least .70. The overall conclusion of the Rasch analysis supported the confirmatory factor analysis, although with an overall smaller number of items (18 instead of 26). The AMCQ-GER was positively correlated with age (.26**) and participation in organized club sports (.19**). It was negatively correlated with body mass index (−.23**) and school grade in physical activity (−.40**). Conclusions/Relevance: Our rigorous validation protocol has generated a remarkable reproduction of the AMCQ in German. These results suggest that the language and structure of the questionnaire is appropriate for German-speaking countries.

Multirate Mixture Probability Principal Component Analysis for Process Monitoring in Multimode Processes

In the multirate sampling processes, the process data are usually collected from various operating conditions and display multimodal characteristics. To monitor these multirate multimode processes, a multirate mixture probability principal component analysis model is proposed for process modeling and fault detection. In this model, the local multirate models are built first for each mode and all of them are subsequently fused with the mixture modeling approach. Such model is able to deal with multirate data with various amount of sampling rates, contributing to a remarkable fault detection and mode identification performance by utilizing all the available measurements even if some variables are unobserved. Then the expectation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$-$</tex-math> </inline-formula> maximum algorithm is utilized to estimate all the model parameters in the probabilistic framework and the corresponding monitoring method is also developed based on the constructed models. Finally, the effectiveness of the proposed method is demonstrated through a PRONTO benchmark and a real multimode ammonia synthesis process. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Motivated by the practical problem of ununiform sampling intervals in multimode processes, this paper proposes a novel multirate mixture probability principle component analysis model for processes modeling and monitoring. In this model, all the available observations with different sampling rates can be incorporated, which contributes greatly to capturing the multimodal characteristics within the industrial processes. Such ability is the key to realize multimode process monitoring, evaluation, fault diagnosis, and process optimization. In addition, although this paper only focuses on the continuous multirate data in industry, it is equally applicable to other forms of multirate data, such as images and videos.

Brain Tumor Detection and Classification Via Kernel Support Vector Machine Using MRI Images

Abstract: The MR images of the brain requires automatic and accurate classification for medical analysis and interpretation nowadays. Numerous methods have been declared already in the previous years. In this paper we have presented a method which classifies the brain image of MRI into normal and abnormal brain tumor images. This method uses wavelet transform that extract features from images. The next step involves principle component analysis (PCA) that reduces the feature dimensions. These reduced features are then employed to a kernel support vector machine (KSVM). 180 images of the brain were collected from the diseased which contained 130 abnormal brain and 50 normal brain images. Four kernels of different types were performed.

Socioeconomic inequalities in type 2 diabetes mellitus: a study based on a population-based survey in Iran

BackgroundType 2 diabetes mellitus (T2DM) is the most prevalent form of Diabetes Mellitus (DM), with social and economic determinants significantly influencing its prevalence. This study aimed to analyze the socioeconomic inequalities associated with T2DM in Iran.MethodsData from an observational survey in Iran, titled “Diabetes Care (DiaCare),” were utilized for this study. Socioeconomic inequalities were assessed through variables including Hemoglobin A1C (HbA1c), Fasting Blood Glucose (FBG), and Triple target (HbA1c, blood pressure, LDL-C), using concentration indices (CIs) and a multivariate logistic regression analysis. Individual socioeconomic status (SES) was determined by calculating an asset index using principle component analysis (PCA) based on their properties. Data analysis was conducted using STATA software version 14.ResultsA total of 13,321 participants were included in the study. The CIs were significantly positive for controlled HbA1c (0.0324) and triple target (0.1067), while for controlled FBG, it was 0.0125, although not significant. Among females, the CIs were significantly positive for controlled HbA1c (0.0745), FBG (0.0367), and triple target (0.209). Additionally, in the 45–55 and 65–75 age groups, the CIs were significantly positive for controlled HbA1c (0.0607) and FBG (0.0708), respectively. This index was significant for controlled Triple target in the 35–45 (0.376) and 65–75 (0.124) age groups. The CI for controlled FBG was significant in rural dwellers (-0.044) while the concentration of controlled triple target was significant in urban dwellers (0.0967). Controlled HbA1c showed significant concentration in both urban (0.0306) and rural (-0.0576) dwellers. Furthermore, the CIs were significant for controlled HbA1c in regions with medium prevalence (0.0534) and FBG in regions with low prevalence (-0.0277). This index was significantly positive for controlled triple target in regions with high prevalence (0.124).ConclusionsDiabetes care is more concentrated among individuals with higher SES. Policymakers should consider this to mitigate the inequality and alleviate the burden of T2DM.