Independent Principal Components Research Articles

A Model for Selecting Kiwifruit (Actinidia eriantha) Germplasm Resources with Excellent Fruit Quality.

The evaluation of quality traits is an important procedure for kiwifruit breeding and comprehensive utilization. The present study aimed to establish a comprehensive system to assess Actinidia eriantha germplasms by analyzing 22 quality traits on kiwifruit samples collected from a wild population of 236 plants grown in the Jiangxi Province, China. Variability, correlation, principal components, and cluster analyses were carried out using the data collected from fruit quality evaluations. The coefficients of variation (CV) of fruit quality traits ranged from 11.66 to 66.16% (average coefficient = 35.09%), indicating a high level of variation among the 236 plants. In addition, different degrees of correlations were found between the traits, with similar traits demonstrating strong correlations. Principal component analysis (PCA) generated eight comprehensive and independent principal components, accounting for 77.93% of the original fruit quality information. Furthermore, an extensive evaluation from PCA ranked the plants based on cluster analysis and grouped them into seven categories. A stepwise regression analysis generated a prediction model, demonstrating a good fit (0.945) with the principal components of the comprehensive evaluation score. Overall, this study identifies nine quality traits, representing fruit appearance, sweetness, acidity, flavor, and nutritional attributes, as important traits for a comprehensive evaluation of A. eriantha fruits.

Non-probabilistic system reliability analysis of structures with interval uncertainty considering correlated failure modes

Large and complex engineering structures consist of multiple components with various failure paths. Thus, it is more reasonable to assess reliability by treating the structure as a structural system with multiple components rather than as a whole. Aiming at the problem that traditional methods are difficult to assess reliability for structural systems based on the correlation and logical relationship between failure mode with limited samples, this paper proposes a novel method based on the non-probabilistic interval model of principal component. This method utilizes the surrogate model and principal component analysis to obtain independent principal components, followed by optimization to construct the non-probabilistic interval model of principal components. The reliability of the structural system can then be assessed using the non-probabilistic volumetric method. Two numerical examples show that results obtained using the proposed method at 95 % and 98 % confidence levels have errors of 1.4286 %, 1.1097 %, and 1.4213 %, 0.8772 %, respectively, compared to those from Monte Carlo Simulation. This demonstrates that the proposed method efficiently addresses the challenge of reliability assessment for structural systems with limited samples, providing more realistic and accurate assessment for large and complex structures.

Introducing the MESMER-M-TPv0.1.0 module: spatially explicit Earth system model emulation for monthly precipitation and temperature

Abstract. Emulators of Earth system models (ESMs) are statistical models that approximate selected outputs of ESMs. Owing to their runtime efficiency, emulators are especially useful when large amounts of data are required, for example, for in-depth exploration of the emission space, for investigating high-impact low-probability events, or for estimating uncertainties and variability. This paper introduces an emulation framework that allows us to emulate gridded monthly mean precipitation fields using gridded monthly mean temperature fields as forcing. The emulator is designed as an extension of the Modular Earth System Model Emulator (MESMER) framework, and its core relies on the concepts of generalised linear models (GLMs). Precipitation at each (land) grid point and for each month is approximated as a multiplicative model with two factors. The first factor entails the temperature-driven precipitation response and is assumed to follow a gamma distribution with a logarithmic link function. The second factor is the residual variability in the precipitation field, which is assumed to be independent of temperature but may still possess spatial precipitation correlations. Therefore, the monthly residual field is decomposed into independent principal components and subsequently approximated and sampled using a kernel density estimation with a Gaussian kernel. The emulation framework is tested and validated using 24 ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For each ESM, we train on a single-ensemble member across scenarios and evaluate the emulator performance using simulations with historical and Shared Socioeconomic Pathways (SSP5-8.5) forcing. We show that the framework captures grid-point-specific precipitation characteristics, such as variability, trend, and temporal auto-correlations. In addition, we find that emulated spatial (cross-variable) characteristics are consistent with those of ESMs. The framework is also able to capture compound hot–dry and cold–wet extremes, although it systematically underestimates their occurrence probabilities. The emulation of spatially explicit coherent monthly temperature and precipitation time series is a major step towards a computationally efficient representation of impact-relevant variables of the climate system.

Applicability of electromyography (EMG) as a prospective technique for textural evaluation of different types of biscuits

Bakery industry is an important segment of the food industry, worldwide, and texture is an important determinant in the consumer acceptability of baked products especially biscuits. In this study, six different textured biscuits namely, whole wheat flour biscuit, chocolate biscuit, cumin biscuit, cardamom biscuit, pistachio biscuit and cashew biscuit, procured from local bakery, were analyzed for their proximate composition and color characteristics. The biscuits were having significantly (p ≤ 0.05) different values for proximate composition and color parameters. This study primarily aimed at investigating the application of electromyography (EMG) as a prospective technique for texture evaluation of these different types of biscuits. EMG is an upcoming technique for in vivo food texture evaluation which gives real-time information about the dynamic textural changes occurring during mastication of a food product. The EMG study was conducted on ten human subjects who were served six samples of biscuits in duplicate. To eliminate subject factor variance, two-way ANOVA was applied on the relative values of EMG variables. Two recordings of same subject in an EMG session for a biscuit sample showed significant (p ≤ 0.05) degree of similarity indicating that human mastication behavior is reproducible. Initially, twenty-eight EMG parameters were obtained which were reduced to five representative parameters using cluster analysis. Principal component analysis further classified these five masticatory parameters into two independent principal components (PC1 and PC2) for explaining the data variance. Cumulative proportions for PC1 and PC2 were 37.6% and 70.8%, respectively. The results showed that mastication time obtained from electromyographic analysis can evaluate sensory fracturability, hardness and chewiness. While middle interburst duration can effectively evaluate sensory cohesiveness and instrumental flexural strength. The study concluded that EMG can serve as a comprehensive texture evaluation tool in the bakery industry.

Research on dependent evidence combination based on principal component analysis.

Dempster-Shafer evidence theory, as a generalization of probability theory, is a powerful tool for dealing with a variety of uncertainties, such as incompleteness, ambiguity, and conflict. Because of its advantages in information fusion compared with traditional probability theory, it is widely used in various fields. However, the classic Dempster's combination rule assumes that evidences are independent of each other, which is difficult to satisfy in real life. Ignoring the dependence among the evidences will lead to unreasonable fusion results, and even wrong conclusions. Considering the limitations of D-S evidence theory, this paper proposed a new evidence fusion model based on principal component analysis (PCA) to deal with the dependence among evidences. First, the approximate independent principal components of each information source were obtained based on principal component analysis. Second, the principal component data set was used as a new information source for evidence theory. Third, the basic belief assignments (BBAs) were constructed. As the fundamental construct of evidence theory, a BBA is a probabilistic function corresponding to each hypothesis, quantifying the belief assigned based on the evidence at hand. This function facilitates the synthesis of disparate evidence sources into a mathematically coherent and unified belief structure. After constructing the BBAs, the BBAs were fused and a conclusion was drawn. The case study verified that the proposed method is more robust than several traditional methods and can deal with redundant information effectively to obtain more stable results.

Associations of maternal stress, gene expression, and newborn birthweight in the Democratic Republic of Congo.

Maternal stress has long been associated with lower birthweight, which is associated with adverse health outcomes including many adult diseases. The underlying mechanisms remain elusive although changes in gene expression may play a role. Studies are only beginning to test how maternal stress impacts gene expression as reflected in the transcriptome. In a cohort of mothers and newborns in the eastern Democratic Republic of Congo (n=93), we studied the effects of four maternal stress measures (chronic stress, war trauma, sexual trauma, and general trauma) on the transcriptomes of maternal venous blood, newborn venous blood, and placental tissues, and on newborn birthweight. Maternal stress was investigated as independent measures, principal components, and clusters identified through machine learning. The transcriptome was assayed using the ClariomD chip. Multiple regression models were used to test for associations between maternal stress measures, the transcriptome, and newborn birthweight. None of the maternal stress measures showed an association with expression of individual genes. In contrast, when testing global gene expression, war trauma was significantly associated with the placental transcriptome. War trauma was also significantly associated with birthweight in multiple models. Mediation analysis indicated that ~14% of the effect of war trauma on birthweight was mediated by a placental gene expression component. Our results suggest that gene expression in the placenta, which represents the interface between mother and developing fetus, may partially mediate the negative impact of maternal stress on newborn birthweight.

Acoustic signal-based indigenous real-time rainfall monitoring system for sustainable environment

The rainfall weather station employs a tipping bucket rain gauge, which serves as a specialized instrument for the meticulous assessment and documentation of various rainwater parameters. The implementation of a tipping bucket rain gauge for rainfall monitoring bears significant implications for both societal productivity as well as improvement of human life. A noteworthy example can be the constructive influence of rainwater over the sustainable agricultural irrigation practices, wherein the precise monitoring of rainfall through a tipping bucket rain gauge enables the formulation of tedious irrigation strategies. The rainfall monitoring if often handle using rain gauge which majorly faces two challenges named as mechanical devices failure and high installation and maintenance cost. Considering the challenges, we propose the fully automated rain gauge (RG) based on the principle of sound and its properties for rainfall monitoring. The working prototype is part of our work whose primary task is to collect the rainfall acoustic value and store it in the cloud. Our mechanism is to use the acoustic property of rain data to categorize rainfall intensity. We perform blind signal separation on the received signal (acoustic signal recorded with the help of microphone sensor) and feed the separated signal to a recurrent convolution neural network (RCNN). The source separation of the collected acoustic signals is primarily being done using independent component analysis and principal components analysis. The proposed solution can be able to make the classification of rain intensity with more than 80% accuracy. In addition to this, the developed method provides the sustainable solution to the challenges with the low-cost and application-specific acceptable threshold criteria and supplement rain measurement techniques.

Characteristic time scale as optimal input in Machine Learning algorithms: Homogeneous autoignition

Considering temporally evolving processes, the search for optimal input selection in Machine Learning (ML) algorithms is extended here beyond (i) the readily available independent variables defining the process and (ii) the dependent variables suggested by feature extraction methods, by considering the time scale that characterizes the process. The analysis is based on the process of homogeneous autoignition, which is fully determined by the initial temperature T(0) and pressure p(0) of the mixture and the equivalence ratio ϕ that specifies the initial mixture composition. The aim is to seek the optimal input for the prediction of the time at which the mixture ignites. The Multilayer Perceptron (MLP) and Principal Component Analysis (PCA) algorithms are employed for prediction and feature extraction, respectively. It is demonstrated that the time scale that characterizes the initiation of the process τe(0), provides much better accuracy as input to MLP than any pair of the three independent parameters T(0), p(0) and ϕ or their two principal components. Indicatively, it is shown that using τe(0) as input results in a coefficient of determination R2 in the range of 0.953 to 0.982, while the maximum value of R2 when using the independent parameters or principal components is 0.660. The physical grounds, on which the success of τe(0) is based, are discussed. The results suggest the need for further research in order to develop selection methodologies of optimal inputs among those that characterize the process.

Combination of ultrafast dynamic contrast-enhanced MRI-based radiomics and artificial neural network in assessing BI-RADS 4 breast lesions: Potential to avoid unnecessary biopsies.

To investigate whether combining radiomics extracted from ultrafast dynamic contrast-enhanced MRI (DCE-MRI) with an artificial neural network enables differentiation of MR BI-RADS 4 breast lesions and thereby avoids false-positive biopsies. This retrospective study consecutively included patients with MR BI-RADS 4 lesions. The ultrafast imaging was performed using Differential sub-sampling with cartesian ordering (DISCO) technique and the tenth and fifteenth postcontrast DISCO images (DISCO-10 and DISCO-15) were selected for further analysis. An experienced radiologist used freely available software (FAE) to perform radiomics extraction. After principal component analysis (PCA), a multilayer perceptron artificial neural network (ANN) to distinguish between malignant and benign lesions was developed and tested using a random allocation approach. ROC analysis was performed to evaluate the diagnostic performance. 173 patients (mean age 43.1 years, range 18-69 years) with 182 lesions (95 benign, 87 malignant) were included. Three types of independent principal components were obtained from the radiomics based on DISCO-10, DISCO-15, and their combination, respectively. In the testing dataset, ANN models showed excellent diagnostic performance with AUC values of 0.915-0.956. Applying the high-sensitivity cutoffs identified in the training dataset demonstrated the potential to reduce the number of unnecessary biopsies by 63.33%-83.33% at the price of one false-negative diagnosis within the testing dataset. The ultrafast DCE-MRI radiomics-based machine learning model could classify MR BI-RADS category 4 lesions into benign or malignant, highlighting its potential for future application as a new tool for clinical diagnosis.

Estimating yield in commercial wheat cultivars using the best predictors of powdery mildew and rust diseases.

This four-year research determined the best predictors of black, brown and yellow rusts and powdery mildew development in different wheat cultivars and planting dates across 282 experimental field plots. Parameters estimated by exponential (for black rust and powdery mildew) and Gaussian (for brown and yellow rusts) models, area under disease progress curve (AUDPC), and maximum disease severity were considered as disease progress curve elements. Factor analysis determined the most predictive variables among 19 indicators in order to describe wheat yield. According to principal component analysis (PCA), 11 selected wheat diseases and yield predicators accounted for 60% of total variance in datasets. This PCA test described four principal components involving these selected predictors. Next, multivariate regression model, which developed according to four independent principal components, justified a noticeable part of yield variability over and within growing seasons. Present findings may improve accuracy of future studies to examine seasonal patterns of powdery mildew and rusts, predict wheat yield and develop integrative disease management programs.

Assessing executive functions in post-stroke aphasia-utility of verbally based tests.

It is increasingly acknowledged that, often, patients with post-stroke aphasia not only have language impairments but also deficits in other cognitive domains (e.g. executive functions) that influence recovery and response to therapy. Many assessments of executive functions are verbally based and therefore usually not administered in this patient group. However, the performance of patients with aphasia in such tests might provide valuable insights both from a theoretical and clinical perspective. We aimed to elucidate (i) if verbal executive tests measure anything beyond the language impairment in patients with chronic post-stroke aphasia, (ii) how performance in such tests relates to performance in language tests and nonverbal cognitive functions, and (iii) the neural correlates associated with performance in verbal executive tests. In this observational study, three commonly used verbal executive tests were administered to a sample of patients with varying aphasia severity. Their performance in these tests was explored by means of principal component analyses, and the relationships with a broad range of background tests regarding their language and nonverbal cognitive functions were elucidated with correlation analyses. Furthermore, lesion analyses were performed to explore brain–behaviour relationships. In a sample of 32 participants, we found that: (i) a substantial number of patients with aphasia were able to perform the verbal executive tests; (ii) variance in performance was not explained by the severity of an individual’s overall language impairment alone but was related to two independent behavioural principal components per test; (iii) not all aspects of performance were related to the patient’s language abilities; and (iv) all components were associated with separate neural correlates, some overlapping partly in frontal and parietal regions. Our findings extend our clinical and theoretical understanding of dysfunctions beyond language in patients with aphasia.

Mapping combined with principal component analysis identifies excellent lines with increased rice quality

Quality-related traits are some of the most important traits in rice, and screening and breeding rice lines with excellent quality are common ways for breeders to improve the quality of rice. In this study, we used 151 recombinant inbred lines (RILs) obtained by crossing the northern cultivated japonica rice variety ShenNong265 (SN265) with the southern indica rice variety LuHui99 (LH99) and simplified 18 common rice quality-related traits into 8 independent principal components (PCs) by principal component analysis (PCA). These PCs included peak and hot paste viscosity, chalky grain percentage and chalkiness degree, brown and milled rice recovery, width length rate, cooked taste score, head rice recovery, milled rice width, and cooked comprehensive score factors. Based on the weight ratio of each PC score, the RILs were classified into five types from excellent to poor, and five excellent lines were identified. Compared with SN265, these 5 lines showed better performance regarding the chalky grain percentage and chalkiness degree factor. Moreover, we performed QTL localization on the RIL population and identified 94 QTLs for quality-related traits that formed 6 QTL clusters. In future research, by combining these QTL mapping results, we will be using backcrossing to aggregate excellent traits and achieve quality improvement of SN265.

Raman and near Infrared Spectroscopy for Quantification of Fatty Acids in Muscle Tissue-A Salmon Case Study.

The aim of the present study was to critically evaluate the potential of using NIR and Raman spectroscopy for prediction of fatty acid features and single fatty acids in salmon muscle. The study was based on 618 homogenized salmon muscle samples acquired from Atlantic salmon representing a one year-class nucleus, fed the same high fish oil feed. NIR and Raman spectra were used to make regression models for fatty acid features and single fatty acids measured by gas chromatography. The predictive performance of both NIR and Raman was good for most fatty acids, with R2 above 0.6. Overall, Raman performed marginally better than NIR, and since the Raman models generally required fewer components than respective NIR models to reach high and optimal performance, Raman is likely more robust for measuring fatty acids compared to NIR. The fatty acids of the salmon samples co-varied to a large extent, a feature that was exacerbated by the overlapping peaks in NIR and Raman spectra. Thus, the fatty acid related variation of the spectroscopic data of the present study can be explained by only a few independent principal components. For the Raman spectra, this variation was dominated by functional groups originating from long-chain polyunsaturated FAs like EPA and DHA. By exploring the independent EPA and DHA Raman models, spectral signatures similar to the respective pure fatty acids could be seen. This proves the potential of Raman spectroscopy for single fatty acid prediction in muscle tissue.

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction.

This work proposes a new computational framework for learning a structured generative model for real-world datasets. In particular, we propose to learn a Closed-loop Transcriptionbetween a multi-class, multi-dimensional data distribution and a Linear discriminative representation (CTRL) in the feature space that consists of multiple independent multi-dimensional linear subspaces. In particular, we argue that the optimal encoding and decoding mappings sought can be formulated as a two-player minimax game between the encoder and decoderfor the learned representation. A natural utility function for this game is the so-called rate reduction, a simple information-theoretic measure for distances between mixtures of subspace-like Gaussians in the feature space. Our formulation draws inspiration from closed-loop error feedback from control systems and avoids expensive evaluating and minimizing of approximated distances between arbitrary distributions in either the data space or the feature space. To a large extent, this new formulation unifies the concepts and benefits of Auto-Encoding and GAN and naturally extends them to the settings of learning a both discriminative and generative representation for multi-class and multi-dimensional real-world data. Our extensive experiments on many benchmark imagery datasets demonstrate tremendous potential of this new closed-loop formulation: under fair comparison, visual quality of the learned decoder and classification performance of the encoder is competitive and arguably better than existing methods based on GAN, VAE, or a combination of both. Unlike existing generative models, the so-learned features of the multiple classes are structured instead of hidden: different classes are explicitly mapped onto corresponding independent principal subspaces in the feature space, and diverse visual attributes within each class are modeled by the independent principal components within each subspace.

Identification of 4 subgroups in juvenile dermatomyositis by principal component analysis-based cluster analysis.

Juvenile dermatomyositis (JDM) is an autoimmune disease characterised by a great heterogeneity in its clinical manifestations. In this study, we aimed to investigate the association between different clinical subtypes, laboratory data, and myositis antibodies of JDM. A total of 132 JDM patients were enrolled and their medical records were retrospectively reviewed and autoantibodies tested. Twenty-one variables, including clinical manifestations and laboratory findings, were selected for analysis. We selected principal component analysis (PCA) as a pre-processing method for cluster analysis to convert the 21 original variables into independent principal components. We then conducted a PCA-based cluster analysis in order to analyse the association between patient clusters and the clinical data, laboratory data, and myositis autoantibodies. We identified 4 distinct JDM subgroups by PCA-based cluster analysis, namely: cluster A, JDM patients with arthralgia and intense inflammation; cluster B, JDM patients with clinical manifestations of vasculitis; cluster C, hypermyopathic JDM patients; and cluster D, JDM patients with skin involvement. There were significant differences between the 4 groups in serum alkaline phosphatase levels, usage of aggressive immunosuppressive therapy, and autoantibody expression of anti-mi2, anti-MDA5, anti-Jo1, and anti-PM-Scl100. We conducted cluster analysis of a cohort of JDM patients and identified 4 subgroups that represented diverse characteristics in the distribution of laboratory data and myositis autoantibodies, indicating that multidimensional assessment of clinical manifestations is highly valuable and urgently needed in JDM patients. These subgroups may contribute to individualised treatments and improved JDM patient prognosis.

3-D Textural Analysis of 2-[¹⁸F]FDG PET and Ki67 Expression in Nonsmall Cell Lung Cancer

The immunohistochemical determination of the Ki67 expression is regarded as a reliable method to monitor non small cell lung cancer (NSCLC) proliferation. However, cancer tissues are not uniform and the use of a small tumor sample in biopsy may not be accurate. Although positron emission tomography (PET) represents an alternative to biopsy, by providing the 3D functional and morphological distribution of cancer cells across the entire tumor volume, standardized uptake values (SUVs) exhibit poor accuracy. The purpose of this study is to identify quantitative features in 2-18FFDG PET images uniquely correlated with proliferating lung cancer cells that may perform better than SUVs in the evaluation of lung cancer proliferation. By using a xenograft NSCLC model implanted in 19 mice, we found that 89 textural parameters extracted from 3D 2-18FFDG uptake can be described by 6 independent principal components accounting for 95% of the total variance. One of these components features a significant and univocal correlation with 3D-stacked Ki67 staining immunohistochemical images (|r|>0.6;P<0.05/6, including compensation for multiple comparisons). Furthermore, the values of these components extracted from the PET images of in vivo and ex vivo explanted NSCLC cancers exhibit a strong correlation, verifying the stability of the segmentation method.

Computational analysis of 10,860 phenotypic annotations in individuals with SCN2A-related disorders

PurposePathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype–phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed. MethodsWe extracted phenotypic information from primary descriptions of SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms. With higher-level phenotype terms inferred by the HPO structure, we assessed the frequencies of clinical features and investigated the association of these features with variant classes and locations within the NaV1.2 protein. ResultsWe identified 413 unrelated individuals and derived a total of 10,860 HPO terms with 562 unique terms. Protein-truncating variants were associated with autism and behavioral abnormalities. Missense variants were associated with neonatal onset, epileptic spasms, and seizures, regardless of type. Phenotypic similarity was identified in 8/62 recurrent SCN2A variants. Three independent principal components accounted for 33% of the phenotypic variance, allowing for separation of gain-of-function versus loss-of-function variants with good performance. ConclusionOur work shows that translating clinical features into a computable format using a standardized language allows for quantitative phenotype analysis, mapping the phenotypic landscape of SCN2A-related disorders in unprecedented detail and revealing genotype–phenotype correlations along a multidimensional spectrum.

Risk Assessment of Debris Flow Disaster Based on Principal Component Analysis

Debris flow disasters have the characteristics of large energy, strong explosiveness, and great harm. They cause irreversible damage to the environment and at the same time, seriously threaten the safety of human life and property. Therefore, it is essential to evaluate the risk of debris flow. In this paper, nine factors such as the basin area, the length of the main ditch, and the cutting density of the basin along the Jinsha River are used to calculate the cumulative contribution rate to screen the principal components using the principal component analysis method, and then comprehensively score the linear combination of independent principal components. Therefore, the risk assessment of each debris flow channel is carried out, and the results obtained are compared with the methods in different documents, which shows that this method has better applicability and feasibility. This method of determining weights overcomes the shortcomings of the subjective method of determining weights and is more reasonable and objective. The research results can provide decisionmaking references for relevant departments and industries.

Correlation of [18F]florbetaben textural features and age of onset of Alzheimer\u2019s disease: a principal components analysis approach

BackgroundWhen Alzheimer’s disease (AD) is occurring at an early onset before 65 years old, its clinical course is generally more aggressive than in the case of a late onset. We aim at identifying [^{18}F]florbetaben PET biomarkers sensitive to differences between early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD). We conducted [^{18}F]florbetaben PET/CT scans of 43 newly diagnosed AD subjects. We calculated 93 textural parameters for each of the 83 Hammers areas. We identified 41 independent principal components for each brain region, and we studied their Spearman correlation with the age of AD onset, by taking into account multiple comparison corrections. Finally, we calculated the probability that EOAD and LOAD patients have different amyloid-beta (Abeta) deposition by comparing the mean and the variance of the significant principal components obtained in the two groups with a 2-tailed Student’s t-test.ResultsWe found that four principal components exhibit a significant correlation at a 95% confidence level with the age of onset in the left lateral part of the anterior temporal lobe, the right anterior orbital gyrus of the frontal lobe, the right lateral orbital gyrus of the frontal lobe and the left anterior part of the superior temporal gyrus. The data are consistent with the hypothesis that EOAD patients have a significantly different [^{18}F]florbetaben uptake than LOAD patients in those four brain regions.ConclusionsEarly-onset AD implies a very irregular pattern of Abeta deposition. The authors suggest that the identified textural features can be used as quantitative biomarkers for the diagnosis and characterization of EOAD patients.

A Principal Component of Quality of Life Measures Is Associated with Survival for Head and Neck Cancer Patients Treated with Radiation Therapy.

Simple SummaryHealth-related quality of life (HRQOL) surveys describe the patient experience of disease and treatment. The relationship between post-treatment recovery of HRQOL and outcome in head and neck cancer is not well characterized. Impaired recovery of numerous individual components of HRQOL were associated with increased mortality. To obtain a better understanding how HRQOL (as a whole) impacts survival, we utilized a statistical technique called principal component analysis (PCA). PCA generated a total score of several HRQOL domains, named principal component 1 (PC1), to more accurately describe the cumulative impact of poor HRQOL recovery on outcome. PC1 was associated with survival and may be a useful tool in future studies to identify at-risk patients.Background: Health-related quality of life (HRQOL) metrics can be associated with survival in head and neck cancer (HNC); however, the impact of HRQOL recovery and the relevant HRQOL domains regarding outcome are unclear. Methods: Using a single-institution database, we retrospectively reviewed HNC patients treated with definitive or postoperative radiation therapy between 2013 and 2018. The recovery of individual HRQOL domains were determined by the ratio of the post-treatment to baseline scores. Univariate and Multivariate Cox regression were used to analyze survival outcomes. Principal component analysis was used to adjust for multicollinearity of HRQOL domains. Results: In 218 HNC patients who received radiation therapy, median follow-up was 24.8 months (interquartile range (IQR) 14.5–32.0). Principal component analysis evaluating the recovery of HRQOL domains revealed two independent principal components (PC), PC1 and PC2. PC1, which received contributions from the functional domains; physical (PF), role (RF), emotional (EF), cognitive (CF), and global health status (GQOL) was significantly associated with disease-free (HR = 0.77, 95% CI 0.61–0.98, p = 0.034) and overall survival (HR = 0.76, 95% CI 0.65–0.91, p = 0.004) on multivariate analysis and PC2, had no correlation with outcome and was mainly represented by social functioning. Unplanned hospitalization was significantly associated with lower PC1 scores (β = −0.997, Std. Error = 0.244, p < 0.001). Conclusion: Our study provides evidence that post-treatment recovery of HRQOL domains were associated with overall survival (OS) in HNC. PC1 is an attractive clinical tool to assess the recovery across multiple different HRQOL and the relationship with survival. Future prospective studies may identify patients who could benefit from additional rehabilitation based on PC1 score.