Significant Principal Components Research Articles

Metabolite signatures associated with microRNA miR-143-3p serve as drivers of poor lung function trajectories in childhood asthma

Lung function trajectories (LFTs) have been shown to be an important measure of long-term health in asthma. While there is a growing body of metabolomic studies on asthma status and other phenotypes, there are no prospective studies of the relationship between metabolomics and LFTs or their genomic determinants. We utilized ordinal logistic regression to identify plasma metabolite principal components associated with four previously-published LFTs in children from the Childhood Asthma Management Program (CAMP) (n=660). The top significant metabolite principal component (PCLF) was evaluated in an independent cross-sectional child cohort, the Genetic Epidemiology of Asthma in Costa Rica Study (GACRS) (n=1151) and evaluated for association with spirometric measures. Using meta-analysis of CAMP and GACRS, we identified associations between PCLF and microRNA, and SNPs in their target genes. Statistical significance was determined using an false discovery rate-adjusted Q-value. The top metabolite principal component, PCLF, was significantly associated with better LFTs after multiple-testing correction (Q-value=0.03). PCLF is composed of the urea cycle, caffeine, corticosteroid, carnitine, and potential microbial (secondary bile acid, tryptophan, linoleate, histidine metabolism) metabolites. Higher levels of PCLF were also associated with increases in lung function measures and decreased circulating neutrophil percentage in both CAMP and GACRS. PCLF was also significantly associated with microRNA miR-143-3p, and SNPs in three miR-143-3p target genes; CCZ1 (P-value=2.6×10-5), SLC8A1 (P-value=3.9×10-5); and TENM4 (P-value=4.9×10-5). This study reveals associations between metabolites, miR-143-3p and LFTs in children with asthma, offering insights into asthma physiology and possible interventions to enhance lung function and long-term health. Molecular data for CAMP and GACRS via the Trans-Omics in Precision Medicine (TOPMed) program was supported by the National Heart, Lung, and Blood Institute (NHLBI).

Evaluation of Spatiotemporal Patterns and Water Quality Conditions Using Multivariate Statistical Analysis in the Yangtze River, China

As a crucial surface water resource, the Yangtze River has raised concerns about its water quality due to its importance in economic and social development, environmental conservation, and agricultural development. The principal component analysis (PCA), hierarchical clustering analysis (HCA), and the water quality index (WQI) were utilized to assess the overall condition and detect spatiotemporal patterns and the key parameters of water quality in the Yangtze River. All usage data were determined monthly from samples taken in 2021 at the 33 Yangtze River water quality monitoring stations. The results demonstrated that 85% of the monitoring stations in the whole Yangtze River were maintained at a “good” condition, with average WQI values ranging from 71.16 to 81.25. The water quality was slightly poorer in the summer, with 56.6% of monitoring stations being in “medium” condition. Spatially, there was a downward trend in the water quality from upstream to downstream. Two significant principal component scores (PCs) were produced as a result of PCA and HCA, explaining 60.3% of the total variance in the upstream, 67.4% in the transition zone, and 50.4% in the downstream, respectively. In addition, the middle–upper reaches of water quality were found to correlated with CODMn, whereas the water quality in the downstream were mainly influenced by TUR, TP, T, and DO. The results primarily motivated our understanding of the Yangtze River’s water quality status and suggested the main targets for water quality improvement in different monitoring areas.

Spatial varying and co-occurring future climate changes over a heterogeneous river basin: a multivariate approach

Abstract This study explored co-occurring climate-scale changes across the physioclimatically heterogeneous Tapi River basin (TRB) for baseline (1991–2020) and future periods (2021–2100). We used a novel multivariate framework comprising multi-model ensembles of bias-corrected rainfall and temperature from five global climate models (CMIP-5), 12 climate indices (six for each variable), and principal component analysis (PCA). The univariate assessment showed statistically significant warming of 1.1–1.8 °C (1.5–4.0 °C) under RCP-4.5 (RCP-8.5) scenarios. The Middle Tapi basin showed a substantial shift towards a wetter climate regime in the future. The multivariate assessment of spatially varying climate indices resulted in four significant principal components (PCs). The relative evaluation of these PCs showed that nearly 41.6% (47.0%) of the TRB is vulnerable to the transition of the current climatic patterns to the dry–warm (wet–warm) regime under RCP-8.5 (RCP-4.5) in the near (distant) future. On the optimistic side, under RCP-4.5 and RCP-8.5, 53.0% and 69.8% of the TRB displayed signs of uniform temporal distribution with wet rainfall regimes and profound warming towards the end of the 21st century, respectively. The study outcomes would help to devise policies for regional sustainability and adopt mitigation measures to enhance resiliency in a changing climate.

Structural Condition Classification of Railway Bridge KW51 Before, During, and After Retrofitting

AbstractA linear classification algorithm for various structural states (before, during, and after retrofitting) of railway steel arch bridge KW51 based on Linear Discriminant Analysis (LDA) of principal components is proposed. The technique is typically employed in other fields, such as genetics, but its use in civil engineering is still limited. The method is applied in two steps, first, the original vibration measurements are projected to a lower dimensional subspace using Principal Component Analysis (PCA). The significant principal components are provided as inputs to LDA to constitute the newly transformed subspace. Joining PCA and LDA improves the performance aptitude of LDA when only first major principal components hold the main signature of the features. The acceleration datasets that represent bridge conditions under train passage on the steel arch railway bridge in Belgium, mentioned as bridge KW51 have been utilized for validation of the algorithm. The outcome of the visualization process is further analyzed using three clustering methods (kmeans, Fuzzy C means, and Gaussian Mixture Modelling). The confusion matrix is used to examine the performance of the clustering methods. The proposed algorithm provides promising results that outperformed other classification methods.

Genetic Variability, Correlation and Principal Component Analysis for Yield Related Traits in Pigeonpea [Cajanus cajan (L.) Millsp.

Background: Pigeon pea is an economical source of protein cultivated in arid and semi arid tropical areas of the world. Genetic improvement mainly depends upon the amount of genetic variability available in the population. It is necessary for a breeder to understand the extent of genotypic and phenotypic variation among the germplasm to design an effective breeding programme. Methods: Eighty pigeon pea genotypes were evaluated for their variability with regard to yield and yield contributing traits. The experiment was carried out at Regional Research Station, Paiyur during 2021-2022 following randomized block design with two replications. Result: The traits viz., plant height, number of primary branches per plant, number of pods per plant and single plant yield can be improved following simple selection method as they exhibited high variability in combination with high heritability and genetic advance. Principal Component Analysis (PCA) revealed three significant principal components (PCs), which accounted for 81.24% of phenotypic variation among the studied genotypes. The traits days to 50% flowering, days to maturity, number of primary branches per plant, number of pods per plant and hundred seed weight had significantly positive association and positive direct effect with yield. Hence, these traits can be used as good selection criteria for yield improvement.

Predicting Churn in Online Games by Quantifying Diversity of Engagement.

Understanding engagement patterns of users in online platforms, may it be games, online social networks, or academic websites, is a widely studied topic with many real-world applications and economic consequences. A holy grail in this area of research is to develop an automatic prediction algorithm for when a user is going to leave the platform and devise proper intervention. In this work, we study online recreational games and propose to model the engagement patterns of players through an unsupervised learning framework. We think of engagement as a continuous temporal process, measured along specific axes derived from gaming users' data using principal component analysis. We track the overall trend of the projection of the data along the significant principal components. We find that the geometric variability of the trajectory is a good predictor of the users' engagement level. Users characterized by a time series with large variability are users with higher engagement; namely, they will continue playing the game for prolonged periods of time. We evaluated our methodology on two data sets of very different game types and compared the performance of our method with state-of-the-art black-box machine learning algorithms. Our results were fairly competitive with these methods, and we conclude that churn can be predicted using an explainable, intuitive, and white-box decision-rule algorithm.

Additive Main Effect and Multiplicative Interaction Analysis for Grain Yield of Early Maturing Sorghum [Sorghum Bicolor (L.) Moench] Varieties in Drought Prone Areas of Temben, North Ethiopia.

Shortage of widely adapted and high yielding variety is one of the major bottlenecks for production and productivity of sorghum in dry lowlands of Tigray region, Northern Ethiopia. A field experiment was conducted during the main seasons of 2018 and 2019 at four environments using randomized complete block design with three replications to evaluate the performance of ten early maturing sorghum varieties for grain yield using AMMI (Additive Main Effects and Multiplicative Interaction) model. The combined analysis of variance revealed highly significant (P≤0.01) genotype (G), environment (E) and genotype × environment interaction (GEI). The significant genotype by environment interaction effects were further partitioned in to two significant interaction principal components by using AMMI model. The AMMI analysis of variance showed that the genotype, environment and interaction sum squares contributed 41.55 %, 28.67 % and 29.78 % to the treatment sum squares for grain yield respectively. In addition, the first two IPCAs and interaction residual were significant. The first two IPCAs accounted for a total of 82.20 % of the interaction sum square. The results revealed that the observed yield variation among genotypes were due to genetic potential of genotypes and interaction rather than location differences. The highest yield was obtained from Melkam (3276 kg ha-1), while the lowest was from Grana-1 (2094 kg ha-1) and the average grain yield of genotypes was 2462 kg ha-1. Therefore, Melkam is selected as the best stable variety with consistent yielding performance across the testing environments in dry lowland areas of Temben and similar agro-ecologies in Tigray region, Northern Ethiopia.

Urban biotope classification incorporates urban forest and green infrastructure for improved environmental land-use planning in Mexico City

Urban forests are recognized worldwide as the most critical component of green infrastructure due to their capacity to provide various environmental goods and services. As cities continue to expand and their environmental problems intensify, there is a growing need for urban forests and green infrastructure to be better incorporated into strategic land-use planning, especially in developing cities. The first step in building an urban forest management plan is to capture characteristics of the urban forest and how these change across the built environment. Here, we used an urban biotope approach to classify urban forests and environmental characteristics in Mexico City. We sampled 500 fixed-area randomly stratified plots across the city to characterize urban forest structural and compositional variables. PCA and the broken-stick method were used to reduce the number of 25 urban forest variables down to five significant principal components that accounted for 78% of the data's cumulative variation. Ward's method helped classify biotopes into a hierarchical system with seven finer-level biotopes defined by urban forest characteristics (Dunn = 0.09, AC = 0.98), nested within two broader-level biotopes defined by forest canopy conditions (Silhouette = 0.59, AC = 0.99). A no-tree canopy biotope was extracted from sampling locations with no trees. The biotopes derived here can fundament biotope mapping, and support decision-making in urban forest planning, including the identification of available planting spaces, tree diversity targets, and canopy protection. Our work in Mexico City demonstrates how the biotope approach can be adapted and used to better incorporate urban forests and green infrastructure into future management planning for any city.

Chemometric Approach for the Evaluation of Quality and Thermo-Oxidative Stability of Soybean, Palm Olein and Canola Oils and Their Blends.

The objective of our study is to evaluate quality and thermo oxidative stability of soybean, palm olein, and canola oils and their blends. The binary blends of SO:POO and CO:POO were formulated in a ratio of 75:25 and ternary blend were prepared by blending CO:POO:SO in a ratio of 35:30:35. To monitor the thermal stability, pure oils and their blends were subjected to heating at 180°C for 4 hours. Heating process led to a substantial increase in free fatty acid (FFA), peroxide value (PV), p-anisidine value (p-AV), and saponification value (SV), while iodine value (IV) and oxidative stability index (OSI) were decreased. The principal component analysis (PCA) was also performed. The data indicated that three significant principal components have eigenvalue ˃ 1, which collectively constitute 98.8% of the total variance. PC1 contributed mainly with a total value of 50.1%, followed by PC2 (36.2%) and PC3 (12.5%). Results of the present study revealed that the binary and ternary blends exhibited greater oxidative stability than the pure oils. But, the ternary blend CO:POO:SO in a ratio of (35:30:35) was found to be better on the basis of stability and health point of view than other blends. Overall, our study demonstrated the utility of chemometric approaches for evaluating the quality and stability of vegetable oils and their blends, which can be helpful in the selection and optimization of oil blends for food applications.

Integrated Visual Assessment Method for Infestation by Lebbek Borer (Xystrocera globosa) in Rain Trees of Singapore

"Background: In Singapore, determining the level of infestation by lebbek borer (Xystrocera globosa)(Olivier, 1795)[Coleoptera: Cerambycidae] is the crucial first step in control of this pestra in rain tree (Albizia saman [Jacq.] Merr.)[Fabales: Fabaceae]. Current assessment methods rely on symptoms such as canopy colour, defoliation, dieback, and actual estimation of borer population via counting of larvae or exit holes created by adults. Currently, there is a lack of systematic approach to integrate different tree health indicators and symptoms to quantify infestation level. This gap poses challenges in assessment of treatment efficacy as managers could not quantitatively determine whether infestation level has changed following treatment. Thus, this study aimed to develop a visual assessment method that can integrate all mentioned symptoms to quantify infestation level. Methods: We surveyed a total of 388 rain trees and used principal component analysis (PCA) to investigate the correlation between X. globosa infestation and different borer infestation symptoms. Borer Infestation Score (BIS) formula was developed based on the linear combinations of the statistically significant principal component. Results: Infestation level was strongly associated with bark peeling, exit holes, and proximity of bark peeling and/or exit holes to trunk base and weakly associated with defoliation, dieback, and canopy colour. Developed BIS formula generated numerical values that distinguished between noninfested and infested trees, reflected infestation level in surveyed areas and temporal progression of infestation. Conclusions: Described integrated visual assessment method can be executed quickly on field. BIS formula generates quantitative scores easy to be interpreted, tracked, and compared.

Multi-sensor cyber-physical sorting system (CPSS) based on Industry 4.0 principles: A multi-functional approach

Products global demand constantly grows, along with drifts of the volume of waste produced, thus having a negative impact on the environment. Besides the advancement of artificial intelligence and cutting-edge sensing and actuation systems, allow for traditional recycling procedures to be digitised and become more efficient. Among these, automated sorting machines allow such efficacy, by using multiple recognition technologies to categorise items, instead of relying on humans to separate them. In this sense, this paper presents a new approach being included in the Cyber-Physical Sorting System (CPSS) capable to be adapted in different industries such as urban wastes, construction & demolition wastes as well as mineral sorting. The materials are laid out over a conveyor belt, where multi-purpose visual sensors (hyper-spectral, industrial and short-wave infrared) acquire images in order to classify and sort them in different bins using robotic arms. The architecture of the proposed CPSS allows the integration of additional sensors, increasing the interoperability with upcoming sensing devices. Furthermore, an intelligent hyper-spectral component adapted to urban wastes was developed that classifies glass, metal, paper, and plastic using multi-spectral data in visual and near-infrared ranges between 470nm - 975nm. Scatter-correction methods and derivatives were applied to extract noise-free features, while an exploratory analysis based on simple and robust Principal component analysis (PCA) was performed to identify the most significant principal components for the training phase. During the evaluation, the neural network performed better when normalisation and Savtizky and Golay (SG) filtering was applied to the dataset against other classifiers.

Heterogenous ensemble learning driven multi-parametric assessment model for hardware Trojan detection

Semiconductor technologies have gained wide-spread attention across industries due to its use in varied digital signal processing units, healthcare-devices, industrial communication, science and technologies, entertainment, smart devices etc. However, high design-complexities, untrusted foundries, third-party processors etc., have raised the concern of intrusion, particularly Hardware Trojans. Hardware Trojan, a specifically designed micro-intruder that can cause data breaches, system malfunctions etc. And hence may result disastrous consequences. To alleviate such problems, though different methods have been suggested for Trojan detection; however, pre-manufacturing methods can't guarantee Trojan-free design due to non-trusted Foundries and Third-party IPs. Similarly, post-manufacturing methods like side channel analysis or gate -level netlist are limited due to highly complex design, large structure and unavailability of universal golden design structures. Considering it as motivation, this paper proposes a robust register transfer logic (RTL) analysis assisted multi-parameter-based machine learning-driven Hardware Trojan detection system. We applied delay, power consumption and resource utilization profiles to perform Trojan detection over AES-256 circuits. Aforesaid features were extracted using VIVADO tool, which were further employed for resampling process using SMOTE, SMOTE-ENN, SMOTE-Borderline algorithms. Here, the use of resampling intended to alleviate data-imbalance or skewness. Moreover, it helped retaining sufficiently large features for machine learning based training. The resampled features were processed for feature selection using Significant Predictor Test, Cross-Correlation Test and Principal Component Analysis (PCA), distinctly. The selected features were trained using a novel heterogenous ensemble learning model encompassing a total of 11 base-classifiers belonging to Naïve Bayes, Neuro-computing, Decision Tree, Random Forest, Extra Tree classifier and Bagging-AdaBoost algorithms. Applying the maximum voting ensemble, the proposed model performed path-level Trojan detection, where the simulated results affirmed accuracy, F-Measure and Area Under Curve (AUC) of almost 100%, which is higher than any known approaches towards Hardware Trojan detection.

Vascular Cross‐Section, Rather Than Tortuosity, Can Classify First‐Pass Outcome of Mechanical Thrombectomy for Ischemic Stroke

Background Vascular geometry plays an important role in stroke thrombectomy outcomes, but few studies have examined complex characteristics of vessel morphology. The authors hypothesized that engineered vessel cross‐section features could be used to predict thrombectomy first‐pass effect (FPE). Methods The authors analyzed computed tomography angiography and noncontrast computed tomagraphy from 50 patients with anterior circulation stroke thrombectomy. After segmentation, traditional metrics (vessel tortuosity and angulation) were calculated from vessel centerlines that were transformed into the same coordinate system and same region of interest. Univariate statistical analysis and geometric morphometrics were used to interrogate differences in geometry between cases that did and did not achieve FPE, which classical angulation and tortuosity did not quantify. To describe these differences, complex cross‐section features were engineered and quantified using a semiautomatic pipeline. Machine learning was used to train predictive models of FPE based on significant cross‐section features. Results Only one local tortuosity metric was significantly different ( q =0.019) between FPE and first‐pass failure cases. The most significant principal component score ( q =0.012) from geometric morphometrics highlighted the M1 segment of the middle cerebral artery and upper cavernous internal carotid artery variation as morphological indicators of first‐pass outcome. Fifteen cross‐section features, corresponding to internal carotid artery and middle cerebral artery regions, were significantly different between cases that did and did not achieve FPE. Predictive models exhibited a strong prediction of FPE (area under the curve=0.98±0.05) and outperformed models using traditional tortuosity and angulation features. Conclusion Cross‐section features are a novel class of powerful and interpretable predictors of FPE, which could assist in treatment decision‐making.

Water quality modelling using principal component analysis and artificial neural network

The study investigates the latent pollution sources and most significant parameters that cause spatial variation and develops the best input for water quality modelling using principal component analysis (PCA) and artificial neural network (ANN). The dataset, 22 water quality parameters were obtained from Department of Environment Malaysia (DOE). The PCA generated six significant principal component scores (PCs) which explained 65.40 % of the total variance. Parameters for water quality variation are mainlyrelated to mineral components, anthropogenic activities, and natural processes. However, in ANN three input combination models (ANN A, B, and C) were developed to identify the best model that can predict water quality index (WQI) with very high precision. ANN A model appears to have the best prediction capacity with a coefficient of determination (R2) = 0.9999 and root mean square error (RMSE) = 0.0537. These results proved that the PCA and ANN methods can be applied as tools for decision-making and problem-solving for better managing of river quality.

Inherent dimension of the affective space: Analysis using electromyography and machine learning

Among dimensional models of emotions, two- and three-dimensional are most popular, while the true dimension of affective space is a matter of debates. Here we study the inherent dimension of the emotion space represented in facial expressions, along with the mapping of electromyography (EMG) signals recorded from facial muscles to expressed emotions. For this purpose, an experiment was conducted with parallel EMG recording from three facial muscles (Zygomaticus Major, Corrugator Supercilii, and Masseter) and video registration of the face with automated emotion recognition from the video stream. Data analysis based on machine learning methods confirmed the 3D nature of the affective space (at least its part reflected in facial expressions). This result is consistent with the VAD and PAD models. Possibilities of accounting for complex, higher-order, or social emotions without introducing additional dimensions are discussed. The second finding of this study is the ability to reconstruct all three significant principal components of expressed affects using EMG signals recorded from three facial muscles with the help of machine learning.

Genetic Diversity and Evaluation of Assembled Rice (Oryza sativa L.) Germplasm for Breeding Purposes in Northern Ghana

Background of the Study: Rice (Oryza sativa L) is an important staple food crop that feed over half of the global population and it has become the cereal that provides a major source of calories for the urban and rural poor in Africa. Rice has is the second most important food staple after maize in Ghana and its consumption keeps increasing as a result of population growth, urbanization and change in consumer habits. Rice yield across Ghana is far below achievable yield. The need for increasing rice yield depends not only on cultural/traditional practices but also on their inbuilt genetic potential to withstand stresses. Adequate diverse rice germplasm is a pre-requisite for breeding varieties to meet local biotic, abiotic and grain quality challenges. Knowledge of germplasm diversity and genetic relationships among breeding materials is valuable information for crop improvement.
 Aim: To identify the diversity among assembled rice germplasms for evaluation and possibly exploit its genetic variability for earliness for cultivation in the Guinea and Sudan Savanna ecologies.
 Study Design: The experiment was conducted at CSIR-SARI research station, Nyanpkala. Using a Randomized Complete Block Design with two replications, and a plot size of 4 m x 3 m was used. Field data taken included 10 qualitative and 11 quantitative traits. The data were statistically analyzed for various descriptive statistics.
 Results: Correlation analysis, Principal Component Analysis (PCA) and Cluster analysis were used to assess genetic variability. Most of the morphological traits showed remarkable differences in their distribution. Five significant principal components were identified accounting for 78.11% of the total variation. Cluster analysis based on the morphological data grouped the germplasm into two distinct clusters suggesting diversity among the assembled rice germplasms.
 Conclusions: The rice germplasm used in the present study displayed variability for most of the studied traits with the exception of ligule. Twenty one out of the 100 germplasm were distant from the rest, and were selected to constitute a core collection for further improvement.
 Recommendations: Diversity revealed in this study is narrow. It is, therefore, recommended that rice breeding programs in Ghana should include new genetically unrelated genotypes in order to broaden the genetic base of Ghanaian rice germplasm.

Phenotypic Characterization of a Wild-Type Population of Cornelian Cherries (Cornus mas L.) from Austria

Cornelian cherry (Cornus mas L.) belongs to a group of fruit and nut species growing in Europe considered to be underused economically, although it has been recognized as a potential regional “superfood” and as a source of valuable bioactive compounds. Phenotyping fruits of 30 accessions of an Austrian wildtype population of C. mas allowed to evaluate their nutraceutical potential. Ten fruits per accession were characterized by morphological and morphometric approaches. Biochemical analyses were performed to determine the respective amounts of vitamin C, sugars, anthocyanins, iridoids and flavonoids. Both datasets were subjected to statistical analyses. Morphological and morphometric characterization and biochemical analyses enabled the identification of the individuals with the highest economic value. Statistical treatment of data identified the most significant principal components. The first phenotypic profiling of bioactive compounds of wildtype C. mas in Austria yielded a high variability. Dealing with wildtype plants, this is not surprising. However, our results allow to select among the Austrian C. mas accessions the most interesting individuals for further breeding of this alternative fruit with interesting nutritional values.

Pancreatitis-Associated PRSS1-PRSS2 Haplotype Alters T-Cell Receptor Beta (TRB) Repertoire More Strongly Than PRSS1 Expression

Pancreatitis-Associated PRSS1-PRSS2 Haplotype Alters T-Cell Receptor Beta (TRB) Repertoire More Strongly Than PRSS1 Expression

The genetic architecture of blood pressure variability: A genome-wide association study of 9370 participants from UK Biobank.

Long‐term blood pressure variability (BPV) is a risk factor for cardiovascular diseases, dementia, and stroke. However, its genetic architecture is not fully understood. This study aims to explore its genetic factors and provide more evidence on the mechanisms and further pathological study of BPV. The genome‐wide association study (GWAS) is based on the UK Biobank cohort. There were four data collection rounds from 2006 to 2020, and 9370 participants with more than three blood pressure measurements were included. They had a median age of 55 and a male percentage of 50.1%. The phenotypes (BPV) were calculated by four methods and the genetic data contains 6 884 260 single nucleotide polymorphisms (SNPs) after imputation and quality control. A linear regression model was performed with adjustments for sex, age, genotype array, and a significant principal component. Subgroup analysis was performed on hypertension‐free participants. The significant and suggestive significant P thresholds were set as 5 × 10−8 and 1 × 10−6. Six genetic loci (BAD, CCDC88B, GPR137, PLCB3, RPS6KA4 for systolic BPV, and WWC2 for diastolic BPV) were identified by coding region SNPs at the suggestive significant P threshold (1 × 10−6). Among them, gene CCDC88B and RPS6KA4 reached the significant P threshold (5 × 10−8), with the strongest signal of SNP rs1229536170 (P = 6.36 × 10−8, β = –.29). The annotation results indicate that genes CCDC88B, GPR137, RPS6KA4, and BAD are associated with long‐term SBPV. Their functions of inflammation, epithelial dysfunction, and apoptosis are related to artery stiffness, which was reported as potential mechanisms of BPV.

Intrinsic Dimensionality as a Metric for the Impact of Mission Design Parameters.

High‐resolution space‐based spectral imaging of the Earth's surface delivers critical information for monitoring changes in the Earth system as well as resource management and utilization. Orbiting spectrometers are built according to multiple design parameters, including ground sampling distance (GSD), spectral resolution, temporal resolution, and signal‐to‐noise ratio. Different applications drive divergent instrument designs, so optimization for wide‐reaching missions is complex. The Surface Biology and Geology component of NASA's Earth System Observatory addresses science questions and meets applications needs across diverse fields, including terrestrial and aquatic ecosystems, natural disasters, and the cryosphere. The algorithms required to generate the geophysical variables from the observed spectral imagery each have their own inherent dependencies and sensitivities, and weighting these objectively is challenging. Here, we introduce intrinsic dimensionality (ID), a measure of information content, as an applications‐agnostic, data‐driven metric to quantify performance sensitivity to various design parameters. ID is computed through the analysis of the eigenvalues of the image covariance matrix, and can be thought of as the number of significant principal components. This metric is extremely powerful for quantifying the information content in high‐dimensional data, such as spectrally resolved radiances and their changes over space and time. We find that the ID decreases for coarser GSD, decreased spectral resolution and range, less frequent acquisitions, and lower signal‐to‐noise levels. This decrease in information content has implications for all derived products. ID is simple to compute, providing a single quantitative standard to evaluate combinations of design parameters, irrespective of higher‐level algorithms, products, applications, or disciplines.