The visual effect of wind turbines on property values is small and diminishing in space and time.

Renewable power generation is the key to decarbonizing the electricity system. Wind power is the fastest-growing renewable source of electricity in the United States. However, expanding wind capacity often faces local opposition, partly due to a perceived visual disamenity from large wind turbines. Here, we provide a US-wide assessment of the externality costs of wind power generation through the visibility impact on property values. To this end, we create a database on wind turbine visibility, combining information on the site and height of each utility-scale turbine having fed power into the U.S. grid, with a high-resolution elevation map to account for the underlying topography of the landscape. Building on hedonic valuation theory, we statistically estimate the impact of wind turbine visibility on home values, informed by data from the majority of home sales in the United States since 1997. We find that on average, wind turbine visibility negatively affects home values in an economically and statistically significant way in close proximity ([Formula: see text]5 miles/8 km). However, the effect diminishes over time and in distance and is indistinguishable from zero for larger distances and toward the end of our sample.

IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data.

IMPRINTS-CETSA (Integrated Modulation of Protein Interaction States-Cellular Thermal Shift Assay) provides a highly resolved means to systematically study the interactions of proteins with other cellular components, including metabolites, nucleic acids and other proteins, at the proteome level, but no freely available and user-friendly data analysis software has been reported. Here, we report IMPRINTS.CETSA, an R package that provides the basic data processing framework for robust analysis of the IMPRINTS-CETSA data format, from preprocessing and normalization to visualization. We also report an accompanying R package, IMPRINTS.CETSA.app, which offers a user-friendly Shiny interface for analysis and interpretation of IMPRINTS-CETSA results, with seamless features such as functional enrichment and mapping to other databases at a single site. For the hit generation part, the diverse behaviors of protein modulations have been typically segregated with a two-measure scoring method, i.e. the abundance and thermal stability changes. We present a new algorithm to classify modulated proteins in IMPRINTS-CETSA experiments by a robust single-measure scoring. In this way, both the numerical changes and the statistical significances of the IMPRINTS information can be visualized on a single plot. The IMPRINTS.CETSA and IMPRINTS.CETSA.app R packages are freely available on GitHub at https://github.com/nkdailingyun/IMPRINTS.CETSA and https://github.com/mgerault/IMPRINTS.CETSA.app, respectively. IMPRINTS.CETSA.app is also available as an executable program at https://zenodo.org/records/10636134.

Metabolomics meets systems immunology.

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Why and How to Account for Sex and Gender in Brain and Behavioral Research.

Long overlooked in neuroscience research, sex and gender are increasingly included as key variables potentially impacting all levels of neurobehavioral analysis. Still, many neuroscientists do not understand the difference between the terms "sex" and "gender," the complexity and nuance of each, or how to best include them as variables in research designs. This TechSights article outlines rationales for considering the influence of sex and gender across taxa, and provides technical guidance for strengthening the rigor and reproducibility of such analyses. This guidance includes the use of appropriate statistical methods for comparing groups as well as controls for key covariates of sex (e.g., total intracranial volume) and gender (e.g., income, caregiver stress, bias). We also recommend approaches for interpreting and communicating sex- and gender-related findings about the brain, which have often been misconstrued by neuroscientists and the lay public alike.

T cell subset composition differs between blood and cerebrospinal fluid in amyotrophic lateral sclerosis.

Inflammation is a hallmark of amyotrophic lateral sclerosis (ALS) and is often assessed through biological samples. Due to the easier access, peripheral blood is more commonly phenotyped instead of cerebrospinal fluid (CSF) or affected tissues in ALS. Here, using flow cytometry, we compared the composition of T cell subsets in blood and CSF in ALS patients. We found consistent but weak correlations between blood and CSF for all T cell subsets examined. This finding implies that blood and CSF offer complementary information when characterizing T cell immunity in ALS and blood may not be used as a surrogate for CSF.

Improvement of Heterologous Soluble Expression of L-amino Acid Oxidase Using Logistic Regression.

Successful implementation of enzymes in practical application hinges on the development of efficient mass production techniques. However, in a heterologous expression system, the protein is often unable to fold correctly and, thus, forms inclusion bodies, resulting in the loss of its original activity. In this study, we present a new and more accurate model for predicting amino acids associated with an increased L-amino acid oxidase (LAO) solubility. Expressing LAO from Rhizoctonia solani in Escherichia coli and combining random mutagenesis and statistical logistic regression, we modified 108 amino acid residues by substituting hydrophobic amino acids with serine and hydrophilic amino acids with alanine. Our results indicated that specific mutations in Euclidean distance, glycine, methionine, and secondary structure increased LAO expression. Furthermore, repeated mutations were performed for LAO based on logistic regression models. The mutated LAO displayed a significantly increased solubility, with the 6-point and 58-point mutants showing a 2.64- and 4.22-fold increase, respectively, compared with WT-LAO. Ultimately, using recombinant LAO in the biotransformation of α-keto acids indicates its great potential as a biocatalyst in industrial production.

Analysis of cancer omics data: a selective review of statistical techniques.

Cancer is an omics disease. The development in high-throughput profiling has fundamentally changed cancer research and clinical practice. Compared with clinical, demographic and environmental data, the analysis of omics data-which has higher dimensionality, weaker signals and more complex distributional properties-is much more challenging. Developments in the literature are often 'scattered', with individual studies focused on one or a few closely related methods. The goal of this review is to assist cancer researchers with limited statistical expertise in establishing the 'overall framework' of cancer omics data analysis. To facilitate understanding, we mainly focus on intuition, concepts and key steps, and refer readers to the original publications for mathematical details. This review broadly covers unsupervised and supervised analysis, as well as individual-gene-based, gene-set-based and gene-network-based analysis. We also briefly discuss 'special topics' including interaction analysis, multi-datasets analysis and multi-omics analysis.

ProSAP: a GUI software tool for statistical analysis and assessment of thermal stability data.

The Cellular Thermal Shift Assay (CETSA) plays an important role in drug-target identification, and statistical analysis is a crucial step significantly affecting conclusion. We put forward ProSAP (Protein Stability Analysis Pod), an open-source, cross-platform and user-friendly software tool, which provides multiple methods for thermal proteome profiling (TPP) analysis, nonparametric analysis (NPA), proteome integral solubility alteration and isothermal shift assay (iTSA). For testing the performance of ProSAP, we processed several datasets and compare the performance of different algorithms. Overall, TPP analysis is more accurate with fewer false positive targets, but NPA methods are flexible and free from parameters. For iTSA, edgeR and DESeq2 identify more true targets than t-test and Limma, but when it comes to ranking, the four methods show not much difference. ProSAP software is available at https://github.com/hcji/ProSAP and https://zenodo.org/record/5763315.

Biomolecular fingerprints of the effect of zoledronic acid on prostate cancer stem cells: Comparison of 2D and 3D cell culture models.

Revealing the potential of candidate drugs against different cancer types without disrupting normal cells depends on the drug mode of action. In the current study, the drug response of prostate cancer stem cells (PCSCs) to zoledronic acid (ZOL) grown in two-dimensional (2D) and three-dimensional (3D) culture systems was compared using Fourier transform-infrared (FT-IR) spectroscopy which is a vibrational spectroscopic technique, supporting by biochemical assays and imaging techniques. Based on our data, in 2D cell culture conditions, the ZOL treatment of PCSCs isolated according to both C133 and CD44 cell surface properties induced early/late apoptosis and suppressed migration ability. The CD133 gene expression and protein levels were altered, depending on culture systems. CD133 expression was significantly reduced in 2D cells upon ZOL treatment. FT-IR data revealed that the integrity, fluidity, and ordering/disordering states of the cell membrane and nucleic acid content were altered in both 2D and 3D cells after ZOL treatment. Regular protein structures decrease in 2D cells while glycogen and protein contents increase in 3D cells, indicating a more pronounced cytotoxic effect of ZOL for 2D cells. Untreated 3D PCSCs exhibited an even different spectral profile associated with IR signals of lipids, proteins, nucleic acids, and glycogen in comparison to untreated 2D cells. Our study revealed significant differences in the drug response and cellular constituents between 2D and 3D cells. Exploring molecular targets and/or drug-action mechanisms is significant in cancer treatment approaches; thus, FT-IR spectroscopy can be successfully applied as a novel drug-screening method in clinical research.

Computational network analysis of host genetic risk variants of severe COVID-19.

BACKGROUND: Genome-wide association studies have identified numerous human host genetic risk variants that play a substantial role in the host immune response to SARS-CoV-2. Although these genetic risk variants significantly increase the severity of COVID-19, their influence on body systems is poorly understood. Therefore, we aim to interpret the biological mechanisms and pathways associated with the genetic risk factors and immune responses in severe COVID-19. We perform a deep analysis of previously identified risk variants and infer the hidden interactions between their molecular networks through disease mapping and the similarity of the molecular functions between constructed networks. RESULTS: We designed a four-stage computational workflow for systematic genetic analysis of the risk variants. We integrated the molecular profiles of the risk factors with associated diseases, then constructed protein-protein interaction networks. We identified 24 protein-protein interaction networks with 939 interactions derived from 109 filtered risk variants in 60 risk genes and 56 proteins. The majority of molecular functions, interactions and pathways are involved in immune responses; several interactions and pathways are related to the metabolic and cardiovascular systems, which could lead to multi-organ complications and dysfunction. CONCLUSIONS: This study highlights the importance of analyzing molecular interactions and pathways to understand the heterogeneous susceptibility of the host immune response to SARS-CoV-2. We propose new insights into pathogenicity analysis of infections by including genetic risk information as essential factors to predict future complications during and after infection. This approach may assist more precise clinical decisions and accurate treatment plans to reduce COVID-19 complications.

Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods.

Brain-imaging-genetic analysis is an emerging field of research that aims at aggregating data from neuroimaging modalities, which characterize brain structure or function, and genetic data, which capture the structure and function of the genome, to explain or predict normal (or abnormal) brain performance. Brain-imaging-genetic studies offer great potential for understanding complex brain-related diseases/disorders of genetic etiology. Still, a combined brain-wide genome-wide analysis is difficult to perform as typical datasets fuse multiple modalities, each with high dimensionality, unique correlational landscapes, and often low statistical signal-to-noise ratios. In this review, we outline the progress in brain-imaging-genetic methodologies starting from early massive univariate to current deep learning approaches, highlighting each approach's strengths and weaknesses and elongating it with the field's development. We conclude by discussing selected remaining challenges and prospects for the field.

Statistical Analysis Plan for the INTEnsive Ambulance-Delivered Blood Pressure Reduction in Hyper-ACute Stroke Trial.

INTRODUCTION: Recruitment is complete in the fourth INTEnsive ambulance-delivered blood pressure Reduction in hyper-ACute stroke Trial (INTERACT4), a multicenter, prospective, randomized, open-label, blinded endpoint assessed trial of prehospital blood pressure (BP) lowering initiated in the ambulance for patients with a suspected acute stroke and elevated BP in China. According to the registered and published trial protocol and developed by the blinded trial Steering Committee and Operations team, this manuscript outlines a detailed statistical analysis plan for the trial prior to database lock. METHODS: Patients were randomized (1:1) to intensive (target systolic BP 130-140 mm Hg within 30 min) or guideline-recommended BP management (BP lowering only considered if systolic BP >220 mm Hg) group. Primary outcome is an ordinal analysis of the full range of scores on the modified Rankin scale at 90 days. A modified sample size of 2,320 was estimated to provide 90% power to detect a 22% reduction in the odds (common odds ratio of 0.78) of a worse functional outcome using ordinal logistic regression, on the assumption of 5% patients with missing outcome and 6% patients with a stroke mimic. CONCLUSION: The statistical analysis plan for the trial has been developed to ensure transparent, verifiable, and prespecified analysis and to avoid potential bias in the evaluation of the trial intervention.

Guide for Combining Primary Tumors for Statistical Analysis in Rodent Carcinogenicity Studies.

The Tumor Combination Guide was created at the request of the U. S. Food and Drug Administration (FDA) by a Working Group of biopharmaceutical experts from international societies of toxicologic pathology, the Food and Drug Administration (FDA), and members of the Standard for Exchange of Nonclinical Data (SEND) initiative, to assist pharmacology/toxicology reviewers and biostatisticians in statistical analysis of nonclinical tumor data. The guide will also be useful to study and peer review pathologists in interpreting the tumor data. This guide provides a higher-level hierarchy of tumor types or categories correlating the tumor names from the International Harmonization of Nomenclature and Diagnostic Criteria (INHAND) publications with those available in the NEOPLASM controlled terminology (CT) code list in SEND. The version of CT used in a study should be referenced in the nonclinical study data reviewer's guide (SDRG) (section 3.1) of electronic submissions to the FDA. The tumor combination guide instructions and examples are in a tabular format to make informed decisions for combining tumor data for statistical analysis. The strategy for combining tumor types for statistical analysis is based on scientific criteria gleaned from the current scientific literature; as SEND and INHAND terminology and information evolve, this guide will be updated.

Volatile-compound fingerprinting and discrimination of positional isomers in stamp-pad ink tracing using HS-GC-IMS combined with multivariate statistical analysis.

The rising crime rate associated with document forgery has a significant impact on public safety and social stability. In document fraud cases, determining the origin of a particular stamp-pad ink is the most important objective. In this study, a comprehensive analysis of the volatile compounds in quick-drying stamp-pad inks from six commonly used brands were performed for the first time, utilizing a combination of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) and multivariate statistical analysis methods. Visual and comparative analysis of the differential volatile components among different stamp-pad ink samples was conducted using fingerprints and volcano plots. A total of 127 volatile compounds were accurately identified, with ketones, esters, alcohols, and aldehydes being the most abundant compounds in the stamp-pad inks. Hierarchical clustering analysis (HCA), including dendrograms and clustering heatmaps, was utilized to explore the correlations between these compounds and the samples. Additionally, the precise identification of positional isomers and functional group isomers of aliphatic compounds was achieved. To achieve accurate discrimination of various stamp-pad ink samples, a multivariate statistical analysis method was utilized to establish a classification model for them. Based on the results obtained from HS-GC-IMS, effective discrimination among different brands of stamp-pad ink samples was achieved through principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The model exhibited excellent performance, with the fit index of dependent variables (R2Y) and the predictive index of the model (Q2) values of 0.99 and 0.984, respectively. These results provided significant theoretical evidence for the application of HS-GC-IMS as an efficient technique in the analysis of volatile compounds, identification of positional isomers and functional group isomers, as well as tracing the origin of stamp-pad ink and analyzing the formation time of documents.

Delineation of groundwater potential zones and its extent of contamination from the hard rock aquifers in west-Bengal, India.

This study evaluates the groundwater potential and quality in the parts of Chhotanagpur Gneissic Complex situated in the East Indian Shield. The region has faced groundwater development challenges for several decades. Therefore, in the study area, it is crucial to address the depletion of both groundwater quality and quantity, as this facilitates the identification of potential uncontaminated groundwater zones. The present study interprets the groundwater potential zones (GWPZ) utilizing an analytical hierarchical process (AHP) integrated with hydrogeochemical analysis. Several thematic maps were prepared to delineate the GPWZ. It has been found that ∼0.6% of the study area has a very good potential zone, 14.4% has good, 52% has moderate, and approximately 32% and 0.9% have low to very low prospective groundwater resources, respectively. The authentication of results was found to be excellent (91.4%) with the Area Under Curve (AUC). Analysis of hydrogeochemical data suggests that Mixed Ca-Na-HCO3, Mixed Ca-Mg-Cl, Ca-HCO3, and Na-Cl are the dominant water types in the study area. The principal component analysis suggests that Na+, Mg2+, Cl-, NO3-, and SO42- significantly contribute to groundwater chemistry. The K-means clustering and hierarchical cluster analysis classified groundwater samples into three clusters based on the hydrogeochemical characteristics. It is inferred that silicate weathering and reverse ion reactions through rock-water interaction control geogenic processes for groundwater chemistry. It is also inferred that regions with poor to unsuitable water quality indexes also have low GWPZ. Further, groundwater for irrigation is also accessed and found unsuitable at some locations. This research contributes to comprehending groundwater characteristics in analogous geological regions globally. Additionally, it assists in implementing preventive actions to mitigate groundwater contamination, consequently lowering health risks and formulating sustainable plans for the future.

Geochemical evaluation, ecological and human health risk assessment of potentially toxic elements in urban soil, Southern India.

Roadside soil contamination is mostly caused by human-caused pollutant deposition. PTEs are among the many substances that are harmful for both humans and the environment. PTE concentrations in roadside soil in Chennai, southern India, have been determined in this study. To evaluate the seriousness of the threats, more environmental and geochemical indices have been applied. 83 soil samples have been obtained from the study regions and focusing on important roads. Elemental analysis has been analyzed with ED-XRF and sieve-filtered samples focused on PTEs such as arsenic, barium, cobalt, chromium, copper, iron, potassium, nickel, lead, thorium, titanium, zinc, and uranium. Significant metallic variations have been found in soil samples around roads by the investigation. The elements this study examined section ascending in the following sequence: Fe > Ti > Zn > Cr > Pb > Cu > Ni > Th > As > U > K. In the research area, the CD classification denotes high contamination, whereas the CF indices show mild to significant pollution. PLI indicates moderate to high pollution, whereas EF suggests excessive enrichment. Igeo demonstrates a range from uncontaminated to highly contaminated. PERI showed high levels in the northern study region, whereas GUFI shows several hot spots indicating moderate to severe pollution. The Hazard Index (HI) values for all metals were less than one, demonstrating the absence of non-carcinogenic risks for both adults and children. Multivariate data show natural and anthropogenic PTEs in roadside soil. In addition, a soil quality monitoring system is needed to mitigate continual contamination risks.

Normality Analysis of Numeric Rating Scale Scores in Patients with Chronic Axial Spine Pain Before and After Medial Branch Blocks: a Multicenter Study.

OBJECTIVE: The statistical analysis typically employed to compare pain both before and after interventions assumes scores are normally distributed. The present study evaluates whether Numeric Rating Scale (NRS), specifically the NRS-11, scores are indeed normally distributed in a clinically-relevant cohort of adults with chronic axial spine pain pre- and post-analgesic intervention. METHODS: Retrospective review from four academic medical centers of prospectively collected data from a uniform pain diary administered to consecutive patients after undergoing medial branch blocks. The pain diary assessed NRS-11 scores immediately pre-injection and at 12 different time points post-injection up to 48 hours. D'Agostino-Pearson tests were used to test normality at all time points. RESULTS: One hundred fifty pain diaries were reviewed and despite normally distributed pre-injection NRS-11 scores (K2 = 0.655, p = 0.72), all post-injection NRS-11 data was not normally distributed (K2 = 9.70- 17.62, p = 0.0001-0.008). CONCLUSIONS: Although the results of parametric analyses of NRS-11 scores are commonly reported in pain research, some properties of NRS-11 do not satisfy the assumptions required for these analyses. The data demonstrate non-normal distributions in post-intervention NRS-11 scores, thereby violating a key requisite for parametric analysis. We urge pain researchers to consider appropriate statistical analysis and reporting for non-normally distributed NRS-11 scores to ensure accurate interpretation and communication of these data. Practicing pain physicians should similarly recognize that parametric post-intervention pain score statistics may not accurately describe the data and should expect manuscripts to utilize measures of normality to justify the selected statistical methods.

Exploring metabolic effects of dipeptide feed media on CHO cell cultures by in silico model-guided flux analysis.

There is a growing interest in perfusion or continuous processes to achieve higher productivity of biopharmaceuticals in mammalian cell culture, specifically Chinese hamster ovary (CHO) cells, towards advanced biomanufacturing. These intensified bioprocesses highly require concentrated feed media in order to counteract their dilution effects. However, designing such condensed media formulation poses several challenges, particularly regarding the stability and solubility of specific amino acids. To address the difficulty and complexity in relevant media development, the biopharmaceutical industry has recently suggested forming dipeptides by combining one from problematic amino acids with selected pairs to compensate for limitations. In this study, we combined one of the lead amino acids, L-tyrosine, which is known for its poor solubility in water due to its aromatic ring and hydroxyl group, with glycine as the partner, thus forming glycyl-L-tyrosine (GY) dipeptide. Subsequently, we investigated the utilization of GY dipeptide during fed-batch cultures of IgG-producing CHO cells, by changing its concentrations (0.125 × , 0.25 × , 0.5 × , 1.0 × , and 2.0 ×). Multivariate statistical analysis of culture profiles was then conducted to identify and correlate the most significant nutrients with the production, followed by in silico model-guided analysis to systematically evaluate their effects on the culture performance, and elucidate metabolic states and cellular behaviors. As such, it allowed us to explain how the cells can more efficiently utilize GY dipeptide with respect to the balance of cofactor regeneration and energy distribution for the required biomass and protein synthesis. For example, our analysis results uncovered specific amino acids (Asn and Gln) and the 0.5 × GY dipeptide in the feed medium synergistically alleviated the metabolic bottleneck, resulting in enhanced IgG titer and productivity. In the validation experiments, we tested and observed that lower levels of Asn and Gln led to decreased secretion of toxic metabolites, enhanced longevity, and elevated specific cell growth and titer. KEY POINTS: • Explored the optimal Tyr dipeptide for the enhanced CHO cell culture performance • Systematically analyzed effects of dipeptide media by model-guided approach • Uncovered synergistic metabolic utilization of amino acids with dipeptide.

Ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry combined with network pharmacology for analysis of potential quality markers of three processed products of Qingpi.

Qingpi, a well-known traditional Chinese medicine for qi-regulating and commonly processed into three types of pieces, has been widely used in the clinical application of liver disease for thousands of years. In this study, an ultra-high-performance liquid chromatography-quadrupole-time of flight-mass spectrometry approach along with multivariate statistical analysis was developed to assess and characterize the differentiations of three processed products and confirm the potential quality markers of Qingpi. In addition, a systematic analysis combined with network pharmacology and molecular docking was performed to clarify the potential mechanism of Qingpi for the treatment of liver disease. As a result, 18 components were identified and an integrated network of Qingpi-Components-Target-Pathway-Liver Disease was constructed. Eight compounds were finally screened out as the potential quality markers acting on ten main targets and pathways of liver disease. Molecular docking analysis results indicated that the quality markers had a good binding activity with the targets. Overall, this work preliminarily identified the potential quality markers of three processed products of Qingpi, and predicted its targets in the prevention and treatment of liver disease, which can provide supporting information for further study of the pharmacodynamic substances and mechanisms of Qingpi.

Evaluation of the key ingredient from the main production areas of Phellodendri Amurensis Cortex using ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry and ultra-high-performance liquid chromatography coupled to triple-quadrupole mass spectrometry.

Phellodendri Amurensis Cortex (PAC) is a medicinal herb that has been generally used to treat diarrhea and jaundice. In order to comprehensively evaluate the PAC in the main production areas quality, a qualitative and quantitative method with highly effective, sensitive, and reliable was developed. The chemical compositions of PAC were analyzed, and fingerprints were established by ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-Q-TOF-MS). Then, the determination of berberine, canthin-6-one, dictamnine, γ-fagarine, and magnoflorine from PAC samples was simultaneously performed using UPLC-QQQ-MS. Furthermore, the chemical components of PAC from different regions were compared and analyzed by combining hierarchical cluster analysis, principal component analysis, and orthogonal partial least squares discriminant analysis. A total of 58 compounds were identified, including 36 alkaloids, four phenylpropanoids, seven terpenoids, four flavonoids and their glycosides, an organic acid compound, and six other components. The fingerprint results show that samples have good similarity. Meanwhile, the content of the five ingredients in different habitats is quite different. By multivariate statistical analysis, 18 batches of PAC could be divided into three categories, and 20 components were identified as differential markers of various origins. A comprehensive method of PAC quality evaluation and chemical composition difference analysis was established, which provided the scientific basis for quality evaluation and further pharmacological mechanism research.