Principal Component Modes Research Articles

Quantitative Analysis of Lung Shape in Idiopathic Pulmonary Fibrosis: Insights Into Disease- and Age-Associated Patterns

Rationale and objectivesFibrotic scarring in idiopathic pulmonary fibrosis (IPF) typically develops first in the posterior-basal lung tissue before advancing to involve more of the lung. The complexity of lung shape in the costo-diaphragmatic region has been proposed as a potential factor in this regional development. Intrinsic and disease-related shape could therefore be important for understanding IPF risk and its staging. We hypothesised that lung and lobe shape in IPF would have important differences from controls. Materials and MethodsA principal component (PC) analysis was used to derive a statistical shape model (SSM) of the lung for a control cohort aged > 50 years (N=39), using segmented lung and fissure surface data from CT imaging. Individual patient shape models derived for baseline (N=18) and follow-up (N=16) CT scans in patients with IPF were projected to the SSM to describe shape as the sum of the SSM average and weighted PC modes. Associations between the first four PC shape modes, lung function, percentage of fibrosis (fibrosis%) and pulmonary vessel-related structures (PVRS%), and other tissue metrics were assessed and compared between the two cohorts. ResultsShape was different between IPF and controls (p<0.05 for all shape modes), with IPF shape forming a distinct shape cluster. Shape had a negative relationship with age in controls (p = 0.013), but a positive relationship with age in IPF (p = 0.026). Some features of shape changed on follow-up. Shape in IPF was associated with fibrosis% (p<0.05) and PVRS% (p<0.05). ConclusionQuantitative comparison of lung and lobe shape in IPF with controls of a similar age reveals shape differences that are strongly associated with age and percent fibrosis. The clustering of IPF cohort shape suggests that it could be an important feature to describe disease.

Morphological features of the non-affected side of the hindfoot in patients with unilateral varus ankle osteoarthritis

BackgroundThe innate shape characteristics of the hindfoot bones alter the loading conditions of the foot and thus may be associated with an increased risk of developing varus ankle osteoarthritis (OA). This study aimed to clarify the innate morphological patterns of the hindfoot bones that may be associated with ankle OA by analyzing the differences between the bone morphology of the non-affected side of patients with unilateral varus ankle OA and that of healthy participants. MethodsIn this case-control study, computed tomography images were used to develop three-dimensional models of three hindfoot bones (distal tibia with fibula, talus, and calcaneus) from 23 non-affected sides of patients with radiography-diagnosed unilateral ankle OA and 22 healthy control participants. Anatomical and sliding landmarks were placed on the surface of each bone, and the principal components (PCs) of shape variation among specimens were independently calculated for each bone, preserving homology between individuals. The PC modes representing 5% or more of the overall variation were statistically compared between the ankle OA and control groups. ResultsSignificant differences were identified between the OA and control groups in the fifth PC mode for the tibia with fibula (proportion of variance, 5.1%; p =.025), fifth PC mode for the talus (6.7%, p =.031), and third PC mode for the calcaneus (7.4%, p =.001). The hindfoot bones of the participants who developed ankle OA had the following innate morphological characteristics: the lateral malleolar articular surface of the fibula was shifted superiorly, tibial plafond was enlarged posteroinferiorly, posterior width of the talar trochlea was narrower, talonavicular articular surface of the talus was oriented more frontally, anterior-middle talocalcaneal articular surfaces of the talus were more medially shifted and those of the calcaneus were flatter, calcaneal sustentaculum tali was less protruding, and lateral plantar process of the calcaneus was more superiorly positioned. ConclusionsThese distinctive morphological alterations may increase the incidence and progression of varus ankle OA through aberrant anterior translation, internal rotation, and varus tilting of the talus.

Characteristic Scales of Tropical Convection Based on the Japanese Advanced Himawari-8 Imager Observations

Convective activities play an important role in tropical weather systems. To investigate the characteristic scales of convection, a method combining a principal component (PC) analysis with Fourier decomposition is applied to brightness temperature observations from Advanced Himawari-8 Imager (AHI). Characteristic scales of different modes in tropical convective systems are obtained. The explained variance reduces rapidly from the first to the 60th PC mode by two magnitudes; the horizontal scale decreases from over 2000 km to about 100 km, and the timescale changes from more than 4 days to around 5 h. By a detailed comparison of the first, 20th, 40th, and 60th PC modes, it is found that large-scale (over 2000 km in wavelength) convective activities usually have significant nocturnal enhancement, whereas meso-scale (about 100 km in wavelength) convective activities feature short period and fast change with more intense development in regions of active large-scale convection. This study may be of some importance for the choice of AHI data assimilation cycling interval, the horizontal resolution as well as data thinning for geostationary satellite observations.

Investigation of Cooperative Modes for Collective Molecules Using Grid-Based Principal Component Analysis.

A grid-based principal component analysis method (GBPCA) has been developed and implemented to investigate the modes of collective molecules present in the cube in the grid system from their trajectories using molecular dynamics (MD) simulation. This method is applied to the simulations of water, methane, and hydrated proteins. In the cases of single molecules, GBPCA demonstrates that while individual molecules interact with other molecules and move randomly, the collective molecules nevertheless produce cooperative principal component (PC) modes. Notably, these PC modes of the collective molecules formed vorticial or symmetrical structures, with some resembling torus-like structures. Such structures were observed not only in simulations with models that reproduced real molecules, such as water or methane, but also in simulations with artificial molecules that were modified to interact weakly or only repulsively. On the contrary, molecules without any interactions did not exhibit the cooperative PC modes that lead to the formation of specific structures. These results imply that when molecules subject to intermolecular forces are located in a space in which they can interact with one another, the PC modes of the collective molecules will form vorticial and torus-like structures. In the cases of hydrated proteins, the results reveal that the collective molecules in the protein and water molecules showed cooperative PC modes, forming vorticial or torus-like structures, as in the cases of the water or methane molecules. The PC modes of the water molecules close to the protein were influenced by the protein motion and indicated cooperative modes with the protein. Moreover, proteins with the same folds had similar PC mode structures. Besides, the PC modes of the proteins composed of multiple domains appeared independently in the protein domain. Hence, it can be understood that the cooperative motions of the collective molecules of the protein and surrounding water molecules contribute strongly to the protein's structure and function. These results are expected to help our understanding of the dynamics of molecules in the liquid state.

Fine scale surface climate in complex terrain using machine learning

AbstractAccurate and high spatial resolution (&lt;100 m) surface climate information is crucial for process‐based modelling in hydrology, ecology, agriculture, urban studies etc, especially in complex terrain landscapes where coarse grid resolution information (∼10 km) is inadequate to represent pronounced local variability. We used a machine learning‐based workflow to predict high resolution (30 m) and sub‐daily atmospheric variables fields of near‐surface air temperature and humidity, and wind speed. The method used the Principal Component Analysis (PCA) decomposition applied on ground stations observations or Global Climate Model (GCM) residual error, in a sequence with bias correction and statistical models (Linear Regression‐LR, Artificial Neural Network model‐ANN and Empirical Quantile Mapping‐EQM) to provide downscaling from large scale atmospheric conditions to complex terrain variability. The predictions described relationships of Principal Component (PC) scores dependent on GCM temporal variability on 6‐hourly basis (with LR or ANN or EQM), and PC loadings dependent on topographic indexes to help providing horizontal sub‐grid extrapolation. The methods were validated with a 1‐year dataset from a dense weather stations network deployed in a complex terrain basin in tropical climate of Southeast Brazil. We present an exhaustive description of the PC modes daily/seasonal variability for each variable, and their spatial variability associated to the topography and thermal driven circulations. The predictions in general substantially improved accuracy when compared to GCM outputs, especially near the valley and in sheltered area where local effects are mandatories. Specially, ANN and EQM significantly improved the predictions at the variability of extreme events, such as the formation of strong cold air pooling or wetting in the valley.

Systematic Dissociation Pathway Searches Guided by Principal Component Modes.

We introduce a novel method, Pathway Search guided by Internal Motions (PSIM), that efficiently finds molecular dissociation pathways of a ligand-receptor system with guidance from principal component (PC) modes obtained from molecular dynamics (MD) simulations. Modeling ligand-receptor dissociation pathways can provide insights into molecular recognition and has practical applications, including understanding kinetic mechanisms and barriers to binding/unbinding as well as design of drugs with desired kinetic properties. PSIM uses PC modes in multilayer internal coordinates to identify natural molecular motions that guide the search for conformational switches and unbinding pathways. The new multilayer internal coordinates overcome problems with Cartesian and classical internal coordinates that fail to smoothly present dihedral rotation or generate nonphysical distortions. We used HIV-1 protease, which has large-scale flap motions, as an example protein to demonstrate use of the multilayer internal coordinates. We provide examples of algorithms and implementation of PSIM with alanine dipeptide and chemical host-guest systems, 2-naphthyl ethanol-β-cyclodextrin and tetramethylammonium-cryptophane complexes. Tetramethylammonium-cryptophane has slow binding/unbinding kinetics. Its residence time, the length to dissociate tetramethylammonium from the host, is ∼14 s from experiments, and PSIM revealed 4 dissociation pathways in approximately 150 CPU h. We also searched the releasing pathways for the product glyceraldehyde-3-phosphate from tryptophan synthase, and one complete dissociation pathway was constructed after running multiple search iterations in approximately 300 CPU h. With guidance by internal PC modes from MD simulations, the PSIM method has advantages over simulation-based methods to search for dissociation pathways of molecular systems with slow noncovalent kinetic behavior.

Statistical modeling to characterize relationships between knee anatomy and kinematics

The mechanics of the knee are complex and dependent on the shape of the articular surfaces and their relative alignment. Insight into how anatomy relates to kinematics can establish biomechanical norms, support the diagnosis and treatment of various pathologies (e.g., patellar maltracking) and inform implant design. Prior studies have used correlations to identify anatomical measures related to specific motions. The objective of this study was to describe relationships between knee anatomy and tibiofemoral (TF) and patellofemoral (PF) kinematics using a statistical shape and function modeling approach. A principal component (PC) analysis was performed on a 20-specimen dataset consisting of shape of the bone and cartilage for the femur, tibia and patella derived from imaging and six-degree-of-freedom TF and PF kinematics from cadaveric testing during a simulated squat. The PC modes characterized links between anatomy and kinematics; the first mode captured scaling and shape changes in the condylar radii and their influence on TF anterior-posterior translation, internal-external rotation, and the location of the femoral lowest point. Subsequent modes described relations in patella shape and alta/baja alignment impacting PF kinematics. The complex interactions described with the data-driven statistical approach provide insight into knee mechanics that is useful clinically and in implant design.

Remote estimation of phytoplankton size fractions using the spectral shape of light absorption.

Phytoplankton size structure plays an important role in ocean biogeochemical processes. The light absorption spectra of phytoplankton provide a great potential for retrieving phytoplankton size structure because of the strong dependence on the packaging effect caused by phytoplankton cell size and on different pigment compositions related to phytoplankton taxonomy. In this study, we investigated the variability in light absorption spectra of phytoplankton in relation to the size structure. Based on this, a new approach was proposed for estimating phytoplankton size fractions. Our approach use the spectral shape of the normalized phytoplankton absorption coefficient (a(ph)(λ)) through principal component analysis (PCA). Values of a(ph)(λ) were normalized to remove biomass effects, and PCA was conducted to separate the spectral variance of normalized a(ph)(λ) into uncorrelated principal components (PCs). Spectral variations captured by the first four PC modes were used to build relationships with phytoplankton size fractions. The results showed that PCA had powerful ability to capture spectral variations in normalized a(ph)(λ), which were significantly related to phytoplankton size fractions. For both hyperspectral a(ph)(λ) and multiband a(ph)(λ), our approach is applicable. We evaluated our approach using wide in situ data collected from coastal waters and the global ocean, and the results demonstrated a good and robust performance in estimating phytoplankton size fractions in various regions. The model performance was further evaluated by a(ph)(λ) derived from in situ remote sensing reflectance (R(rs)(λ)) with a quasi-analytical algorithm. Using R(rs)(λ) only at six bands, accurate estimations of phytoplankton size fractions were obtained, with R(2) values of 0.85, 0.61, and 0.76, and root mean-square errors of 0.130, 0.126, and 0.112 for micro-, nano-, and picophytoplankton, respectively. Our approach provides practical basis for remote estimation of phytoplankton size structure using a(ph)(λ) derived from satellite observations or rapid field instrument measurements in the future.

Analysis of ATMS striping noise from its Earth scene observations

AbstractThe differences between brightness temperature observations and simulated observations based on numerical weather predictions, i.e., O‐B, for Advanced Technology Microwave Sounder (ATMS) temperature sounding channels exhibit a clear striping pattern [Bormann et al., 2013]. We propose to first use the principal component analysis to isolate scan‐dependent features such as the cross‐track striping from the atmospheric signal and then to use an Ensemble Empirical Mode Decomposition (EEMD) to extract the striping noise in ATMS Earth scene brightness temperature observations for both temperature and water vapor sounding channels. It is shown that the Principal Component (PC) coefficient of the first PC mode, which mainly describes a scan‐dependent feature of cross‐track radiometer measurements, captures the striping noise. The EEMD is then applied to the PC coefficient to extract the first three high‐frequency intrinsic mode functions (IMFs), which are denoted as the PC1/IMF3 noise. When the PC1/IMF3 noise is removed from the data, the striping noise is imperceptible in the global distribution of O‐B for ATMS temperature sounding channels 1–16. Using the same method, it is demonstrated that the striping noise is also present in ATMS water vapor sounding channels 17–22. The magnitude of the ATMS striping noise is about ±0.3 K for the temperature sounding channels and ±1.0 K for the moisture sounding channels. The same technique is also applied to Advanced Microwave Sounding Unit‐A (AMSU‐A), AMSU‐B, and Microwave Humidity Sounder (MHS). The striping noise is undetectable for AMSU‐A but present in AMSU‐B and MHS data.

Application of Principal Component Analysis to CHAMP Radio Occultation Data for Quality Control and a Diagnostic Study

Abstract A principal component analysis (PCA) method is applied to Challenging Minisatellite Payload (CHAMP) level-2 radio occultation (RO) observations and the corresponding global analyses from the National Centers for Environmental Prediction (NCEP) in March 2004. The PCA is performed on a square symmetric vertical correlation matrix of observed or modeled RO profiles. By decomposing the matrix into pairs of loadings (EOFs) and associated principal components (PCs), outliers are identified and important modes that explain most variances of the vertical variability of the atmosphere as represented by the GPS RO data and the NCEP analyses are extracted and compared. Specifically, a quality control of RO data based on Hotelling’s T 2 index is applied first, which removes 255 RO profiles from 4884 total profiles (about 5%) and smoothes the distributions of PC modes, making the remaining GPS RO dataset much more meaningful. The leading PC mode for global refractivity explains 60% of the total variance and is associated with a symmetric zonal pattern, with positive anomalies in the Tropics and negative anomalies at the two poles. The second PC mode explains an additional 16% of the total variance and shows a dipole pattern with positive anomalies in the North Pole and negative anomalies in the South Pole. Three significant positive anomalies are also found in the second and third PC modes over three predominant convective areas in the western Pacific, South America, and Africa in the Tropics. The first leading PC mode calculated from global NCEP analyses compared favorably with that from CHAMP observations, which proves that NCEP analyses are capable of representing most of the variance of the atmospheric profiles. However, disagreements between CHAMP observations and NCEP analyses are noticed in the second EOF over the Tropics and the Southern Hemisphere (SH). It is also found that the NCEP analyses describe CHAMP-observed larger vertical scale features better than smaller-scale features, captures features of more leading EOF modes in the Northern Hemisphere than in the SH and the Tropics, and does not capture the vertical structures revealed by the EOFs in CHAMP observations near and above the tropopause in the Tropics.

Wind tides in the Rio de la Plata estuary: Meteorological conditions

AbstractSynoptic situations associated with strong southeasterly winds over the Rio de la Plata (RP) estuary are locally known as sudestadas. This phenomenon affects the coast of Buenos Aires city and its outskirts with floods, normally accompanied with persistent rainfall.Storm surge levels (river level minus the astronomical tide levels) were used to identify cases associated with sudestadas. Although there is a local belief that most sudestadas are caused by cyclogenesis during winter, they actually occur during the whole year, being indeed less frequent in winter. In addition, they are not necessarily associated with cyclonic circulation. The maximum frequency of sudestadas is observed during summer and at the beginning of spring and autumn. This annual distribution was similar when two different water‐level thresholds were used to characterize the storm surge.The three principal components (PCs) of the atmospheric circulation field at 1000 hPa accompanying the sudestadas explain 75% of the variance. These patterns show that sudestadas are associated with either a combination of a high‐pressure system to the south of the RP and relatively low pressure to the north (first and second PC modes) or a very deep low‐pressure zone to the north of the river (third PC mode). These cases are associated with southeasterly winds, which produce tide waves on the RP.Almost all the sudestadas associated with the third PC occur in winter and they reach on average greater peak levels than the others. These cases are associated with an intense low‐pressure system north of the RP, which is typically due to the frequent cyclogenesis in that region. Copyright © 2004 Royal Meteorological Society

Changes in the Southwest Monsoon mean daily rainfall intensity in Sri Lanka: relationship to the El Niño–Southern Oscillation

Daily rainfall data for 187 stations in Sri Lanka spanning the period 1960–1996 were analyzed to investigate the spatial and temporal characteristics of the mean rainfall intensity (MRI) through this time interval with special focus on the Southwest Monsoon (May–September). Particular emphasis was laid on temporal changes in the MRI series. The mean and standard deviation (SD) of the MRI data showed considerable spatial variation. Regression analysis expressing precipitation as a function of time at the various stations revealed distinct spatial trends; the results point to high MRI in lowland areas and low MRI in mountain areas. Principal Components Analysis of the temporal relationships among a reduced set of stations located in an equal-sized grid showed that the three dominant principal components (PCs) are characterized by the maximum and minimum mean and SD of the MRI series together with the mean number of rainy days. The first, second and third PC modes show significant patterns of the MRI data series over the northern half, southern half and southwestern coastal belt of Sri Lanka, respectively. The time series pattern of the dominant PC modes revealed distinct changes in MRI over time. A noticeable higher value in MRI was found from 1977 to 1996; this tendency is most pronounced for the first PC mode. The time series of the Southern Oscillation Index was found to be closely related to changes in the MRI patterns associated with the first PC mode. In addition, El Niño years coincide with low values of the first PC mode. Some La Niña years show a positive response for the first and third PC modes, while there is no clear response for the MRI pattern identified by the second PC.

Characteristics of Surface Airflow and Pressure Patterns over the Island of Taiwan during TAMEX

Abstract Principal component (PC) analysis was used to study the wind and sea level pressure patterns over the island of Taiwan during the early summer rainy season. An eight-point, running-mean, low-pass filter was applied to the 3-h surface winds and sea level pressure deviations from its areal average to remove signals with a time scale of 1 day or less. The first and the second PC modes of the low-pass-filtered wind data account for 66% and 13% of the total variance, respectively. The first, the second, and the third PC modes of the low-pass-filtered pressure deviations account for 63%, 22%, and 10% of the total variance, respectively. Composite analysis of low-pass-filtered surface variables was made during a 3-day period for each TAMEX (Taiwan Area Mesoscale Experiment) frontal passage episode, using the timing of the maximum PC scores of the dominant PC modes to arrive at a general picture of the evolution of surface airflow and pressure patterns. The prefrontal southwesterly flow reached the maxim...

Meteorological forcing and sea level variability in the Aegean Sea

The effect of atmospheric pressure and surface wind vector on sea level variability in the Aegean Sea is investigated. Daily values of sea level, atmospheric pressure, and surface wind covering the period 1984–1987 from five stations are analyzed. Principal component (PC) analysis is used to extract the spatially coherent signal from the sea level and atmospheric pressure time series, while rotary principal component analysis is used to extract the coherent part of surface wind. The first PC modes are subsequently used in multiple regression analysis in the frequency domain in order to examine the dependence of sea level fluctuations on each of the parameters. The response of sea level to atmospheric pressure is found to be frequency dependent. The response is overisostatic for lower frequencies and becomes underisostatic for higher frequencies approaching isostasy only at the limit of analysis (0.5 cycles/day). The contribution of the first principal wind component is found to be very small.