Number Of Principal Components Research Articles

Trend and principal components of HIV/AIDS among adults in SSA

This study aimed to identify the most important principal components (PCs) that contribute to the prevalence and change of HIV/AIDS in 44 SSA and data from different national and international datasets. The study estimated HIV prevalence, trend, and principal component analysis (PCA). Using the elbow method, the number of important PCs and contributions was identified. The quality of representation was checked, and more contributing variables for most important PCs were identified. Finally, the status by prevalence, the progress by trend, the more influenced component by PCA, and the more influenced variable with quality of representation by PCs were reported. The study found that HIV prevalence varied significantly, with 30 of the countries showed good progress/decline. Four PCs accounted for 51% of the total variance. Literacy, cohabitation, media exposure, and HIV status awareness are highly contributing factors. Based on these findings, a gap-based response will help reduce the burden of HIV.

Population stratification correction using Bayesian shrinkage priors for genetic association studies.

Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity. To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs. Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses. The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.

Identification of procymidone in rapeseed oils based on olfactory visualization technology

A new method for qualitative identification of pesticide residues in edible oil based on olfactory visualization technology is proposed. Firstly, according to the pre-experimental experience, 12 suitable chemical dyes were selected to prepare sensor arrays to form an olfactory visualization system. The system was used to gather volatile odor information in rapeseed oil samples that had different levels of procymidone residues. Subsequently, the olfactory information was translated into a visual representation. Then, the visual data, i.e. color features, were preprocessed and underwent principal component analysis to extract and reduce the dimensionality of sensor image information, resulting in the optimal number of principal components (PCs). Finally, three different models based on the sensor image features were constructed for analysis. These models included the support vector machine (SVM), the random forest (RF) and the kernel based extreme learning machine (KELM). In process of model correction, the parameters of the three models were optimized by dung beetle optimizer (DBO) algorithm, resulting in the identification of the optimal parameters. The results show that the cumulative variance contribution increases slowly when the number of PCs starts from 6, and the three models (i.e. DBO-SVM, DBO-RF, and DBO-KELM) have the best prediction results when PCs = 6. Compared to the DBO-RF and DBO-KELM models, the training and prediction performance of the SVM model based on the DBO optimization algorithm is more stable and has the higher recognition accuracy of the samples, reaching 100%. The results show that it is feasible to use olfactory visualization technology to achieve high-precision qualitative identification of edible oil pesticide residues.

Limitations of principal components in quantitative genetic association models for human studies.

Principal Component Analysis (PCA) and the Linear Mixed-effects Model (LMM), sometimes in combination, are the most common genetic association models. Previous PCA-LMM comparisons give mixed results, unclear guidance, and have several limitations, including not varying the number of principal components (PCs), simulating simple population structures, and inconsistent use of real data and power evaluations. We evaluate PCA and LMM both varying number of PCs in realistic genotype and complex trait simulations including admixed families, subpopulation trees, and real multiethnic human datasets with simulated traits. We find that LMM without PCs usually performs best, with the largest effects in family simulations and real human datasets and traits without environment effects. Poor PCA performance on human datasets is driven by large numbers of distant relatives more than the smaller number of closer relatives. While PCA was known to fail on family data, we report strong effects of family relatedness in genetically diverse human datasets, not avoided by pruning close relatives. Environment effects driven by geography and ethnicity are better modeled with LMM including those labels instead of PCs. This work better characterizes the severe limitations of PCA compared to LMM in modeling the complex relatedness structures of multiethnic human data for association studies.

Exploring Integrability-Chaos Transition with a Sequence of Independent Perturbations.

A gas of interacting particles is a paradigmatic example of chaotic systems. It is shown here that, even if all but one particle are fixed in generic positions, the excited states of the moving particle are chaotic. They are characterized by the number of principal components (NPC)-the number of integrable system eigenstates involved into the nonintegrable one, which increases linearly with the number of strong scatterers. This rule is a particular case of the general effect of an additional perturbation on the system chaotic properties. The perturbation independence criteria supposing the system chaoticity increase are derived here as well. The effect can be observed in experiments with photons or cold atoms as the decay of observable fluctuation variance, which is inversely proportional to NPC and, therefore, to the number of scatterers. This decay indicates that the eigenstate thermalization is approached. The results are confirmed by numerical calculations for a harmonic waveguide with zero-range scatterers along its axis.

Automatic monitoring and detection of tail-biting behavior in groups of pigs using video-based deep learning methods.

Automated monitoring of pigs for timely detection of changes in behavior and the onset of tail biting might enable farmers to take immediate management actions, and thus decrease health and welfare issues on-farm. Our goal was to develop computer vision-based methods to detect tail biting in pigs using a convolutional neural network (CNN) to extract spatial information, combined with secondary networks accounting for temporal information. Two secondary frameworks were utilized, being a long short-term memory (LSTM) network applied to sequences of image features (CNN-LSTM), and a CNN applied to image representations of sequences (CNN-CNN). To achieve our goal, this study aimed to answer the following questions: (a) Can the methods detect tail biting from video recordings of entire pens? (b) Can we utilize principal component analyses (PCA) to reduce the dimensionality of the feature vector and only use relevant principal components (PC)? (c) Is there potential to increase performance in optimizing the threshold for class separation of the predicted probabilities of the outcome? (d) What is the performance of the methods with respect to each other? The study utilized one-hour video recordings of 10 pens with pigs prior to weaning, containing a total of 208 tail-biting events of varying lengths. The pre-trained VGG-16 was used to extract spatial features from the data, which were subsequently pre-processed and divided into train/test sets before input to the LSTM/CNN. The performance of the methods regarding data pre-processing and model building was systematically compared using cross-validation. Final models were run with optimal settings and evaluated on an independent test-set. The proposed methods detected tail biting with a major-mean accuracy (MMA) of 71.3 and 64.7% for the CNN-LSTM and the CNN-CNN network, respectively. Applying PCA and using a limited number of PCs significantly increased the performance of both methods, while optimizing the threshold for class separation did result in a consistent but not significant increase of the performance. Both methods can detect tail biting from video data, but the CNN-LSTM was superior in generalizing when evaluated on new data, i.e., data not used for training the models, compared to the CNN-CNN method.

Cardiorespiratory Coordination Among Intermediate And Novice Female Collegiate Rowers

Elite rowers present high levels of cardiorespiratory performance. Rowers’ on-water performance can be predicted by a cardiopulmonary exercise test (CPET) and related measured parameters such as maximum oxygen uptake test (VO2max), ventilation (VE), heart rate (HR), expired fraction of oxygen (FeO2) and carbon dioxide (FeCO2). However, such testing provides little information about the non-linear dynamic interactions during rowing and the qualitative synergetic reconfigurations between cardiovascular and respiratory systems to adjust to the individual rowing performance. Cardiorespiratory coordination (CRC) has been proposed as a method to measure the co-variation among cardiorespiratory variables during a CPET. PURPOSE: To measure and compare the CRC through principal components analysis (PCA) between intermediate (9) and novice (9) rowers. METHODS: Eighteen females, members of NCAA Division II team, participated based on their off-water performance on 6000 m time trial and training status (i.e., Intermediate vs, Novice) in a discontinuous incremental rowing ergometer test to exhaustion. A variety of cardiorespiratory values were recorded. VE, HR, FeO2, and FeCO2 values during the last two stages before volitional fatigue between intermediate and novice rowers were analyzed by PCA on SPSS vs28. The number of principal components (PCs) and the first PC eigenvalues were computed for each group. RESULTS: While 67% of participants in the intermediate group showed one PC, only 22% of the novice group displayed 1 single PC. The formation of an additional PC in N rowers was the result of the shift of FeCO2 from the PC1 cluster of variables. CONCLUSIONS: Intermediate rowers showed a trend towards lower number of PCs compared to novice rowers, reflecting a higher degree of CRC. These findings point toward a higher efficiency of cardiorespiratory function in intermediate rowers, potentially as a result of improved bicarbonate buffering efficiency during high-intensity exercise. Intermediate rowers presented better gas exchange and relied less on ventilation for a given oxygen consumption. CRC appears as a complementary measure to assess aerobic fitness as well as cardiorespiratory interactions and their response to exercise in rowing may be useful to coaches, athletes and other stakeholders.

An eigenvalue ratio approach to inferring population structure from whole genome sequencing data.

Inference of population structure from genetic data plays an important role in population and medical genetics studies. With the advancement and decreasing cost of sequencing technology, the increasingly available whole genome sequencing data provide much richer information about the underlying population structure. The traditional method originally developed for array-based genotype data for computing and selecting top principal components (PCs) that capture population structure may not perform well on sequencing data for two reasons. First, the number of genetic variants p is much larger than the sample size n in sequencing data such that the sample-to-marker ratio is nearly zero, violating the assumption of the Tracy-Widom test used in their method. Second, their method might not be able to handle the linkage disequilibrium well in sequencing data. To resolve those two practical issues, we propose a new method called ERStruct to determine the number of top informative PCs based on sequencing data. More specifically, we propose to use the ratio of consecutive eigenvalues as a more robust test statistic, and then we approximate its null distribution using modern random matrix theory. Both simulation studies and applications to two public data sets from the HapMap 3 and the 1000 Genomes Projects demonstrate the empirical performance of our ERStructmethod.

FindPC: An R package to automatically select the number of principal components in single-cell analysis.

Principal component analysis is widely used in analyzing single-cell genomic data. Selecting the optimal number of principal components (PCs) is a crucial step for downstream analyses. The elbow method is most commonly used for this task, but it requires one to visually inspect the elbow plot and manually choose the elbow point. To address this limitation, we developed six methods to automatically select the optimal number of PCs based on the elbow method. We evaluated the performance of these methods on real single-cell RNA-seq data from multiple human and mouse tissues and cell types. The perpendicular line method with 30 PCs has the best overall performance, and its results are highly consistent with the numbers of PCs identified manually. We implemented the six methods in an R package, findPC, that objectively selects the number of PCs and can be easily incorporated into any automatic analysis pipeline. findPC R package is freely available at https://github.com/haotian-zhuang/findPC. Supplementary data are available at Bioinformatics online.

Determination of optimal number of independent components in yield traits in rice

The principal component regression (PCR) and the independent component regression (ICR) are dimensionality reduction methods and extremely important in genomic prediction. These methods require the choice of the number of components to be inserted into the model. For PCR, there are formal criteria; however, for ICR, the adopted criterion chooses the number of independent components (ICs) associated to greater accuracy and requires high computational time. In this study, seven criteria based on the number of principal components (PCs) and methods of variable selection to guide this choice in ICR are proposed and evaluated in simulated and real data. For both datasets, the most efficient criterion and that drastically reduced computational time determined that the number of ICs should be equal to the number of PCs to reach a higher accuracy value. In addition, the criteria did not recover the simulated heritability and generated biased genomic values.

Nonnegative principal component analysis in thin layer fingerprint screening: A case of Gentiana extracts from in vitro cultures

Twenty-one species of Gentiana L. were successfully grown in vitro in the same conditions and 72 samples of various cultures of these species (root, shoots, cotyledon callus, hypocotyl callus, and root callus) were obtained. The investigated species were as follows: G. affinis, G. andrewsii, G. bhutanica, G. burseri, G. cachemirica, G. capitata, G. crassicaulis, G. dahurica, G. decumbens, G. freyniana, G. frigida, G. gelida, G. grossheimii, G. kurroo, G. macrophylla, G. paradoxa, G. robusta, G. scabra, G. septemfida, G. siphonantha, and G. tianschanica. The obtained samples were extracted with methanol–acetone–water (3:1:1) mixture, evaporated to dryness, and subjected to TLC on silica gel with mobile phase ethyl acetate–methanol–water (8:2:2) in sandwich mode. Data fusion of two densitometric modes: extinction at 254 nm and fluorescence at 312 nm (emission above 370 nm) were used as a fingerprint of each sample. Densitograms were denoised, subjected to baseline removal, warped, and analyzed with principal component analysis (PCA), hierarchical cluster analysis and—the novel proposal—nonnegative PCA. This allowed to split negatively intercorrelated fingerprint features to subsequent principal components (PCs), which increases the number of PCs required to see the same information, but these PCs are much more informative.

Data-Driven Patient Clustering and Differential Clinical Outcomes in the Brigham and Women's Rheumatoid Arthritis Sequential Study Registry.

ObjectiveTo use unbiased, data‐driven, principal component (PC) and cluster analysis to identify patient phenotypes of rheumatoid arthritis (RA) that might exhibit distinct trajectories of disease progression, response to treatment, and risk for adverse events.MethodsPatient demographic, socioeconomic, health, and disease characteristics recorded at entry into a large, single‐center, prospective observational registry cohort, the Brigham and Women’s Rheumatoid Arthritis Sequential Study (BRASS), were harmonized using PC analysis to reduce dimensionality and collinearity. The number of PCs was established by eigenvalue >1, cumulative variance, and interpretability. The resulting PCs were used to cluster patients using a K‐means approach. Longitudinal clinical outcomes were compared between the clusters over 2 years.ResultsAnalysis of 142 variables from 1,443 patients identified 41 PCs that accounted for 77% of the cumulative variance in the data set. Cluster analysis distinguished 5 patient clusters: 1) less RA disease activity/multimorbidity, shorter RA duration, lower incidence of comorbidities; 2) less RA disease activity/multimorbidity, longer RA duration, more infections, psychiatric comorbidities, health care utilization; 3) moderate RA disease activity/multimorbidity, more neurologic comorbidity; 4) more RA disease activity/multimorbidity, shorter RA duration, more metabolic comorbidity, higher body mass index; 5) more RA disease activity/multimorbidity, longer RA duration, more hepatic, orthopedic comorbidity and RA‐related surgeries. The clusters exhibited differences in clinical outcomes over 2 years of follow‐up.ConclusionData‐driven analysis of the BRASS registry identified 5 distinct phenotypes of RA. These results illustrate the potential of data‐driven patient profiling as a tool to support personalized medicine in RA. Validation in an independent data set is ongoing.

Automatic sparse principal component analysis

The wide availability of computers enables us to accumulate a huge amount of data, thus effective tools to extract information from the huge volume of data have become critical. Principal component analysis (PCA) is a useful and traditional tool for dimensionality reduction of massive high‐dimensional datasets. Recently, sparse principal component (PC) loading estimation based on L1‐type regularization has drawn a large amount of attention. Although sparse PCA makes interpretation easily and performs dimension reduction without disturbance from noisy features, the existing studies on sparse PCA were based on an arbitrary number of PCs without any statistical justification. We propose a novel method, called as automatic sparse PCA, which can perform PC selection and sparse PC loading estimation, simultaneously. For PC selection, we first develop sparse singular value decomposition (sparse SVD), then incorporate sparsity into PC loading estimation. The proposed method enables us to perform dimension reduction and PC loading estimation, simultaneously. Furthermore, we can perform PCA without disturbance from noisy features. It can be seen through Monte Carlo experiments that the proposed automatic sparse PCA outperforms sparse structure identification and reconstructing data based on low‐dimensional projection. The proposed method is also applied to a number of real datasets and it can be also seen that our method achieves effectiveness for estimation accuracy and interpreting PCA results.

Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer.

Bladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix™) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the RametrixTM screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix™ screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control SurineTM. Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the RametrixTM screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, RametrixTM was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve RametrixTM models and demonstrate clinical relevance, this study demonstrates the ability of the RametrixTM screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.

Combining Statistical Shape Model and Principal Component Analysis to Estimate Left Ventricular Volume and Ejection Fraction.

Left ventricular ejection fraction (LVEF) assessment is instrumental for cardiac health diagnosis, patient management, and patient eligibility for participation in clinical studies. Due to its non-invasiveness and low operational cost, ultrasound (US) imaging is the most commonly used imaging modality to image the heart and assess LVEF. Even though 3D US imaging technology is becoming more available, cardiologists dominantly use 2D US imaging to visualize the LV blood pool and interpret its area changes between end-systole and end-diastole. Our previous work showed that LVEF estimates based on area changes are significantly lower than the true volume-based estimates by as much as 13%,1 which could lead to unnecessary and costly therapeutic decisions. Acquiring volumetric information about the LV blood pool necessitates either time-consuming 3D reconstruction or 3D US image acquisition. Here, we propose a method that leverages on a statistical shape model (SSM) constructed from 13 landmarks depicting the LV endocardial border to estimate a new patient's LV volume and LVEF. Two methods to estimate the 3D LV geometry with and without size normalization were employed. The SSM was built using the 13 landmarks from 50 training patient image datasets. Subsequently, the Mahalanobis distance (with size normalization) or the vector distance (without size normalization) between an incoming patient's LV landmarks and each shape in the SSM were used to determine the weights each training patient contributed to describing the new, incoming patient's LV geometry and associated blood pool volume. We tested the proposed method to estimate the LV volumes and LVEF for 16 new test patients. The estimated LVEFs based on Mahalanobis distance and vector distance were within 2.9% and 1.1%, respectively, of the ground truth LVEFs calculated from the 3D reconstructed LV volumes. Furthermore, the viability of using fewer principal components (PCs) to estimate the LV volume was explored by reducing the number of PCs retained when projecting landmarks onto PCA space. LVEF estimated based on 3 PCs, 5 PCs, and 10 PCs are within 6.6%, 5.4%, and 3.3%, respectively, of LVEF estimates using the full set of 39 PCs.

The Rametrix™ PRO Toolbox v1.0 for MATLAB®.

BackgroundExisting tools for chemometric analysis of vibrational spectroscopy data have enabled characterization of materials and biologicals by their broad molecular composition. The Rametrix™ LITE Toolbox v1.0 for MATLAB® is one such tool available publicly. It applies discriminant analysis of principal components (DAPC) to spectral data to classify spectra into user-defined groups. However, additional functionality is needed to better evaluate the predictive capabilities of these models when “unknown” samples are introduced. Here, the Rametrix™ PRO Toolbox v1.0 is introduced to provide this capability.MethodsThe Rametrix™ PRO Toolbox v1.0 was constructed for MATLAB® and works with the Rametrix™ LITE Toolbox v1.0. It performs leave-one-out analysis of chemometric DAPC models and reports predictive capabilities in terms of accuracy, sensitivity (true-positives), and specificity (true-negatives). Rametrix™PRO is available publicly through GitHub under license agreement at: https://github.com/SengerLab/RametrixPROToolbox. Rametrix™ PRO was used to validate Rametrix™ LITE models used to detect chronic kidney disease (CKD) in spectra of urine obtained by Raman spectroscopy. The dataset included Raman spectra of urine from 20 healthy individuals and 31 patients undergoing peritoneal dialysis treatment for CKD.ResultsThe number of spectral principal components (PCs) used in building the DAPC model impacted the model accuracy, sensitivity, and specificity in leave-one-out analyses. For the dataset in this study, using 35 PCs in the DAPC model resulted in 100% accuracy, sensitivity, and specificity in classifying an unknown Raman spectrum of urine as belonging to a CKD patient or a healthy volunteer. Models built with fewer or greater number of PCs showed inferior performance, which demonstrated the value of Rametrix™ PRO in evaluating chemometric models constructed with Rametrix™ LITE.

Principal component analysis of linear measures of young horses national champions of Mangalarga Marchador breed

ABSTRACT: The conformation is directly related to the quality of the movements, and can direct the selection by equine aptitude. This study aimed to identify which are the morphometric measurements that explain the total variance available in the marcha batida and picada gaits of young Mangalarga Marchador horses. Analyses were performed by evaluating 20 linear measurements of 420 champion horses. Measures were separated by gender (male-M and female-F), type of marcha, (batida-MB e picada-MP) and divided into eight age groups. Principal component analysis (PCA) was used to identify which measurements were most important in determining marcha variance by selecting principal component (PC) which sum of eigenvalues was able to explain the minimum percentage of 80% of the total variation. The PC number varied randomly according to age groups, being 2 to 3 in both genders in MP, 3 to 4 for M-MB, and up to 5 for F-MB, suggesting lower overall variability in MP, and higher in F-MB. There was no defined pattern concerning the amount of PC per age group, demonstrating that each category may have independent variations. Although, some repetitions of variables occurred similarly in different ages, sexes, and marcha types, the responsibility for the highest occurrence of variation was the posterior cannon and gaskin length. The significant variance in the length of these segments, regardless of gender, age, and marcha, and the fact they are not measured daily suggested there is not only a lack of standardization of these segments, but there is also size compensation among them since the group evaluated is composed of breed champions.

Improved supervised classification of bedrock in areas of transported overburden: Applying domain expertise at Kerkasha, Eritrea

A regional bedrock map provides a foundation from which to build geological interpretations. However, rapid and accurate bedrock mapping in an area that lacks outcrop is a common problem, especially in regions with sparse data. A historic bedrock map from an Au and base metal project in the Kerkasha district, Eritrea, is significantly improved by predicting bedrock distribution in areas previously mapped as transported overburden. Publicly-available remote sensing data (DTM and ASTER) were combined with airborne geophysical data (magnetics and radiometrics) to provide features for bedrock prediction. Remote sensing data were pre-processed using Principal Components Analysis (PCA) to yield an equal number of principal components (PC) as input features. Four iterations were trialled, using different combinations of remote sensing PC features. The two initial trials used all available remote sensing data but compared results when feature ranking and selection is applied to reduce the number of PCs used for training and classification. The subsequent two trials used subsets of available remote-sensing data, selected based on domain expertise (i.e., the domain-specific knowledge of a geologist), with all respective PCs were retained. Five-fold cross-validation scores were highest when a DTM, magnetics, and radiometrics data were included as input features. However, qualitative visual appraisal of predicted results across trials, complemented by maps of class membership uncertainty (using a measure of entropy), indicate that geologically-meaningful results are also produced when radiometrics are omitted and only the DTM and magnetics are used. The study concludes with a generalised workflow to assist geologists who are seeking to improve the bedrock interpretation of areas under cover in a single area of interest. Domain expertise is shown to be critical for the selection of appropriate input features and validation of results during predictive lithologic mapping.

Observable predictions for massive-neutrino cosmologies with model-independent dark energy

We investigate the bounds on the sum of neutrino masses in a cosmic-acceleration scenario where the equation of state $w(z)$ of dark energy (DE) is constructed in a model-independent way, using a basis of principal components (PCs) that are allowed to cross the phantom barrier $w(z)=-1$. We find that the additional freedom provided to $w(z)$ means the DE can undo changes in the background expansion induced by massive neutrinos at low redshifts. This has two significant consequences: (1) it leads to a substantial increase in the upper bound for the sum of the neutrino masses ($M_{\nu} < 0.33 - 0.55$ eV at the 95\% C.L. depending on the data sets and number of PCs included) compared to studies that choose a specific parametrization for $w(z)$; and (2) it causes $\sim1\sigma$ deviations from $\Lambda$CDM in the luminosity distance and the Hubble expansion rate at higher redshifts ($z \gtrsim 2$), where the contribution of DE is subdominant and there is little constraining data. The second point consequently means that there are also observable deviations in the shear power spectrum and in the matter power spectrum at low redshift, since the clustering of matter throughout cosmic time depends on the expansion rate. This provides a compelling case to pursue high-$z$ BAO and SN measurements as a way of disentangling the effects of neutrinos and dark energy. Finally, we find that the additional freedom given to the dark energy component has the effect of lowering $S_8$ with respect to $\Lambda$CDM.

Principal component analysis as a novel approach for cardiorespiratory exercise testing evaluation

Objective: Our purpose was to apply a principal component analysis (PCA) approach to cardiorespiratory exercise to test evaluation and its sensitivity to workload accumulation. Approach: Twenty-five healthy young adults performed a progressive and maximal cycling test, which was divided into two parts: moderate and high workload intensities, using a ventilatory threshold as a cut point. A PCA of the time series of cardiovascular and respiratory variables was performed in each part and the number of principal components (PCs), the eigenvalues of the first PC (PC1), and the information entropy were calculated. Main results: The number of PCs increased, the eigenvalues of PC1 decreased (t = 5.32; p < 0.001; d = 1.39) and entropy was significantly higher (Z = 3.10; p = .002; d = 1.16) at high workload intensities, compared to moderate intensities. Significance: Results showed the sensitivity of the PCA approach to workload accumulation and corroborates its potential for improving the evaluation and interpretation of cardiorespiratory exercise testing. In particular, it points to being a good candidate to objectively detect qualitative changes or thresholds.