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Abstract Transcriptome data are frequently used to investigate coral bleaching; however, the factors controlling gene expression in natural populations of these species are poorly understood. We studied two corals, Montipora capitata and Pocillopora acuta, that inhabit the sheltered Kāne'ohe Bay, Hawai'i. M. capitata colonies in the bay are outbreeding diploids, whereas P. acuta is a mixture of clonal diploids and triploids. Populations were sampled from six reefs and subjected to either control (no stress), thermal stress, pH stress, or combined pH and thermal stress treatments. RNA‐seq data were generated to test two competing hypotheses: (1) gene expression is largely independent of genotype, reflecting a shared treatment‐driven response (TDE) or, (2) genotype dominates gene expression, regardless of treatment (GDE). Our results strongly support the GDE model, even under severe stress. We suggest that post‐transcriptional processes (e.g., control of translation, protein turnover) modify the signal from the transcriptome, and may underlie the observed differences in coral bleaching sensitivity via the downstream proteome and metabolome.

Abstract Background and Aims The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood. Methods The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the “ComBat” algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score. Results Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune‐associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer‐related and immune‐related pathways such as pathways involving Toll‐like receptors, T‐cell receptors, JAK‐STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer‐relevant signaling pathways, which explain prostate cancer's poor prognosis. Conclusion The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Abstract In order to realize efficient and lightweight digital inspection technology, we propose an improved method to simplification large volumes of scanned point cloud data of aircraft integral panels and facilitate subsequent processing. Fast Point Feature Histogram (FPFH) method is utilized to extract feature information and optimize the Principal Component Analysis (PCA) algorithm to calculate the contribution degree to transform into principal components, yielding PCA‐based FPFH features. Next, based on PCA‐based FPFH features, we classify the point cloud data into non‐feature and feature point clouds and extract the feature point clouds through random downsampling to obtain simplified non‐feature point clouds, extract the feature point clouds to retain their boundary integrity, and downsample the remaining feature point clouds by curvature to obtain the simplified feature point clouds. Finally, we combine the two to obtain the final simplified panel point cloud data within 30 s. To evaluate the simplification effect, we adopt a standardized information entropy‐based point cloud simplification accuracy evaluation method based on the simplification rate. Our method achieves an information entropy of more than 0.95, indicating its effectiveness in simplification point cloud data for efficient and lightweight digital inspection technology.

Abstract As the next generation of green power system, smart grids have gradually enhanced the operation efficiency of power system. Meanwhile, the application of communication and intelligent technologies make the power grid more vulnerable to the emerging cyber‐physical attacks, such as the false data injection attack (FDIA). Particularly, the deception property of the FDIA on the output measurement estimation can fool the current security mechanism without triggering an alarm. Motivated by this problem, this paper aims at developing a novel detection and recovery mechanism against FDIA in smart grid. Based on the established state space grid model derived from the three‐phase sinusoidal voltage equations, an improved principal component analysis (PCA)‐based detection method is proposed. By introducing the mathematical transformation principle method, the detection performance such as detection rate and false positive rate can be improved. To keep the stable running of power system, a genetic optimization algorithm‐based linear quadratic regulator (LQR) defense method is developed. In addition, to improve the response performance to external attacks, an artificial intelligence method named genetic optimization algorithm is introduced to optimize the robust performance of the proposed defense method. Finally, the simulation results on the IEEE 6‐bus and 118‐bus grid system demonstrate the superiority of the proposed genetic algorithm optimization‐based LQR defense method.

Abstract Introduction International guidelines on cancer treatment recommend screening for early detection and treatment of distress. However, screening rates are insufficient. In the present study, a survey was developed to assess perceived systemic barriers to distress screening. Methods A three‐step approach was used for the study. Based on qualitative content analysis of interviews and an expert panel, an initial survey with 53 questions on barriers to screening was designed. It was completed by 98 nurses in a large comprehensive cancer center in Switzerland. From this, a short version of the survey with 24 questions was derived using exploratory principal component analysis. This survey was completed by 150 nurses in four cancer centers in Switzerland. A confirmatory factor analysis was then performed on the shortened version, yielding a final set of 14 questions. Results The initial set of 53 questions was reduced to a set of 14 validated questions retaining 53% of the original variance. These 14 questions allow for an assessment within 2–3 min that identifies relevant barriers to distress screening from the perspective of those responsible for implementation of distress screening. Across several hospitals in Switzerland, the timing of the first distress screening, lack of capacity, patient and staff overload, and refusal of distressed patients to be referred to support services emerged as major problems. Conclusion The validated 14 questions on barriers to screening cancer patients for distress enable clinicians and hospital administrators to quickly identify relevant issues and take action to improve screening programs.

Abstract Functional polymer microspheres have broad application prospects in various fields, such as metal ion detection, adsorption, separation, and controlled drug release. However, integrating different functions in a single microsphere system is a significant challenge in this field. In this work, we prepared multicompartmental emulsion droplets utilizing microfluidic technology. Fe3O4 magnetic nanoparticles were added to one of the compartments of the emulsion droplets as functional particles, and Janus microspheres were obtained after curing. Fluorescent probes enter the two compartments of the Janus microspheres by diffusion. The fluorescence changes of the microspheres were observed in situ and captured through a fluorescence microscope. The images are processed by image recognition software and a Python program. The “fingerprint” of the detected metal ions is obtained by dimensionality reduction of the data through Principal Component Analysis. We employ different algorithms to build Machine Learning models for predicting the metal ion species and concentration. The variation of fluorescence intensity of the three fluorescent probes and the corresponding R, G, and B channel values and time are used as descriptors. The results show that the Random Forest, K‐neighborhood (KNN), and Neural Network models demonstrated a better predicted effect with a variance (R2) greater than 0.9 and a smaller root mean square error; among them, the KNN model predicted the most accurate results.

Abstract Lodging is one of the main problems affecting the maize production. In this study, 220 maize inbred lines were used for determining the lodging resistance. Analysis methods such as the correlation analysis, the principal component analysis, the cluster analysis, the stepwise discriminate analysis, and the ridge regression analysis were deployed for data interpretation. The results of the correlation analysis showed that 17 characters were correlated with varying degrees. Strong and positive correlations between TID and FID (r = 0.910), TIL and FIL (r = 0.898), NSVB and NLVB (r = 0.775), ASVB and ALVB (r = 0.746), and LC and HC (r = 0.656) were observed. The first six principal components explained 79.13% of the phenotypic variation of the 17 characters, with the contribution rates being 20.77%, 18.12%, 14.09%, 11.17%, 8.66%, and 6.32%, respectively. Five categories were clustered in the 220 inbred lines. The result of stepwise discriminate analysis showed that 211 inbred lines were correctly discriminated and the identification rate was 95.91%, and 9 inbred lines were incorrectly discriminated and the identification rate was 4.09%, which meant that the result of cluster analysis was accurate and reliable. The stalk bending strength, ear height, cellulose content, number of small vascular bundles, and cross‐sectional area were selected, and the regression model of lodging resistance of inbred lines was established by using the ridge regression method. Thirty of the 220 inbred lines, including R1656, 4003, and LD61, showed the highest lodging resistance. The results provide a reference for the selection of lodging‐resistant germplasm resources in breeding for the lodging resistance hybrids.

Abstract Anthropogenic activities that degrade natural ecosystems may also impact human health. However, research on the links between human and environmental health has most often been conducted at small scales (e.g. individual cities) and cannot easily be extrapolated to larger scales. We created a macrosystems ecology framework to identify associations between human and environmental health by combining human mortality and socioeconomic data for the conterminous United States with spatially aligned data on the physicochemical characteristics of river basins. Principal component analysis was first used to reduce a list of 596 environmental variables to a subset of 64 environmental covariates, representing six main environmental themes (climate, geology, hydrology, land use, river basin morphology and pollution). Independent, spatially aligned information was then obtained for 12 socioeconomic covariates. Random forest modelling was used to predict age‐adjusted mortality rate as a function of the environmental and socioeconomic covariates. An independent data subset (random 75:25 model building vs. testing split) was also used for model validation. The coefficient of determination between predicted and observed mortality rates was 0.76 for the validation data. Furthermore, model residuals (predicted − observed mortality) were centered near zero and normally distributed (1 SD = 62.26), suggesting high model accuracy and precision. Socioeconomic covariates were consistently the most influential predictors of mortality rate. Smoking, food insecurity, and lack of physical activity were particularly important. However, environmental covariates accounted for 5 of the 10 strongest predictors overall, with air temperature and precipitation being most influential among environmental variables. This proof‐of‐concept study demonstrates the utility of a modelling framework that combines environmental and human health data at macroscales. We suggest that further application of macrosystems ecology tools will improve the capacity to anticipate human health responses to ongoing environmental change. Read the free Plain Language Summary for this article on the Journal blog.

Abstract Ramp phenomena caused by abrupt changes in wind speed may confound the stable operation of correlated electrical power supply systems, yet accurate numerical predictions are challenging, as the wind is affected by complex interactions between large‐scale weather patterns and local geographical conditions. Further, optimal numerical weather prediction (NWP) methods and physics schemes vary as a function of weather patterns. The present study proposed a new real‐time wind power ramp forecast framework based on the flexible selection of optimal NWP models, which were derived via principal component analysis (PCA). The novelty of this analysis lies in that statistical methods were employed for NWP optimization, compared with their more conventional use during an NWP postprocessing. Here, a weather pattern was classified by PCA using outcomes from the global‐scale prediction models, and the optimum regional NWP system settings were acquired according to the weather patterns for further wind field dynamical downscaling. The performance of the developed prediction system was verified with wind power at wind turbine hub‐heights for three areas in eastern Japan, and the Critical Success Index (CSI) indicated an improvement of prediction accuracy over benchmark predictions by ≤0.184 for ramp‐up events and ≤0.127 for ramp‐down events (both observed in Tohoku area). Higher CSI values were consistently seen in three wind farm areas, indicative of the improvement in detection probability for actual ramp events compared with benchmark.

Objective One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source of surgical complications. Accurate recognization of the tissues is the key to increase safety and effect as well as to reduce the complications of spinal surgery. The study aimed at tissue recognition in the spinal operation area based on electrical impedance and the boundaries of electrical impedance between cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus. Methods Two female white swines with body weight of 40 kg were used to expose cortical bone, cancellous bone, spinal cord, muscle, and nucleus pulposus under general anesthesia and aseptic conditions. The electrical impedance of these tissues at 12 frequencies (in the range of 10–100 kHz) was measured by electrochemical analyzer with a specially designed probe, at 22.0–25.0°C and 50%–60% humidity. Two types of tissue recognition models ‐ one combines principal component analysis (PCA) and support vector machine (SVM) and the other combines combines SVM and ensemble learning ‐ were constructed, and the boundaries of electrical impedance of the five tissues at 12 frequencies of current were figured out. Linear correlation, two‐way ANOVA, and paired T‐test were conducted to analyze the relationship between the electrical impedance of different tissues at different frequencies. Results The results suggest that the differences of electrical impedance mainly came from tissue type (p

Abstract The direct preventative detection of flow‐induced atherosclerosis remains a significant challenge, impeding the development of early treatments and prevention measures. This study proposes a method for diagnosing atherosclerosis in the carotid artery using nanometer biomarker measurements through surface‐enhanced Raman spectroscopy (SERS) from single‐drop blood samples. Atherosclerotic acceleration is induced in apolipoprotein E knockout mice which underwent a partial carotid ligation and were fed a high‐fat diet to rapidly induce disturbed flow‐induced atherosclerosis in the left common carotid artery while using the unligated, contralateral right carotid artery as control. The progressive atherosclerosis development of the left carotid artery was verified by micro‐magnetic resonance imaging (micro‐MRI) and histology in comparison to the right carotid artery. Single‐drop blood samples are deposited on chips of gold‐coated ZnO nanorods grown on silicon wafers that filter the nanometer markers and provide strong SERS signals. A diagnostic classifier was established based on principal component analysis (PCA), which separates the resultant spectra into the atherosclerotic and control groups. Scoring based on the principal components enabled the classification of samples into control, mild, and severe atherosclerotic disease. The PCA‐based analysis was validated against an independent test sample and compared against the PCA‐PLS‐DA machine learning algorithm which is known for applicability to Raman diagnosis. The accuracy of the PCA modification‐based diagnostic criteria was 94.5%, and that of the machine learning algorithm 97.5%. Using a mouse model, this study demonstrates that diagnosing and classifying the severity of atherosclerosis is possible using a single blood drop, SERS technology, and machine learning algorithm, indicating the detectability of biomarkers and vascular factors in the blood which correlate with the early stages of atherosclerosis development.

Abstract Evidence is mounting that chronic high‐fructose diets (HFrD) can lead to metabolic abnormalities and cause a variety of diseases. However, the underlying mechanism by which long‐term high fructose intake influencing systemic metabolism remains unclarified. This study, therefore, attempted to investigate the impact of a high‐fructose diet on metabolic profile. Four‐week‐old male C57BL/6 mice were fed with 15% fructose solution as their only source of water for 8 weeks. Afterward, gas chromatography–mass spectrometry (GC–MS) was employed to investigate the comprehensive metabolic profile of serum, muscle, liver, heart, white adipose, brain, and kidney tissues, and multivariate analyses including principal component analysis (PCA) and orthogonal partial least squared‐discriminant analysis (OPLS‐DA) were applied to screen for differential metabolite expression between the HFrD and control groups. Furthermore, the MetaboAnalyst 5.0 (http://www.metaboanalyst.ca) and Kyoto Encyclopedia of Genes and Genomes database (KEGG; http://www.kegg.jp) were employed to portray a detailed metabolic network. This study identified 62 metabolites related to HFrD and 10 disturbed metabolic pathways. The results indicated that high fructose intake mainly influenced amino acid metabolism and biosynthesis (glycine, serine, and threonine metabolism; aspartate, and glutamate metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis, and arginine biosynthesis pathways), glutathione metabolism, sphingolipid metabolism, and glyoxylate and dicarboxylate metabolism in serum, whereas these pathways were suppressed in the brain. Starch and sucrose metabolism in muscle was also disrupted. These results elucidate the effects of long‐term high fructose consumption on the metabolic profiles of various tissues and provide new insight for the identification of potential metabolic biomarkers and pathways disrupted by high fructose.

Abstract Virtual sensing enables estimation of stress in unmeasured locations of a system using a system model, physical sensors and a process model. The system model holds the relationship between the physical sensors and the desired stress response. A process model processes both the physical sensors and the system model to synthesise virtual sensors that ‘measure’ the desired stress response. Thus, virtual sensing enables mapping between the physical sensors (input) and the desired stress response (output). The system model is a mathematical model of the system based on knowledge or data of the system. Here, the data‐driven system model is constructed directly on data analyses for the specific system. In this paper, supervised learning and data‐driven system models are applied to strain estimation of an offshore wind turbine in the dynamic range through a novel use of principal component analysis (PCA); 40 min of training data is used to establish the data‐driven system model that can estimate the dynamic strain response with high precision for 2 months, while the estimated fatigue damage averaged out to −1.76% of the measured strain response.

Abstract Sit‐to‐stand (STS) movement is a frequently performed activity of daily living. The analysis of STS movement can lead to the early detection of a decline in physical functioning. There is thus interest in home‐based STS monitoring systems. The present study (1) extracted universal differences by age group in common STS movements of daily life and (2) clarified where the differences between age groups lie in the sequence of movements. We experimented to measure the three patterns of STS movement assumed to be performed in living spaces. Ten young adults and seventeen elderly people participated. Data were acquired by an Azure Kinect DK, an RGB‐D camera, and converted into angles and angular velocities of the major body segments. Principal component analysis was applied to the overall time series data. From the synthesized principal component space, principal components with significant differences between the elderly and young‐adult groups were extracted, and the movements of the elderly participants were evaluated. We found parameters and movement strategies that show the characteristics of each age group for each movement pattern. The results of this study can contribute to the design of home‐based STS monitoring systems.

Abstract Background Foot morphology is associated with altered loading of the ankle–foot complex in adolescent footballers, predisposing to pain and injury. However, usual singular plane clinical assessments do not accurately capture the 3D nature of foot morphology. A new approach is 3D laser scanning, with statistical shape model techniques creating individual‐to‐group comparison. However, no research exists on the adolescent, football‐playing foot. Furthermore, a link between 3D foot morphology, and usual clinical and performance measures would be beneficial for practical implementation. Methods Four hundred forty‐seven 3D foot scans from 224 elite male footballers (U12‐U19) in bilateral stance were collected and further processed with statistical shape model techniques. Weighted shape parameters for individual principal components (Modes) were extracted for each foot. Centre of pressure displacement expressed as total travelled way in millimetres was calculated for bilateral and unilateral postural stability measures. Clinical assessments (Clarke's Angle, Resting Calcaneal Stance Position) were calculated on the 3D foot scans. Differences in weighted shape parameters, postural stability measures, and clinical assessments between age groups were determined by ANOVA. Correlations determined the relationship of Modes and clinical assessments to postural stability measures. Linear regression established if clinical assessments predicted the mode describing foot arch variation. Results Age groups significantly differed for Mode 1 (foot length), Mode 2 (foot arch), and Mode 5 (tibial rotation relative to the foot) (p

Abstract Machine learning methods can be used as robust techniques to provide invaluable information for analyzing biological samples in pharmaceutical industries, such as predicting the concentration of viral particles of interest in biological samples. Here, we utilized both convolutional neural networks (CNNs) and random forests (RFs) to predict the concentration of the samples containing measles, mumps, rubella, and varicella‐zoster viruses (ProQuad®) based on Raman and absorption spectroscopy. We prepared Raman and absorption spectra data sets with known concentration values, then used the Raman and absorption signals individually and together to train RFs and CNNs. We demonstrated that both RFs and CNNs can make predictions with R2 values as high as 95%. We proposed two different networks to jointly use the Raman and absorption spectra, where our results demonstrated that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. Additionally, we further verified the advantage of using joint Raman‐absorption with principal component analysis. Furthermore, our method can be extended to characterize properties other than concentration, such as the type of viral particles.

Analyzing longitudinal data in health studies is challenging due to sparse and error-prone measurements, strong within-individual correlation, missing data and various trajectory shapes. While mixed-effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non-parametric approach developed for regular and dense functional data that flexibly describes temporal trajectories at a potentially lower computational cost. This article presents an empirical simulation study evaluating the behavior of FPCA with sparse and error-prone repeated measures and its robustness under different missing data schemes in comparison with MM. The results show that FPCA is well-suited in the presence of missing at random data caused by dropout, except in scenarios involving most frequent and systematic dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA was applied to describe the trajectories of four cognitive functions before clinical dementia and contrast them with those of matched controls in a case-control study nested in a population-based aging cohort. The average cognitive declines of future dementia cases showed a sudden divergence from those of their matched controls with a sharp acceleration 5 to 2.5 years prior to diagnosis., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named principal component mediation analysis (PCMA). Likelihood-based estimators are introduced for simultaneous estimation of the component projections and effect parameters. The asymptotic distribution of the estimators is derived for low-dimensional data. A bootstrap procedure is introduced for inference. Simulation studies illustrate the superior performance of the proposed approach. Applied to a proteomics-imaging dataset from the Alzheimer's disease neuroimaging initiative (ADNI), the proposed framework identifies protein deposition - brain atrophy - memory deficit mechanisms consistent with existing knowledge and suggests potential AD pathology by integrating data collected from different modalities., (© 2024 John Wiley & Sons Ltd.)

Sensor devices, such as accelerometers, are widely used for measuring physical activity (PA). These devices provide outputs at fine granularity (e.g., 10-100 Hz or minute-level), which while providing rich data on activity patterns, also pose computational challenges with multilevel densely sampled data, resulting in PA records that are measured continuously across multiple days and visits. On the other hand, a scalar health outcome (e.g., BMI) is usually observed only at the individual or visit level. This leads to a discrepancy in numbers of nested levels between the predictors (PA) and outcomes, raising analytic challenges. To address this issue, we proposed a multilevel longitudinal functional principal component analysis (mLFPCA) model to directly model multilevel functional PA inputs in a longitudinal study, and then implemented a longitudinal functional principal component regression (FPCR) to explore the association between PA and obesity-related health outcomes. Additionally, we conducted a comprehensive simulation study to examine the impact of imbalanced multilevel data on both mLFPCA and FPCR performance and offer guidelines for selecting optimal methods., (© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)

Proton magnetic resonance spectroscopic imaging ( 1 H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in 1 H-MRSI has led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, Marchenko-Pastur principal component analysis (MP-PCA) based denoising and low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo 1 H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower-concentration metabolites. Our study provided a framework for how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical 1 H-FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low-concentration metabolites., (© 2024 The Author(s). NMR in Biomedicine published by John Wiley & Sons Ltd.)

Background: This study aims to accelerate revision surgery and treatment using X-ray imaging and deep learning to identify shoulder implant manufacturers in advance., Methods: A feature engineering approach based on principal component analysis and a k-means algorithm was used to cluster shoulder implant data. In addition, a pre-trained DenseNet201 combined with a capsule network (DenseNet201-Caps) shoulder implant classification model was proposed., Results: DenseNet201-Caps was the most effective classification model on the clustered dataset with an accuracy of 94.25% and an F1 score of 96.30%. Notably, clustering the dataset in advance improved the accuracy and the Caps implementations successfully enhanced the performance of all convolutional neural network models. The analysed results indicate that DenseNet201-Caps struggled to distinguish between the Cofield and Depuy manufacturers. Hence, a multistage classification approach was developed with an improved accuracy of 96.55% achieved., Conclusions: The DenseNet201-Caps method enables the accurate identification of shoulder implant manufacturers., (© 2024 John Wiley & Sons Ltd.)

Introduction: Compound annotation is always a challenging step in metabolomics studies. The molecular networking strategy has been developed recently to organize the relationship between compounds as a network based on their tandem mass (MS2) spectra similarity, which can be used to improve compound annotation in metabolomics analysis., Objective: This study used Bupleuri Radix from different geographic areas to evaluate the performance of molecular networking strategy for compound annotation in liquid chromatography-mass spectrometry (LC-MS)-based metabolomics., Methodology: The Bupleuri Radix extract was analyzed by LC-quadrupole time-of-flight MS under MSe acquisition mode. After raw data preprocessing, the resulting dataset was used for statistical analysis, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The chemical makers related to the sample growth place were selected using variable importance in projection (VIP) > 2, fold change (FC) > 2, and p < 0.05. The molecular networking analysis was applied to conduct the compound annotation., Results: The score plots of PCA showed that the samples were classified into two clusters depending on their growth place. Then, the PLS-DA model was constructed to explore the chemical changes of the samples further. Sixteen compounds were selected as chemical makers and tentatively annotated by the feature-based molecular networking (FBMN) analysis., Conclusion: The results showed that the molecular networking method fully exploits the MS information and is a promising tool for facilitating compound annotation in metabolomics studies. However, the software used for feature extraction influenced the results of library searching and molecular network construction, which need to be taken into account in future studies., (© 2024 John Wiley & Sons Ltd.)

Introduction: Cordyceps sinensis (CS) is a precious medicinal fungus. Wild CS (WCS) and artificial CS (ACS) are destroyed for their identification using traditional methods, which are time consuming and labor-intensive. Therefore, it is crucial to establish a nondestructive identification method to rapidly screen WCS., Objective: The aim of this study was to provide technical support for rapid screening of CS and evaluation of its quality. The applicability of the model was improved through model transfer., Methods: In this study, continuous wavelet transform was used to analyze the differences in moisture content and active components between WCS and ACS from the perspective of characteristic molecular groups. A portable instrument and a laboratory benchtop instrument were used to determine CS spectra. Partial least squares discrimination analysis was conducted for the identification of WCS and ACS while preserving the original shape of CS. Moreover, improved principal component analysis was utilized to transfer the model between the two types of near-infrared spectroscopy (NIRS) instruments., Results: The results demonstrated that three peaks, at 1443, 1941, and 2183 nm, were characteristic absorption peaks. The model based on NIRS could initially provide rapid differentiation between WCS and ACS. At the same time, the accuracy of the external test set was further improved to over 95% through forward transfer., Conclusion: Therefore, this method could be used for rapid screening of WCS and provides technical support for the nondestructive identification of CS and initial assessment of CS quality., (© 2023 John Wiley & Sons, Ltd.)

Statistical shape modeling (SSM) offers the potential to describe the morphological differences in similar shapes using a compact number of variables. Its application in orthopedics is rapidly growing. In this study, an SSM of the intramedullary canal of the proximal femur was built, with the aim to better understanding the complexity of its shape which may, in turn, enhance the preoperative planning of total hip arthroplasty (THA). This includes the prediction of the prosthetic femoral version (PFV) which is known to be highly variable amongst patients who have undergone THA. The model was built on three dimensional (3D) models of 64 femoral canals which were generated from pelvic computed tomography images including the proximal femur in the field of view. Principal component analysis (PCA) was performed on the mean shape derived from the model and each segmented canal. Five prominent modes of variations representing approximately 84% of the total 3D variations in the population of shapes were found to capture variability in size, proximal torsion, intramedullary femoral anteversion, varus/valgus orientation, and distal femoral shaft twist/torsion, respectively. It was established that the intramedullary femoral canal is highly variable in its size, shape, and orientation between different subjects. PCA-driven SSM is beneficial for identifying patterns and extracting valuable features of the femoral canal., (© 2024 The Author(s). Journal of Orthopaedic Research® published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.)

Abstract With the increasing deployment of wide‐area monitoring systems (WAMS) and phasor measurement units (PMUs), along with artificial neural network (ANN) and high‐performance distributed computation technique for smart grid and smart metering environment, online dynamic security assessment (DSA) plays a key role for early unstable event detection on power system security. It is especially important at a post‐fault operation that the timing by DSA to detect an unstable event is critical to emergency remedial control action. However, excessive update training is one of the constraints for ANN to be effectively performed at pre‐fault and post‐fault operations on online DSA. This paper describes how transfer learning is successfully employed to shorten the training time for online DSA. It also helps to improve the validation accuracy if the training dataset of scratch ANN model is insufficient. Besides, a new approach of using the densely connected convolutional network with kernel principal component analysis (KPCA) is proposed to eliminate the traditional step of dimensionality reduction.

Abstract Algae contamination accumulated on the surface of insulators will affect the electrical properties of insulators. At present, the characterization method of algae contamination is the image method, which determines the distribution of algae contamination through colour. The detection results are affected by environmental changes and the growth state of algae. This paper puts forward a method of detecting polluted insulator surface with algae by using laser‐induced breakdown spectroscopy (LIBS) technique, combined with the scanning electron microscope (SEM), inductively coupled plasma emission spectrometer (ICP), analyzed by the spectra of algae contamination. Mg, Ca, Fe are selected as the analytical element. The classification of algae contamination, non‐algae contamination and no contamination are realized with the principal component analysis (PCA). The research results are of great significance for characterization of algal pollution on insulator surface by LIBS.

In‐memory computing (IMC) with crosspoint arrays of resistive switching memory (RRAM) has gained wide attention for accelerating machine learning, data analysis, and deep neural networks. By IMC, matrix‐vector multiplication (MVM) can be executed in the crosspoint array in just one step, thus accelerating a broad range of tasks in machine learning and data analytics. However, a key issue for RRAM crosspoint arrays is the forming operation of the memories which limits the stability and accuracy of the conductance state in the memory device. In this work, a hardware implementation of crosspoint array of forming‐free devices for fast, energy‐efficient accelerators of MVM is reported. RRAM devices with a 1.5 nm‐thick HfO2 layer show an initial low resistance without forming and an analogue‐mode programming behavior for high‐accuracy IMC. Accurate hardware MVM is demonstrated by experimental eigenvalue/eigenvector calculation according to the power‐iteration algorithm, with a fast convergence within about ten iterations to the correct solution. Deflation technique and principal component analysis (PCA) enable the classification of the Iris dataset with 98% accuracy compared with floating‐point implementation. These results support forming‐free crosspoint arrays for accelerating advanced machine learning with IMC.

Abstract This study seeks to assess whether the German bilateral climate finance (GCF) geared toward the agriculture sector of 35 SSA countries is helping achieve food security. In measuring food security, the study employed principal component analysis to generate an index for all four food security dimensions proposed by FAOSTAT‐food availability, access, stability, and utilization. A general composite food security index was also generated. System‐GMM was used to estimate the influence of GCF on all the food security components. The findings show that GCF improves food availability and general food security in SSA, but government expenditure is inadequate to do so. It was also found that agricultural technology or capacity moderates the effect of GCF on food availability and overall food security. Based on the findings of the study, it is clear that bilateral climate finance flow invested in agricultural technology coupled with an increase in government agricultural expenditure from the current 6% to 10% as proposed by the Malabo declaration will put SSA on a path to achieving food security. It is recommended that SSA countries deepen incentives to attract FDI inflows to the region due to its role in improving food security via food access.

Abstract While various frameworks for iris recognition have been proposed, most lack efficiency and high speed. A new framework for iris recognition is presented that is both efficient and fast. Feature extraction is performed by extracting Gabor features and then applying supervised locality‐preserving projections with heat kernel weights, which improves the recognition rate in comparison with the results from unsupervised dimensionality reduction techniques such as principal component analysis, locality‐preserving projections, and random projections. Afterwards, a classification is performed using the recently proposed sparse representation‐based classification (SRC). To considerably improve classification performance, SRC is proposed, using a greedy compressed‐sensing recovery algorithm, as opposed to employing the traditional computationally expensive ℓ1 minimisation. The proposed framework achieves a recognition rate of about 99.5% using two iris databases, with a significant improvement in speed over related frameworks.

Objective To screen serum microRNAs (miRNAs) which could discriminate fracture status in postmenopausal women with or without diabetes. Methods The miRNA expression profile dataset GSE70318 was downloaded from Gene Expression Omnibus (GEO) database. This dataset composed of 74 samples, among these, 55 postmenopausal women was selected for bioinformatics analysis, including 19 osteoporotic fracture patients with type‐2 diabetes, 19 osteoporotic fracture patients without type‐2 diabetes, and 17 healthy control subjects. These samples were divided into two groups: fracture patients with diabetes vs healthy subjects (FH group) and fracture patients without diabetes vs healthy subjects (DFH group). Then, the differentially expressed miRNA (DEMs) in FH group and DFH group were respectively identified. The target genes of DEMs were predicted, followed by functional enrichment analysis. Furthermore, DEMs related to long non‐coding RNAs (lncRNAs) were screened, and DEMs‐lncRNA‐target genes network was constructed. Subsequently, principal component analysis (PCA) of DEMs was performed to further explore the expression characteristics of the selected miRNAs in different types of fracture samples. Finally, the expression level of significant DEMs was calculated by quantitative real‐time polymerase chain reaction (qPCR) to verify the accuracy of the results of bioinformatics analysis. Results A total of 18 and 23 DEMs were identified in FH and DFH groups, respectively. Gene ontology (GO) analysis showed that genes in FH group were significantly enriched in regulation of transcription (GO: 0045449) and genes in DFH group were mainly enriched in cellular response to hormone stimulus (GO: 0032870). Meanwhile, pathway analysis indicated that genes in FH group were primarily enriched in T cell receptor signaling pathway (hsa04660) and genes in DFH group were mainly implicated in neurotrophin‐signaling pathway (hsa04722). Moreover, the miRNA‐lncRNA analysis revealed that miR‐155‐5p regulated by lncRNA MIR155HG was up‐regulated in FH group; in addition, the miR‐181c was significantly up‐regulated and miR‐375 was observably down‐regulated in DFH group. Furthermore, PCA analysis suggested that the screened miRNAs were able to differentiate these two types of fractures in postmenopausal women. The miR‐181c and miR‐375 might be regarded as potential predictors for fracture, while miR‐155‐5p might be a candidate diagnostic biomarker for diabetic fracture. Finally, the results of qPCR were consistent with that of microarray data. Conclusions Overall, these three miRNAs might be regarded as potential diagnostic biomarkers to discriminate fracture status in postmenopausal women with and or without diabetes, and they served a putative role in the pathogenesis of these two diseases. However, these findings were only observed in serum samples and further clinical trials are urgently demanded to validate our results.

Abstract Aims There are only a few studies on novel biomarkers for incident heart failure (HF). We investigated the association of multiple circulating biomarkers with incident HF in a large prospective population‐based study. Methods and results Conventional risk factors and inflammatory biomarkers were measured, and systemic metabolic measures determined by a high‐throughput serum nuclear magnetic resonance platform in a population‐based Metabolic Syndrome in Men study including 10 106 Finnish men without HF at baseline. During an 8.8 year follow‐up, 172 (1.7%) participants developed HF. Adiponectin, high‐sensitivity C‐reactive protein (hs‐CRP), glycoprotein acetyls, alanine, phenylalanine, glycerol, and pyruvate were associated with incident HF in unadjusted Cox regression analyses, in addition to age, systolic blood pressure, body mass index (BMI), waist circumference, fasting plasma glucose and insulin, haemoglobin A1c (HbA1c), and urinary albumin excretion rate (UAER). After adjustment for age, BMI, diabetes, and statin medication, only adiponectin [hazard ratio (HR) 1.18 (1.10–1.26, P = 4.1E‐08)], pyruvate [HR 1.38 (1.28–1.50, P = 8.2E‐05)], and UAER [HR 1.15 (1.11—1.18, P = 7.8E‐06)] remained statistically significant. In principal component analysis of biomarkers associated with HF in univariate Cox regression analysis, we identified six components, explaining 61.7% of total variance. Four principal components, one with significant loadings on waist, BMI, fasting plasma insulin, interleukin 1 receptor antagonist, and hs‐CRP; another on pyruvate, glycoprotein acetyls, alanine, glycerol and HbA1c; third on age and glomerular filtration rate; and fourth on systolic blood pressure, UAER, and adiponectin, significantly associated with incident HF. Conclusions Several novel metabolic and inflammatory biomarkers were associated with incident HF, suggesting early activation of respective pathways in the pathogenesis of HF.

Abstract In the process of synthetic aperture radar image noise suppression by the bi‐dimensional empirical mode decomposition (BEMD) algorithm, the edge effect is a key problem in the BEMD operation. To weaken this effect, an improved BEMD‐kernel principal component analysis (BEMD‐KPCA) method of image denoising is proposed in this study. Experimental results show that the BEMDKPCA algorithm has a good capability of improving edge effects in the BEMD decomposition process and satisfying the requirement of the reliable decomposition results. Compared with the traditional BEMD method, the proposed approach has a good effect on suppressing speckle noise. Additionally, the denoised image from the decomposed components of the IMFs processed by the BEMD‐KPCA method sufficiently preserves the edge and detail information, confirming its high coherency with the original image.

Abstract Hemodynamic instability is frequently present in critically ill patients, primarily caused by a decreased preload, contractility, and/or afterload. We hypothesized that peripheral arterial blood pressure waveforms allow to differentiate between these underlying causes. In this in‐silico experimental study, a computational cardiovascular model was used to simulate hemodynamic instability by decreasing blood volume, left ventricular contractility or systemic vascular resistance, and additionally adaptive and compensatory mechanisms. From the arterial pressure waveforms, 45 features describing the morphology were discerned and a sensitivity analysis and principal component analysis were performed, to quantitatively investigate their discriminative power. During hemodynamic instability, the arterial waveform morphology changed distinctively, for example, the slope of the systolic upstroke having a sensitivity of 2.02 for reduced preload, 0.80 for reduced contractility, and −0.02 for reduced afterload. It was possible to differentiate between the three underlying causes based on the derived features, as demonstrated by the first two principal components explaining 99% of the variance in waveforms. The features with a high correlation coefficient (>0.25) to these principal components are describing the systolic up‐ and downstroke, and the anacrotic and dicrotic notches of the waveforms. In this study, characteristic peripheral arterial waveform morphologies were identified that allow differentiation between deficits in preload, contractility, and afterload causing hemodynamic instability. These findings are confined to an in silico simulation and warrant further experimental and clinical research in order to prove clinical usability in daily practice.

Purpose: To develop a SNR enhancement method for CEST imaging using a denoising convolutional autoencoder (DCAE) and compare its performance with state-of-the-art denoising methods., Method: The DCAE-CEST model encompasses an encoder and a decoder network. The encoder learns features from the input CEST Z-spectrum via a series of one-dimensional convolutions, nonlinearity applications, and pooling. Subsequently, the decoder reconstructs an output denoised Z-spectrum using a series of up-sampling and convolution layers. The DCAE-CEST model underwent multistage training in an environment constrained by Kullback-Leibler divergence, while ensuring data adaptability through context learning using Principal Component Analysis-processed Z-spectrum as a reference. The model was trained using simulated Z-spectra, and its performance was evaluated using both simulated data and in vivo data from an animal tumor model. Maps of amide proton transfer (APT) and nuclear Overhauser enhancement (NOE) effects were quantified using the multiple-pool Lorentzian fit, along with an apparent exchange-dependent relaxation metric., Results: In digital phantom experiments, the DCAE-CEST method exhibited superior performance, surpassing existing denoising techniques, as indicated by the peak SNR and Structural Similarity Index. Additionally, in vivo data further confirm the effectiveness of the DCAE-CEST in denoising the APT and NOE maps when compared with other methods. Although no significant difference was observed in APT between tumors and normal tissues, there was a significant difference in NOE, consistent with previous findings., Conclusion: The DCAE-CEST can learn the most important features of the CEST Z-spectrum and provide the most effective denoising solution compared with other methods., (© 2024 International Society for Magnetic Resonance in Medicine.)

Abstract In recent years, China has promoted many new energy projects in order to meet the growing demand for electricity. Therefore, China's offshore wind power installed capacity has grown rapidly. China has a long coastline and abundant offshore wind energy resources. Offshore wind power is an important area for the development of renewable energy, which can promote wind power technology advancement and energy structure adjustment. Therefore, conducting effective research and forecast on the cumulative installed capacity of China's offshore wind power will help the government to rationally deploy and reduce the risk of investment in offshore wind power. In order to accurately predict the future prospects of offshore wind power in China, this paper firstly constructed a set of influencing factors and used gray correlation analysis to screen the main influencing factors. Then, this paper proposed a novel forecasting model named e‐VMD‐PCA‐RELM. The algorithm is based on the traditional RELM (robust extreme learning machine) algorithm, which effectively processes the noise information through the PCA (principal component analysis) algorithm, and extracted the feature elements of the RELM hidden layer to reduce the information redundancy. At the same time, the e‐VMD (variational mode decomposition optimized by entropy) algorithm is used to decompose the original time series to obtain multiple components. By comparing with the other forecasting algorithms, it is proved that the proposed forecasting model has strong generalization ability and has achieved good prediction result. Finally, the e‐VMD‐PCA‐RELM model is used to predict the scale of offshore wind farms in China from 2019 to 2035. We find that the cumulative installed capacity of China's offshore wind power will exceed 60 GW in 2035, and the installed capacity will increase year by year. In 2030, there will be a large increase, with a relative growth rate of 20%.

Abstract Background Measurements of plantar loading reveal foot-to-floor interaction during activity, but information on bone architecture cannot be derived. Recently, cone-beam computer tomography (CBCT) has given visual access to skeletal structures in weight-bearing. The combination of the two measures has the potential to improve clinical understanding and prevention of diabetic foot ulcers. This study explores the correlations between static 3D bone alignment and dynamic plantar loading. Methods Sixteen patients with diabetes were enrolled (group ALL): 15 type 1 with (N, 7) and without (D, 8) diabetic neuropathy, and 1 with latent autoimmune diabetes. CBCT foot scans were taken in single-leg upright posture. 3D bone models were obtained by image segmentation and aligned in a foot anatomical reference frame. Absolute inclination and relative orientation angles and heights of the bones were calculated. Pressure patterns were also acquired during barefoot level walking at self-selected speed, from which regional peak pressure and absolute and normalised pressure-time integral were worked out at hallux and at first, central and fifth metatarsals (LOAD variables) as averaged over five trials. Correlations with 3D alignments were searched also with arch index, contact time, age, BMI, years of disease and a neuropathy-related variable. Results Lateral and 3D angles showed the highest percentage of significant (p

Abstract Lemon is a fruit with delicious taste and attractive flavor, which has been widely accepted by people over the world. Dry lemon slice is a classical processed product of lemon. It is readily to be browning during storage. Which chemicals are responsible for the browning behavior remains unclear. Therefore, in this work, nuclear magnetic resonance spectroscopy is used to analyze the metabolites changes in dry lemon slice during storage. Nineteen metabolites were identified and their related levels were statistically calculated by principal component analysis. The first principal components were represented by glutamic acid, α‐amino‐butyric acid, γ‐amino‐butyric acid, alanine, limonoid, citric acid, glutamine, and phosphocholine. The changes of browning degree during storage and its correlations with the levels of polyphenol, ascorbic acid, amino acid nitrogen, and 5‐hydroxymethylfurfural were analyzed. The results indicated that Maillard reaction and oxidation of phenolics were two main processes contributing to the nonenzymatic browning during storage.

Abstract Litter is essential to promote nutrient cycling and maintain the sustainability of forest resources. However, its variability has not been sufficiently studied at the local scale. The prediction of litter amount using ordinary cokriging with Pearson correlation analysis (COKP) and ordinary cokriging with principal component analysis (COKPCA) was compared with that using ordinary kriging (OK) based on cross‐validation at the local scale of a 1‐ha plot over natural spruce–fir mixed forest in Jilin Province, China. Litter samples in semidecomposed (F) and complete decomposed (H) horizons were collected using an equidistant grid point sampling of 10 m × 10 m. Pearson correlation analysis and principal component analysis (PCA) were used to confirm auxiliary variables. The results showed that the amount of litter was 19.65 t/ha in the F horizon and 10.37 t/ha in the H horizon. The spatial structure variance ratio in the H horizon was smaller than that in the F horizon, indicative of its stronger spatial autocorrelation. Spatial distributions of litter amount in both horizons exhibited a patchy and heterogeneous pattern. Of the selected stand characteristics and litter properties, litter moisture content indicated the strongest relationship with litter amount. Cross‐validation revealed that COKPCA using the comprehensive score as an auxiliary variable produced the most accurate map. The average standard error and root‐mean‐square error between the predicted and measured values were always smaller, the mean error and mean standardized error were much closer to 0, and the root‐mean‐square standardized error was closer to 1 than COKP using litter moisture and OK. Therefore, a clear advantage of cokriging based on principal component analysis was observed and COKPCA was found to be a very useful approach for further interpolation prediction.

Abstract Ocean anoxia was an important kill mechanism in the end‐Permian mass extinction and uranium isotope data are among the most powerful tools for quantifying the global extent and duration of ocean deoxygenation due to the dependence of uranium isotope fractionation on bottom‐water redox conditions. Although coherent stratigraphic variation in uranium isotope ratios (δ238U) and uranium concentrations ([U]) indicative of prolonged deoxygenation beginning coincident with the extinction is well established, the precise extent of anoxia and associated uncertainty have yet to be quantified. Uncertainty arises from both noise in the data and imprecise knowledge of key parameters within the uranium cycle. In this study, we use the Monte Carlo method to explore a range of scenarios and their implications for the uranium cycle across the Permian‐Triassic boundary and through the first 1.7 million years of the Triassic. We then compare model predictions against measured data using principal component analysis to identify model runs and associated parameter values most compatible with trends in the observed data. The best‐fitting models indicate a pronounced increase in the extent of seafloor anoxia across the Permian/Triassic transition, reaching 18% of the seafloor (95% CI: [11%, 47%]), lasting anywhere from 20 kyr to 1.2 Myr. There is an inverse relationship between the extent and duration of anoxia in the set of best‐fitting models. This initial pulse of pronounced anoxia is followed by a prolonged aftermath, which continues through the remainder of the study interval, of less extensive, yet still expanded, anoxia covering 7.8% of the seafloor (95% CI: [1.6%, 48.9%]). Both expanded and protracted anoxia are required to fit existing data, with no indication of full re‐oxygenation during the study interval.

Abstract Introduction The implementation of spatial‐covariance [18F]fluorodeoxyglucose positron emission tomography–based disease‐related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND‐PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores. Methods Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined. Results Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences. Discussion Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.

Seventy-eight parameters are theoretically needed to describe the relative position and orientation of all the 14 bones in the foot and ankle with respect to a reference bone (foot posture). However, articular contacts and soft tissues introduce kinematic coupling, reducing the number of the foot degrees-of-freedom (DOF). This study aims at providing quantification and definition of these couplings. The foot posture was measured in vitro through a series of computed tomography scans, spanning the whole range of foot dorsi/plantar flexion and pronation/supination, also considering the effect of weightbearing. The envelope of foot postures was investigated by means of principal component analysis. The foot and ankle motion were well described with four principal sets of kinematic couplings, that is, synergies. One synergy covers the independent motion of the ankle, while three synergies describe the foot motion. The first foot synergy shows all the bones rotating approximatively about a common axis, mapping the foot abduction/adduction about the Chopart joint. The second foot synergy results in a spherical motion, whose center is located between lateral cuneiform and navicular bone, mapping the foot pronation/supination. The third foot synergy maps the opening of the foot arches during the load acceptance. The foot and ankle complex can thus be described as a four DOF system, whose motion is the result of the linear combination of four synergies. Significance: Synergies reveal the contribution of each bone to the three-dimensional foot posture, providing a compact representation of the motion of the foot and ankle complex, improving the comprehension of its physiology., (© 2024 Orthopaedic Research Society.)

Introduction: Polygala fallax Hemsl (PFH) is a widely used herbal medicine in Guangxi, China. At present, research on PFH mainly focuses on extraction technology and cultivation, lacking quality control standards for systematic evaluation., Objectives: The study aimed to assess the quality of PFH from different sources and to predict markers that would help assess quality., Methods: Fingerprinting of 15 batches of PFH samples was performed by ultra-high performance liquid chromatography (UPLC) and similarity was assessed using hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discrimination (OPLS-DA). Differential components were screened by mathematical analysis, and a "component-target-pathway" network map was constructed in combination with network pharmacology, quality markers (Q-markers) of PFH were predicted, and quantitative analysis was performed., Results: Fifteen batches were fingerprinted for PFH, with 11 common peaks, and peak 5 was identified as 4-hydroxybenzoic acid, which was generally consistent with the results of HCA, PCA, and OPLS-DA. Network pharmacology screened 18 potential compounds, 45 core targets, and 20 key pathways, integrating fingerprinting, pattern recognition, and network pharmacology methods. One of the potential Q-markers that can identify the principle of testability, efficacy, and specificity is 4-hydroxybenzoic acid, whose content ranges from 0.0188 to 1.4517 mg/g., Conclusion: The potential Q-markers of PFH were predicted by integrating fingerprinting, pattern recognition, and network pharmacological analysis, which provided a scientific basis for the overall control and evaluation of the quality of PFH and a theoretical reference for the study of the quality standard of multi-base traditional Chinese medicine., (© 2024 John Wiley & Sons Ltd.)

The shape of the talus, its internal structure, and its mechanical properties are important in determining talar behavior during loading, which may be significant for the design of surgical tools and implants. Although recent studies using statistical shape modeling have described quantitative talar external shape variation, no similar quantitative study exists to describe the density distribution of internal talar structure. The goal of this study is to quantify statistical variation in talar shape and density to benefit the design of talar implants. To this end, weight-bearing computed tomography (CT) scans of the ankle were collected in neutral, bilateral standing posture, and three-dimensional models were generated for each talus. Local density derived from the Hounsfield unit of each CT voxel was extracted. A weighted spherical harmonic analysis was performed to quantify the talar external shape. One hundred and seventy-nine volumes of interest were placed in the same relative position within each talus to quantify the talar density. Additionally, a finite element analysis (FEA) was conducted on a talus with both heterogeneous and homogeneous material properties to observe the effect of these properties on the stress and strain response. Significant differences were found in the talar density in sex and age, as well as in the stress and strain response between homogeneous and heterogeneous FEA. These differences show the importance of considering heterogeneity when examining the load response of tarsal bones., (© 2024 Orthopaedic Research Society.)

Background and Objective: Glycyrrhiza glabra L. (GG) and Strychnos nux-vomica L. (NV) are traditional Chinese medicines (TCMs). Changes in the chemical composition may occur before and after the GG-NV compatibility. Ultra-performance liquid chromatography Q-exactive Orbitrap mass spectrometry (UPLC-QE-Orbitrap-MS) was applied here to study the difference in the components of the GG and NV decoctions before and after they were combined. The changes in the chemical composition of GG and NV before and after the combination were determined., Methods: The precise molecular weight, retention time, and fragment ion peak of the different components of the decoctions before and after compatibility were obtained through UPLC-QE-Orbitrap-MS. Differential analysis methods, such as principal component analysis, were used for comparison., Results: In the positive ion mode, 200 new components were added, whereas six components were lost. In the negative ion mode, 144 new compounds were identified, whereas three components were missing., Conclusions: The compatibility difference between GG and NV was studied through UPLC-QE-Orbitrap-MS. The chemical composition of GG and NV changed before and after compatibility, and a class of compounds different from GG and NV was identified in the co-decoction. This study provides an experimental basis for subsequent research into detoxification mechanisms of the GG-NV combination and offers a new analytical method for investigating the compatibility of various other TCM pairs., (© 2024 John Wiley & Sons Ltd.)

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain., (© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)

Introduction: Artemisia argyi Folium (AAF) is a traditional medicinal herb and edible plant. Analyzing the differential metabolites that affect the efficacy of AAF with different aging years is necessary., Objective: The aim of the study was to investigate the changing trend and differential markers of volatile and nonvolatile metabolites of AAF from different aging years, which are necessary for application in clinical medicine., Methodology: Metabolites were analyzed using a widely targeted metabolomic approach based on ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and gas chromatography tandem mass spectrometry (GC-MS)., Results: A total of 153 volatile metabolites and 159 nonvolatile metabolites were identified. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) could clearly distinguish AAF aged for 1 year (AF-1), 3 years (AF-3), and 5 years (AF-5). Seven flavonoids and nine terpenoids were identified as biomarkers for tracking the aging years., Conclusions: The metabolomic method provided an effective strategy for tracking and identifying biomarkers of AAF from different aging years. This study laid the foundation for analysis of the biological activity of Artemisia argyi with different aging years., (© 2024 John Wiley & Sons Ltd.)

Objective: Rheumatoid arthritis (RA) is an autoimmune disease in which the joint lining or synovium becomes highly inflamed and majorly contributes to disease progression. Understanding pathogenic processes in RA synovium is critical for identifying therapeutic targets. We performed laser capture microscopy (LCM) followed by RNA sequencing (LCM-RNAseq) to study regional transcriptomes throughout RA synovium., Methods: Synovial lining, sublining, and vessel samples were captured by LCM from seven patients with RA and seven patients with osteoarthritis (OA). RNAseq was performed on RNA extracted from captured tissue. Principal component analysis was performed on the sample set by disease state. Differential expression analysis was performed between disease states based on log 2 fold change and q value parameters. Pathway analysis was performed using the Reactome Pathway Database on differentially expressed genes among disease states. Significantly enriched pathways in each synovial region were selected based on the false discovery rate., Results: RA and OA transcriptomes were distinguishable by principal component analysis. Pairwise comparisons of synovial lining, sublining, and vessel samples between RA and OA revealed substantial differences in transcriptional patterns throughout the synovium. Hierarchical clustering of pathways based on significance revealed a pattern of association between biologic function and synovial topology. Analysis of pathways uniquely enriched in each region revealed distinct phenotypic abnormalities. As examples, RA lining samples were marked by anomalous immune cell signaling, RA sublining samples were marked by aberrant cell cycle, and RA vessel samples were marked by alterations in heme scavenging., Conclusion: LCM-RNAseq confirms reported transcriptional differences between the RA synovium and the OA synovium and provides evidence supporting a relationship between synovial topology and molecular anomalies in RA., (© 2024 American College of Rheumatology.)