Principal Component Analysis Modes Research Articles

Scapular morphology is associated with certain patterns of glenohumeral osteoarthritis, but not with full-thickness rotator cuff tears

BackgroundThe role of anatomic factors in rotator cuff (RC) pathology or in the development of shoulder osteoarthritis (OA) is not yet well understood or recognized. While two-dimensional imaging and measurements are easy to capture and interpret, three-dimensional (3D) methods provide a more thorough description and analysis of the scapula’s complex shape. The purpose of this study is to investigate whether scapular anatomy is associated with RC tears or glenohumeral OA using 3D statistical shape modelling (SSM). MethodsA total of 126 computed tomography (CT)-scans of complete scapulae were captured from three cohorts: RC pathology, OA, and control (C). The RC cohort included patients with posterosuperior RC tears, the OA cohort included patients with glenohumeral OA, and the C cohort included patients without shoulder pathology, imaged for other purposes. The RC and OA cohorts each included 55 patients matched by age and gender, while the C cohort included 16 patients, without a specific matching. A full segmentation of each scapula was manually created and 3D surfaces were reconstructed for input to a SSM analysis. SSM-based correspondence particles were analyzed using principal component analysis (PCA) and linear discriminant analysis (LDA). Various landmarks were manually identified on each scapula. From these landmarks, 3D measurements of glenoid version and inclination, critical shoulder angle (CSA), acromial angle, among others, were obtained. Measurements were compared between the three cohorts using ANOVA or Student’s T-test, as appropriate. ResultsThere were no statistically significant differences in mean age (RC:57±9, OA:58±8 and C:61±7 years) or gender proportion (males:RC:51%, OA:51% and C:75%) between the three cohorts. 13 PCA modes captured significant shape variation (83.3% of overall variation) of the scapula. The LDA was significantly different between OA and both RC and C (RC:1.0±1.4, OA: -1.0±1.9 and C:1.4±1.2). Compared to C, the OA cohort had smaller CSA (24.9±6.1° vs. 32.4±4.3°; p<0.001), larger acromion angle (65.4±9.3° vs. 57.4±9.5°; p=0.019), among other parameters. Compared to RC, the OA cohort also had less distance between coracoid and acromion (47.2±5.8° vs. 44.2±6.4°; p=0.026), as well as similar morphological differences as to the C cohort. There were no significant differences between RC and C cohort. ConclusionsPCA and LDA applied to manually segmented scapulae revealed statistically significant differences in scapular morphology between OA and control scapulae (a more superior and more horizontal acromion with a lower CSA), but only minimal differences between RC and control scapulae (except a more anterior anterolateral corner of the acromion).

Differentiating Left Ventricular Remodeling in Aortic Stenosis From Systemic Hypertension.

Left ventricular (LV) hypertrophy occurs in both aortic stenosis (AS) and systemic hypertension (HTN) in response to wall stress. However, differentiation of hypertrophy due to these 2 etiologies is lacking. The aim was to study the 3-dimensional geometric remodeling pattern in severe AS pre- and postsurgical aortic valve replacement and to compare with HTN and healthy controls. Ninety-one subjects (36 severe AS, 19 HTN, and 36 healthy controls) underwent cine cardiac magnetic resonance. Cardiac magnetic resonance was repeated 8 months post-aortic valve replacement (n=18). Principal component analysis was performed on the 3-dimensional meshes reconstructed from 109 cardiac magnetic resonance scans of 91 subjects at end-diastole. Principal component analysis modes were compared across experimental groups together with conventional metrics of shape, strain, and scar. A unique AS signature was identified by wall thickness linked to a LV left-right axis shift and a decrease in short-axis eccentricity. HTN was uniquely linked to increased septal thickness. Combining these 3 features had good discriminative ability between AS and HTN (area under the curve, 0.792). The LV left-right axis shift was not reversible post-aortic valve replacement, did not associate with strain, age, or sex, and was predictive of postoperative LV mass regression (R2=0.339, P=0.014). Unique remodeling signatures might differentiate the etiology of LV hypertrophy. Preliminary findings suggest that LV axis shift is characteristic in AS, is not reversible post-aortic valve replacement, predicts mass regression, and may be interpreted to be an adaptive mechanism.

Poster 354: Tibiofemoral Bone Shape Features Are Associated with Re-Tear after Anterior Cruciate Ligament Reconstruction

Introduction: Knee bony morphology is linked to anterior cruciate ligament (ACL) injury, ACL graft failure, and osteoarthritis (OA) risk, and employing tools such as statistical shape modeling could identify individuals prone to these joint conditions. Re-tear after ACL reconstruction remains a common and devastating complication, and therefore, recognizing patients at high risk of graft failure before primary ACL reconstruction is crucial for potential intervention to reduce failure rates. Magnetic resonance imaging (MRI) is most commonly obtained in the setting of possible ACL injury and before ACL reconstruction surgery, yet currently available MRI-based bone shape assessment methods require high-resolution sequences or manual segmentation, limiting feasibility of large-scale analysis. The development of automated tools for bone shape extraction from clinical MRI could allow for three-dimensional statistical shape modeling across large datasets. We established an automated pipeline for bone segmentation from proton density (PD)-weighted MRI scans, aiming to extract knee bony morphology from preoperative clinical MRIs in patients with ACL injuries. We hypothesized that our segmentation pipeline could uncover specific tibiofemoral joint bone shape features associated with ACL re-tear after reconstruction. Objective: To identify bone shape features prior to initial primary ACL reconstruction in patients with eventual ACL graft re-tear compared to those with a known, intact ACL graft. Methods: Patients who retrospectively experienced a subsequent re-tear after reconstruction within three years of initial injury were compared to those without a re-tear within three years (20 without subsequent re-tear, 22 with subsequent re-tear for distal femur; 22 without subsequent re-tear, 19 with subsequent re-tear for proximal tibia). The control group had MRI scans at 3 years after reconstruction that confirmed an intact ACL graft. An automatic femur and tibia segmentation framework was developed by training two deep convolutional neural networks using clinical, two-dimensional PD-weighted knee MRI sequences (3.5 mm slice thickness) from patients with ACL tears. Using the segmentation pipeline, bone was automatically segmented from preoperative clinical PD-weighted knee MRI sequence (3 mm slice thickness), three-dimensional triangulated meshes were generated, scaled to a reference, and aligned and matched to an atlas using Iterative Closest Point algorithm. Modes of shape variance were extracted from principal component analysis (PCA). The effect of each mode is modeled by three-dimensional surfaces for each specific shape feature. Modes were considered statistically significant if the mean principal component score between groups was p&lt;0.05 using Students T-test. Results: Statistical shape modeling of the femoral condyles using 15 PCA modes reveals significant differences (p &lt; 0.05) in Modes 1 and 4 at the initial injury in patients who subsequently experienced ACL re-tear after reconstruction compared to those without re-tear. Femur Mode 1 is characterized by a narrower intercondylar notch width, a wider medial condylar width, and an increased ratio of femoral condylar offset in patients with ACL re-tear. Femur Mode 4 illustrates a more prominent trochlear sulcus and increased surface area along the lateral femoral condyle relative to the medial condyle in patients with ACL re-tear. The initial 15 PCA modes of the distal femur collectively account for 75.04% of the normalized accumulated total variance. Statistical shape modeling of the tibial plateau with 15 PCA modes reveals that Mode 7 is significantly different (p &lt; 0.05) at the initial injury in patients who experienced subsequent ACL re-tear after reconstruction compared to those without re-tear. Tibia Mode 7 indicates diminished ACL facet prominence, a squared lateral tibial plateau, and a broader, flatter tibial spine in patients with subsequent ACL re-tear. The first 15 PCA modes of the proximal tibia collectively explain 73.60% of the normalized accumulated total variance. Conclusions: Using an automatic bone segmentation pipeline on preoperative clinical MRIs, we identify specific shape features in the tibiofemoral joint associated with ACL re-tear after reconstruction. Patients with subsequent ACL re-tear exhibit a variety of distinct three-dimensional morphology differences, including reduced intercondylar notch width, wider medial condylar width, increased lateral tibial plateau convexity, and broader, lateralized tibial spine, uncovered using statistical shape modeling. Statistical shape modeling identifies three-dimensional morphology patterns, thus capturing differences otherwise unaccounted for by traditional two-dimensional measurements. This approach offers predictive potential for patients at risk of ACL re-tear, improving treatment decisions for those with high-risk morphologic features.

Femurs in patients with hip dysplasia have fundamental shape differences compared with cam femoroacetabular impingement.

Femoral deformities are common in developmental dysplasia of the hip (DDH), but decisions about how to treat them are not standardized. Of interest are deformities that may be akin to cam femoroacetabular impingement (FAI). We used three-dimensional and two-dimensional measures to clarify the similarities and differences in proximal femur shape variation among female patients with DDH (n = 68) or cam FAI (n = 60). Three-dimensional measures included femoral head asphericity, as well as shape variation using statistical shape modeling and principal component analysis (PCA). Two-dimensional measures included the α-angle, head-neck offset (HNO) and the neck-shaft angle (NSA). Significant shape variations were captured in the first five PCA modes, with the greatest shared variation between groups being the length from the lesser trochanter to the femoral head and greater trochanter height. Variations unique to DDH were irregularities at different areas of the femoral head, but not at the lateral femoral head-neck junction where variation was strong in FAI. The FAI group also had unique variations in greater trochanter shape. DDH femoral heads were less spherical, as indicated by larger sphere-fitting errors (P < 0.001). Radiographically, the DDH group had significantly smaller α-angles (P < 0.001), larger head-neck offsets (P = 0.02) and larger NSAs (P < 0.001). Both the articular and extra-articular regions of the proximal femur have distinct shape features in DDH and cam FAI that can uniquely affect the biomechanics of each disorder. Accordingly, approaches to addressing each disorder should be unique.

Machine learning framework for the real-time reconstruction of regional 4D ocean temperature fields from historical reanalysis data and real-time satellite and buoy surface measurements

The ability to model 4D properties of near-coast ocean regions in real time is valuable for both general environmental monitoring and for specialized industry applications. We present a computationally parsimonious framework to build improved spatiotemporal models that combine dynamics inferred from historical high-fidelity model outputs and real-time sensor measurements. We are motivated by a temperature data set of the Massachusetts Bay from sensors that can only obtain measurements at the ocean’s surface. We first apply standard principal component analysis (PCA) to a historical reanalysis data set of the time-evolving 3D (x,y,z) temperature field. Next, we train a temporal convolutional neural network (TCN) to predict the time coefficients of the vertical PCA modes, and their variance, as a function of surface temperature. Simultaneously, we estimate the time-evolving 2D (x,y) surface temperature field from the satellite and buoy measurements with multi-fidelity Gaussian process regression (GPR). Finally, the surface temperature is used as input to the neural network to probabilistically predict the PCA coefficients and reconstruct the full 4D temperature field. The results are compared to in-situ measurements at all depths, and the median absolute error is found to be 0.97°C. Overall, the proposed framework is able to produce more accurate models in less time, and the uncertainty quantification can be leveraged for future decision-making.

Structural and pKa Estimation of the Amphipathic HR1 in SARS-CoV-2: Insights from Constant pH MD, Linear vs. Nonlinear Normal Mode Analysis.

A comprehensive understanding of molecular interactions and functions is imperative for unraveling the intricacies of viral protein behavior and conformational dynamics during cellular entry. Focusing on the SARS-CoV-2 spike protein (SARS-CoV-2 sp), a Principal Component Analysis (PCA) on a subset comprising 131 A-chain structures in presence of various inhibitors was conducted. Our analyses unveiled a compelling correlation between PCA modes and Anisotropic Network Model (ANM) modes, underscoring the reliability and functional significance of low-frequency modes in adapting to diverse inhibitor binding scenarios. The role of HR1 in viral processing, both linear Normal Mode Analysis (NMA) and Nonlinear NMA were implemented. Linear NMA exhibited substantial inter-structure variability, as evident from a higher Root Mean Square Deviation (RMSD) range (7.30 Å), nonlinear NMA show stability throughout the simulations (RMSD 4.85 Å). Frequency analysis further emphasized that the energy requirements for conformational changes in nonlinear modes are notably lower compared to their linear counterparts. Using simulations of molecular dynamics at constant pH (cpH-MD), we successfully predicted the pKa order of the interconnected residues within the HR1 mutations at lower pH values, suggesting a transition to a post-fusion structure. The pKa determination study illustrates the profound effects of pH variations on protein structure. Key results include pKa values of 9.5179 for lys-921 in the D936H mutant, 9.50 for the D950N mutant, and a slightly higher value of 10.49 for the D936Y variant. To further understand the behavior and physicochemical characteristics of the protein in a biologically relevant setting, we also examine hydrophobic regions in the prefused states of the HR1 protein mutants D950N, D936Y, and D936H in our study. This analysis was conducted to ascertain the hydrophobic moment of the protein within a lipid environment, shedding light on its behavior and physicochemical properties in a biologically relevant context.

Uncertainty analysis of measured geometric variations in turbine blades and impact on aerodynamic performance

Inevitable geometric variations significantly affect the performance of turbines or even that of entire engines; thus, it is necessary to determine their actual characteristics and accurately estimate their impact on performance. In this study, based on 1781 measured profiles of a typical turbine blade, the statistical characteristics of the geometric variations and the uncertainty impact are analyzed, and some commonly used uncertainty modelling methods based on Principal-Component Analysis (PCA) are verified. The geometric variations are found to be evident, asymmetric, and non-uniform, and the non-normality of the random distributions is non-negligible. The performance is notably affected, which is manifested as an overall offset, a notable scattering, and significant deterioration in several extreme cases. Additionally, it is demonstrated that the PCA reconstruction model is effective in characterizing major uncertainty characteristics of the geometric variations and their impact on the performance with almost the first 10 PCA modes. Based on a reasonable profile error and mean geometric deviation, the Gaussian assumption and stochastic-process-based model are also found to be effective in predicting the mean values and standard deviations of the performance variations. However, they fail to predict the probability of some extreme cases with high loss. Finally, a Chi-square-based correction model is proposed to compensate for this deficiency. The present work can provide a useful reference for uncertainty analysis of the impact of geometric variations, and the corresponding uncertainty design of turbine blades.

Statistical modeling: Assessing the anatomic variability of knee joint space width

Population-based knee joint space width (JSW) assessments are promising for the prevention and early diagnosis of osteoarthritis. This study aimed to establish the statistical shape and alignment model (SSAM) of knee joints for assessing anatomic variation in knee JSW in the healthy Chinese male population. CT scans of asymptomatic knee joints of healthy male participants (n = 107) were collected for manual segmentation to create mesh samples. The as-scanned positional error was reduced by a standard processing flow of deformable mesh registration. Principal component analysis (PCA) was performed to create a tibiofemoral SSAM that was trained on all mesh samples. The anatomic variability of the JSW in the healthy Chinese male population was then assessed using the SSAM with regression analysis and 3D analysis by color-coded mapping. Almost all PCA modes had a linear influence on the anatomic variation of the medial and lateral JSW. The JSW variability within the SSAM was mainly explained by mode 1 (45.1 % of variation), demonstrating that this mode had the greatest influence on JSW variation. 3D assessment of the JSW showed that the minimum medial JSW varied from 2.76 to 3.23 mm, and its site shifted a short distance on the medial tibial plateau. The root-mean-square fitting and generalization errors of the SSAM were below 1 mm. This study will benefit the design and optimization of prosthetic devices, and may be applicable to the prevention and early diagnosis of osteoarthritis.

Low-cost assimilation for sound speed fields in the PCA framework

Forecast ocean sound speed fields are often inaccurate and need to be reconciled with observation data. Conventional data-assimilation methods used for this are generally quite computationally intensive. A compressed representation of the forecast sound speed fields can be obtained using principal component analysis (PCA), where the forecast fields are represented by a linear combination fo PCA modes. We develop a low-cost assimilation approach that updates the PCA compressed representation of the background forecast field, based on observation data.The approach uses Bayes‘ rule to obtain the maximum likelihood estimate for the update in the space spanned by the PCA modes. Significant cost savings come from the dimensionality reduction that is provided by PCA. Results are presented for sound speed fields derived from HYCOM ocean forecast data and expendable bathythermograph data obtained from the Scipps Institution of Oceanography.

Development and validation of a population-based anatomical colorectal model for radiation dosimetry in late effects studies of survivors of childhood cancer

PurposeThe purposes of this study were to develop and integrate a colorectal model that incorporates anatomical variations of pediatric patients into the age-scalable MD Anderson Late Effects (MDA-LE) computational phantom, and validate the model for pediatric radiation therapy (RT) dose reconstructions. MethodsColorectal contours were manually derived from whole-body non-contrast computed tomography (CT) scans of 114 pediatric patients (age range: 2.1–21.6 years, 74 males, 40 females). One contour was used for an anatomical template, 103 for training and 10 for testing. Training contours were used to create a colorectal principal component analysis (PCA)-based statistical shape model (SSM) to extract the population’s dominant deformations. The SSM was integrated into the MDA-LE phantom. Geometric accuracy was assessed between patient-specific and SSM contours using several overlap metrics. Two alternative colorectal shapes were generated using the first 17 dominant modes of the PCA-based SSM. Dosimetric accuracy was assessed by comparing colorectal doses from test patients’ CT-based RT plans (ground truth) with reconstructed doses for the mean and two alternative models in age-matched MDA-LE phantoms. ResultsWhen using all 103 PCA modes, the mean (min–max) Dice similarity coefficient, distance-to-agreement and Hausdorff distance between the patient-specific and reconstructed contours for the test patients were 0.89 (0.85–0.91), 2.1 mm (1.7–3.0), and 8.6 mm (5.7–14.3), respectively. The average percent difference between reconstructed and ground truth mean and maximum colorectal doses for the mean (alternative 1, 2) model were 6.3% (8.1%, 6.1%) and 4.4% (4.3%, 4.7%), respectively. ConclusionsWe developed, validated and integrated a colorectal PCA-based SSM into the MDA-LE phantom and demonstrated its dosimetric performance for accurate pediatric RT dose reconstruction.

Effect of Delphinidin on Metabolomic Profile in Breast Carcinogenesis.

Breast cancer is a malignant tumor which threat to women's physical and mental health. Delphinidin, one of the main anthocyanidins, has potent anti-cancer properties. In previous study, we found that delphinidin has the preventive role in MNU-induced breast carcinogenesis of rats, but the molecular mechanism by which delphinidin combats breast cancer has not been completely elucidated.The aim of the present study was to identify metabolic profile that account for delphinidin on the preventive effect on 1-methyl-1-nitrosourea (MNU)-induced breast carcinogenesis of rats. In the present study, liquid chromatography-mass spectrometry (LC-MS) was conducted to identify metabolic profiles of rat tissues collected from normal mammary glands (normal group), breast tumors derived from MNU-induced breast carcinogenesis models (control group) and delphinidin administration models (delphinidin group). Principal component analysis (PCA) and partial least squares-discriminate analysis (PLS-DA) were employed to identify biochemical patterns. The values of variable importance in the projection (VIP) in PLS-DA model combined with the P value of Student's t-test were used to determine important metabolites. An orthogonal partial least square discriminant analysis (OPLS-DA) was used to conduct the supervised analysis. The fitness and prediction capabilities of PCA modes were measured by R 2 and Q 2 value respectively. Potential biomarkers were subjected to pathway analysis with Metaboanalyst 3.0 based on the KEGG Pathway Database to identify related metabolic pathways. The PCA and PLS-DA analysis indicated that the proposed method were satisfactory for metabolomic analysis. Metabolites from the obtained features were further filtered by PLS-DA analysis with VIP>1.0 and P<0.05. The significant difference was appeared in 190 metabolites between normal group and control group (P<0.05). Eight most significant metabolic pathways were obtained on the basis of the results of P<0.05 data analysis between control and normal group, embodying in aminoacyl-tRNA biosynthesis, arginine biosynthesis, biosynthesis of unsaturated fatty acids, valine, leucine and isoleucine biosynthesis, purine metabolism, alanine, aspartate and glutamate metabolism, glycerophospholipid metabolism, histidine metabolism. A total of 48 metabolites were identified to be associated with protective effects of delphinidin on MNU-induced rats significantly(P<0.05). Compared with control group, a total of 5 metabolic pathways were significantly perturbed in response to delphinidin administration (p<0.05), including in taurine and hypotaurine metabolism, Glycerophospholipid metabolism, arachidonic acid metabolism, aminoacyl-tRNA biosynthesis and primary bile acid biosynthesis. Metabolites and metabolic pathways were identified to be associated with protective effects of delphinidin on MNU-induced rats. The findings provided new insights into the precise mechanism of delphinidin in preventing breast carcinogenesis.

All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes.

Introduction: Myriad disorders cause right ventricular (RV) dilation and lead to tricuspid regurgitation (TR). Because the thin-walled, flexible RV is mechanically coupled to the pulmonary circulation and the left ventricular septum, it distorts with any disturbance in the cardiopulmonary system. TR, therefore, can result from pulmonary hypertension, left heart failure, or intrinsic RV dysfunction; but once it occurs, TR initiates a cycle of worsening RV volume overload, potentially progressing to right heart failure. Characteristic three-dimensional RV shape-changes from this process, and changes particular to individual TR causes, have not been defined in detail. Methods: Cardiac MRI was obtained in 6 healthy volunteers, 41 patients with ≥ moderate TR, and 31 control patients with cardiac disease without TR. The mean shape of each group was constructed using a three-dimensional statistical shape model via the particle-based shape modeling approach. Changes in shape were examined across pulmonary hypertension and congestive heart failure subgroups using principal component analysis (PCA). A logistic regression approach based on these PCA modes identified patients with TR using RV shape alone. Results: Mean RV shape in patients with TR exhibited free wall bulging, narrowing of the base, and blunting of the RV apex compared to controls (p 0.05). Using four primary PCA modes, a logistic regression algorithm identified patients with TR correctly with 82% recall and 87% precision. In patients with pulmonary hypertension without TR, RV shape was narrower and more streamlined than in healthy volunteers. However, in RVs with TR and pulmonary hypertension, overall RV shape continued to demonstrate the free wall bulging characteristic of TR. In the subgroup of patients with congestive heart failure without TR, this intermediate state of RV muscular hypertrophy was not present. Conclusion: The multiple causes of TR examined in this study changed RV shape in similar ways. Logistic regression classification based on these shape changes reliably identified patients with TR regardless of etiology. Furthermore, pulmonary hypertension without TR had unique shape features, described here as the “well compensated” RV. These results suggest shape modeling as a promising tool for defining severity of RV disease and risk of decompensation, particularly in patients with pulmonary hypertension.

Le Cœur en Sabot: shape associations with adverse events in repaired tetralogy of Fallot

BackgroundMaladaptive remodelling mechanisms occur in patients with repaired tetralogy of Fallot (rToF) resulting in a cycle of metabolic and structural changes. Biventricular shape analysis may indicate mechanisms associated with adverse events independent of pulmonary regurgitant volume index (PRVI). We aimed to determine novel remodelling patterns associated with adverse events in patients with rToF using shape and function analysis.MethodsBiventricular shape and function were studied in 192 patients with rToF (median time from TOF repair to baseline evaluation 13.5 years). Linear discriminant analysis (LDA) and principal component analysis (PCA) were used to identify shape differences between patients with and without adverse events. Adverse events included death, arrhythmias, and cardiac arrest with median follow-up of 10 years.ResultsLDA and PCA showed that shape characteristics pertaining to adverse events included a more circular left ventricle (LV) (decreased eccentricity), dilated (increased sphericity) LV base, increased right ventricular (RV) apical sphericity, and decreased RV basal sphericity. Multivariate LDA showed that the optimal discriminative model included only RV apical ejection fraction and one PCA mode associated with a more circular and dilated LV base (AUC = 0.77). PRVI did not add value, and shape changes associated with increased PRVI were not predictive of adverse outcomes.ConclusionPathological remodelling patterns in patients with rToF are significantly associated with adverse events, independent of PRVI. Mechanisms related to incident events include LV basal dilation with a reduced RV apical ejection fraction.

A three-dimensional statistical shape model of the growing mandible

Mandibular growth and morphology are important topics in the field of oral and maxillofacial surgery. For diagnostic and planning purposes, a normative database or statistical shape model of the growing mandible can be of great benefit. A collection of 874 cadaveric children’s mandibles with dental age between 1 and 12 years old were digitized using computed tomography scanning and reconstructed to three-dimensional models. Point correspondence was achieved using iterative closest point and coherent point drift algorithms. Principal component analysis (PCA) was applied to find the main modes of variation in the data set. The average mandible was presented, along with the first ten PCA modes. The first mode explained 78% of the total variance; combining the first ten modes accumulated to 95% of the total variance. The first mode was strongly correlated with age and hence, with natural growth. This is the largest study on three-dimensional mandibular shape and development conducted thus far. The main limitation is that the samples lack information such as gender and cause of death. Clinical application of the model first requires validation with contemporary samples.

Planck 2018 results

Author(s): Aghanim, N; Akrami, Y; Ashdown, M; Aumont, J; Baccigalupi, C; Ballardini, M; Banday, AJ; Barreiro, RB; Bartolo, N; Basak, S; Battye, R; Benabed, K; Bernard, JP; Bersanelli, M; Bielewicz, P; Bock, JJ; Bond, JR; Borrill, J; Bouchet, FR; Boulanger, F; Bucher, M; Burigana, C; Butler, RC; Calabrese, E; Cardoso, JF; Carron, J; Challinor, A; Chiang, HC; Chluba, J; Colombo, LPL; Combet, C; Contreras, D; Crill, BP; Cuttaia, F; De Bernardis, P; De Zotti, G; Delabrouille, J; Delouis, JM; DI Valentino, E; DIego, JM; Dore, O; Douspis, M; Ducout, A; Dupac, X; Dusini, S; Efstathiou, G; Elsner, F; Enslin, TA; Eriksen, HK; Fantaye, Y; Farhang, M; Fergusson, J; Fernandez-Cobos, R; Finelli, F; Forastieri, F; Frailis, M; Fraisse, AA; Franceschi, E; Frolov, A; Galeotta, S; Galli, S; Ganga, K; Genova-Santos, RT; Gerbino, M; Ghosh, T; Gonzalez-Nuevo, J; Gorski, KM; Gratton, S; Gruppuso, A; Gudmundsson, JE; Hamann, J; Handley, W; Hansen, FK; Herranz, D; Hildebrandt, SR; Hivon, E; Huang, Z; Jaffe, AH; Jones, WC; Karakci, A; Keihanen, E; Keskitalo, R; Kiiveri, K; Kim, J; Kisner, TS | Abstract: In the original version, the bounds given in Eqs. (87a) and (87b) on the contribution to the early-time optical depth, (15,30), contained a numerical error in deriving the 95th percentile from the Monte Carlo samples. The corrected 95% upper bounds are: τ(15,30) l 0:018 (lowE, flat τ(15, 30), FlexKnot), (1) τ(15, 30) l 0:023 (lowE, flat knot, FlexKnot): (2) These bounds are a factor of 3 larger than the originally reported results. Consequently, the new bounds do not significantly improve upon previous results from Planck data presented in Millea a Bouchet (2018) as was stated, but are instead comparable. Equations (1) and (2) give results that are now similar to those of Heinrich a Hu (2021), who used the same Planck 2018 data to derive a 95% upper bound of 0.020 using the principal component analysis (PCA) model and uniform priors on the PCA mode amplitudes.

Exposures and Health Risks Associated with Elements in Diets from a Gold Mining Area.

There has been a concern of metal contamination from the mining areas which could expose the population to toxic metal through consumption of food products in the regions. Therefore, the study was conducted to analyze 18 elements using X-ray fluorescence analyzer and to assess for metal exposure through dietary intakes. Dietary recall survey instrument was used to collect consumption, demographic, and anthropometric data from 308 consumers in life-stage groups: toddlers, adolescents, and adults. Cassava, maize, rice, tomato, and yam indigenous diet samples (66) from the gold mining regions of Ghana were obtained by purposively sampling from street vendors. Principal component analysis was used to apportion pollution source. Health risks due to dietary elements were quantified using US EPA probabilistic protocols for cancer and non-cancer disease endpoints. Deficiency and overage risks of essential elements were tested against reference nutrient intakes. The element-diet-location mode of principal component analysis separated cassava and maize diets as significant vehicles for both essential and toxic elements apportioned to mining pollution source. The chronic daily intake trend revealed a preponderance for arsenic (maize: toddlers 0.009, adolescents 0.025 adults 0.010mg/kg-d) and lead (maize: toddlers 0.009, adolescents 0.026, adults 0.010mg/kg-d) exposure through maize diets compared to cassava diets. The hazard quotient showed (HQ > 1) life-stage risk differentiation for barium, although there were cumulative and pervasive health risks for all age groups based on the hazard index (Ag, Ba, Cd, Co, Hg, Mn, Mo, Sb, Sn, Sr, U, V, W, and Zr). The likely cancer risk (mode) for all life-stage groups (3 × 10-6-1 × 10-2) were also high in that they exceeded the 1 × 10-6 safety threshold. The modal margin of exposure (MoE < 1) alerted high public health concern due to lead-induced toxicities among all life-stage groups through all diet vehicles. Regarding nutritional adequacy, there were possible overage risks associated with selenium, including deficiency risks for calcium, potassium, and zinc for all life-stage groups. Toddlers and adolescents were susceptible to iron deficiency risks through cassava diets and iron overage risks through maize diets.

Frictional Segmentation of the Chilean Megathrust From a Multivariate Analysis of Geophysical, Geological, and Geodetic Data

AbstractWe present a formal seismic segmentation model of the Chile Margin derived from a multivariate analysis of gravity anomalies, basal friction and interplate locking from GPS velocities. These independent proxies of the frictional structure along the megathrust integrate over timescales of 101–107 years. We analyze their covariance using Principal Component Analysis (PCA) and extracting the Empirical Orthogonal Functions (EOFs) and Principal Components (PCs). We find that (a) the first and first + second PCA modes for the triple combination of the proxies explain (respectively) 41.4% and 69.4% of the total variance and (b) the first‐order spatial pattern of each isolated proxy is largely reproduced when it is combined with the other two proxies. This suggests that the three proxies share a first order along‐trench tendency that must be connected through a common physical process. Comparing our results with rupture areas of earthquakes and using the rate‐and‐state friction theory, we infer that trench‐perpendicular PCs reflect the limits of the velocity‐weakening (VW) seismogenic zone. Polarity changes in along‐trench EOFs (mostly for gravity) mark changes between unstable and conditionally stable frictional regimes inside the VW zone and we used them to define the boundaries of 17 unitary segments. These segments correlate with first‐order tectonic features of the Andes at &gt;103‐km scales as well as with zones of multi‐segment ruptures at 102‐km scale. We analyze the combined influence of subducting and upper‐plate geologic features on the nature of persistent seismic barriers. Our results have implications for understanding seismotectonic processes along the Andean margin and elsewhere.

Interannual Variability of GPS Heights and Environmental Parameters over Europe and the Mediterranean Area

Vertical deformations of the Earth’s surface result from a host of geophysical and geological processes. Identification and assessment of the induced signals is key to addressing outstanding scientific questions, such as those related to the role played by the changing climate on height variations. This study, focused on the European and Mediterranean area, analyzed the GPS height time series of 114 well-distributed stations with the aim of identifying spatially coherent signals likely related to variations of environmental parameters, such as atmospheric surface pressure (SP) and terrestrial water storage (TWS). Linear trends and seasonality were removed from all the time series before applying the principal component analysis (PCA) to identify the main patterns of the space/time interannual variability. Coherent height variations on timescales of about 5 and 10 years were identified by the first and second mode, respectively. They were explained by invoking loading of the crust. Single-value decomposition (SVD) was used to study the coupled interannual space/time variability between the variable pairs GPS height–SP and GPS height–TWS. A decadal timescale was identified that related height and TWS variations. Features common to the height series and to those of a few climate indices—namely, the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA), and the multivariate El Niño Southern Oscillation (ENSO) index (MEI)—were also investigated. We found significant correlations only with the MEI. The first height PCA mode of variability, showing a nearly 5-year fluctuation, was anticorrelated (−0.23) with MEI. The second mode, characterized by a decadal fluctuation, was well correlated (+0.58) with MEI; the spatial distribution of the correlation revealed, for Europe and the Mediterranean area, height decrease till 2015, followed by increase, while Scandinavian and Baltic countries showed the opposite behavior.

Estimation of initial-state structures in high-energy heavy-ion collisions using principal component analysis

In high-energy heavy-ion collisions, structures in the initial collision zone are a matter of intense investigation, both from theory and experimental points of views. A large number of models have been developed to represent the initial state of the collision including the Glauber model and color glass condensate (CGC), among others. Another important aspect of the study is to investigate proper observables that will be sensitive to the initial collision zone. In this work, we discuss a formalism to implement the spatial clusters at the partonic level in the string melting version of the ampt model for PbPb collisions at $\sqrt{{s}_{NN}}=200\phantom{\rule{4pt}{0ex}}\mathrm{GeV}$. These clusters are then propagated through the ampt hadronization scheme. Principal component analysis (PCA) has been used on the $\ensuremath{\eta}$, $\ensuremath{\phi}$, and ${p}_{T}$ distributions of the produced charged particles and the eigenvalues have been compared before and after the implementation of the clustering. It is found that for all these three different distributions, all the prominent PCA modes show sensitivity to the clustering. A centrality dependent study has also been performed on those eigenvalues.

Comparison between slow anisotropic LE4PD fluctuations and the principal component analysis modes of ubiquitin.

The biological function and folding mechanisms of proteins are often guided by large-scale slow motions, which involve crossing high energy barriers. In a simulation trajectory, these slow fluctuations are commonly identified using a principal component analysis (PCA). Despite the popularity of this method, a complete analysis of its predictions based on the physics of protein motion has been so far limited. This study formally connects the PCA to a Langevin model of protein dynamics and analyzes the contributions of energy barriers and hydrodynamic interactions to the slow PCA modes of motion. To do so, we introduce an anisotropic extension of the Langevin equation for protein dynamics, called the LE4PD-XYZ, which formally connects to the PCA "essential dynamics." The LE4PD-XYZ is an accurate coarse-grained diffusive method to model protein motion, which describes anisotropic fluctuations in the alpha carbons of the protein. The LE4PD accounts for hydrodynamic effects and mode-dependent free-energy barriers. This study compares large-scale anisotropic fluctuations identified by the LE4PD-XYZ to the mode-dependent PCA predictions, starting from a microsecond-long alpha carbon molecular dynamics atomistic trajectory of the protein ubiquitin. We observe that the inclusion of free-energy barriers and hydrodynamic interactions has important effects on the identification and timescales of ubiquitin's slow modes.