Hierarchical Principal Component Analysis Research Articles

Identifying the most representative actigraphy variables reflecting standardized hand function assessments for remote monitoring in children with unilateral cerebral palsy

BackgroundAccurate assessment of physical activity and motor function in children with cerebral palsy is crucial for determining the effectiveness of interventions. This study aimed to investigate the correlation between real-world activity monitoring outcomes and in-laboratory standardized hand function assessments in children with unilateral cerebral palsy.MethodsActigraphy data were collected over 3 days from children aged 4–12 years with unilateral cerebral palsy before in-laboratory assessments. To tackle the high dimensionality and collinearity of actigraphy variables, we first applied hierarchical clustering using the Pearson correlation coefficient as the distance metric and then performed a principal component analysis (PCA) to reduce the dimensionality of our data.ResultsBoth hierarchical clustering and PCAs revealed a consistent pattern in which magnitude ratio variables (ln[affected side magnitude/less-affected side magnitude]) were more strongly associated with standardized assessments of hand function than with activity time and distance domain variables. Hierarchical clustering analysis identified two distinct clusters of actigraphy variables, with the second cluster primarily consisting of magnitude ratio variables that exhibited the strongest correlation with Melbourne Assessment 2, Pediatric Motor Activity Log, Assisting Hand Assessment, and Manual Ability Classification System level. Principal component 2, primarily representing the magnitude ratio domain, was positively associated with a meaningful portion of subcategories of standardized measures, whereas principal component 1, representing the activity time and distance component, showed limited associations.ConclusionsThe magnitude ratio of actigraphy can provide additional objective information that complements in-laboratory hand function assessment outcomes in future studies of children with unilateral cerebral palsy.Trial registration in ClinicalTrials.govNCT04904796 (registered prospectively; date of registration: 23/05/2021).

Hierarchical Principal Components for Data-Driven Multiresolution fMRI Analyses.

Understanding the organization of neural processing is a fundamental goal of neuroscience. Recent work suggests that these systems are organized as a multiscale hierarchy, with increasingly specialized subsystems nested inside general processing systems. Current neuroimaging methods, such as independent component analysis (ICA), cannot fully capture this hierarchy since they are limited to a single spatial scale. In this manuscript, we introduce multiresolution hierarchical principal components analysis (hPCA) and compare it to ICA using simulated fMRI datasets. Furthermore, we describe a parametric statistical filtering method developed to focus analyses on biologically relevant features. Lastly, we apply hPCA to the Human Connectome Project (HCP) to demonstrate its ability to estimate a hierarchy from real fMRI data. hPCA accurately estimated spatial maps and time series from networks with diverse hierarchical structures. Simulated hierarchies varied in the degree of branching, such as two-way or three-way subdivisions, and the total number of levels, with varying equal or unequal subdivision sizes at each branch. In each case, as well as in the HCP, hPCA was able to reconstruct a known hierarchy of networks. Our results suggest that hPCA can facilitate more detailed and comprehensive analyses of the brain's network of networks and the multiscale regional specializations underlying neural processing and cognition.

A Comparative Evaluation of Initialization Strategies for K-Means Clustering with Swarm Intelligence Algorithms

Clustering is a fundamental data analysis task that presents challenges. Choosing proper initialization centroid techniques is critical to the success of clustering algorithms, such as k-means. The current work investigates six established methods (random, Forgy, k-means++, PCA, hierarchical clustering, and naive sharding) and three innovative swarm intelligence-based approaches—Spider Monkey Optimization (SMO), Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)—for k-means clustering (SMOKM, WOAKM, and GWOKM). The results on ten well-known datasets strongly favor swarm intelligence-based techniques, with SMOKM consistently outperforming WOAKM and GWOKM. This finding provides critical insights into selecting and evaluating centroid techniques in k-means clustering. The current work is valuable because it provides guidance for those seeking optimal solutions for clustering diverse datasets. Swarm intelligence, especially SMOKM, effectively generates distinct and well-separated clusters, which is valuable in resource-constrained settings. The research also sheds light on the performance of traditional methods such as hierarchical clustering, PCA, and k-means++, which, while promising for specific datasets, consistently underperform swarm intelligence-based alternatives. In conclusion, the current work contributes essential insights into selecting and evaluating initialization centroid techniques for k-means clustering. It highlights the superiority of swarm intelligence, particularly SMOKM, and provides actionable guidance for addressing various clustering challenges.

Bi-directional hyperspectral reconstruction of cherry tomato: diagnosis of internal tissues maturation stage and composition.

Precision monitoring maturity in climacteric fruits like tomato is crucial for minimising losses within the food supply chain and enhancing pre- and post-harvest production and utilisation. This paper introduces an approach to analyse the precision maturation of tomato using hyperspectral tomography-like. A novel bi-directional spectral reconstruction method is presented, leveraging visible to near-infrared (Vis-NIR) information gathered from tomato spectra and their internal tissues (skin, pulp, and seeds). The study, encompassing 118 tomatoes at various maturation stages, employs a multi-block hierarchical principal component analysis combined with partial least squares for bi-directional reconstruction. The approach involves predicting internal tissue spectra by decomposing the overall tomato spectral information, creating a superset with eight latent variables for each tissue. The reverse process also utilises eight latent variables for reconstructing skin, pulp, and seed spectral data. The reconstruction of the tomato spectra presents a mean absolute percentage error of 30.44 % and 5.37 %, 5.25 % and 6.42 % and Pearson's correlation coefficient of 0.85, 0.98, 0.99 and 0.99 for the skin, pulp and seed, respectively. Quality parameters, including soluble solid content (%), chlorophyll (a.u.), lycopene (a.u.), and puncture force (N), were assessed and modelled with PLS with the original and reconstructed datasets, presenting a range of R2 higher than 0.84 in the reconstructed dataset. An empirical demonstration of the tomato maturation in the internal tissues revealed the dynamic of the chlorophyll and lycopene in the different tissues during the maturation process. The proposed approach for inner tomato tissue spectral inference is highly reliable, provides early indications and is easy to operate. This study highlights the potential of Vis-NIR devices in precision fruit maturation assessment, surpassing conventional labour-intensive techniques in cost-effectiveness and efficiency. The implications of this advancement extend to various agronomic and food chain applications, promising substantial improvements in monitoring and enhancing fruit quality.

Ink in secular and religious documents from the Pelliot collection (BnF, Paris): A study of ingredients and uses of ink in1st millennium Tocharian, Sanskrit and Chinese manuscripts from the Kucha and Dunhuang regions

This paper presents the study of ink from first millennium manuscripts discovered in the Kucha and Dunhuang regions (West China) from the “Pelliot collection” stored at the Bibliothèque nationale de France (Paris, France). The sixteen analyzed manuscripts written in black ink were selected partly because of their diverse characteristics (script, type, etc.) and partly because of the high possibility for their accurate dating between 4th to the 8th century. This period is defined by an important economic growth as well as cultural and artistic activities, facilitated by the fact that Kucha and Dunhuang played a key role in the passage of the Silk Road network connecting China, India and western Central Asia.FTIR and Raman spectroscopies were first employed as they are non-destructive and non-invasive methods. They indicated that the ink present on the manuscripts was composed of soot and protein binder, however they did not provide direct insight on the precise origin of the soot (e.g pine soot or lampblack) and the protein binder used in the fabrication of inks. The analysis of the inked manuscripts by pyrolysis-comprehensive two-dimensional gas chromatography/mass spectrometry (Py-GCxGC-MS) – for which only a small amount of sample (microgram quantities) is required - and the exploration of the obtained multivariate datasets combining Hierarchical Clustering and PCA provide invaluable information on Asian inks, their manufacture and use in different historical periods. Our research suggests that the majority of the inks were made with pine soot. Furthermore, our results reveal different chemical signatures that may be indicators of difference in the production of pine soot (use of various species of pine wood, combustion parameters, etc.). Moreover, diverse proteinaceous binders and additives, known to have been used in ancient Chinese ink manufacture to ensure ink’s consistency and for their antimicrobial and aromatic properties, were identified.

Precision maturation assessment of grape tissues: Hyperspectral bi-directional reconstruction using tomography-like based on multi-block hierarchical principal component analysis

This paper introduces a tomography-like method for assessing grape maturation. It analyses inner tissue spectra through point-of-measurement (POM) sensing. A multi-block hierarchical principal component analysis (MHPCA) algorithm was used for the spectral reconstruction of total grapes (skin, pulp, and seed). Two grape cultivars, Loureiro (white; n = 216) and Vinhão (red; n = 205) were measured at 12 dates after veraison (DAV). The reconstructed spectra showed no significant differences (p < 0.001) from the originals for both grapes. Loureiro had better statistical metrics (Person's correlation coefficient (r) values for: total grape: 0.99, skin: 1; pulp: 1, seed: 0.94) than Vinhão (r values for: total grape: 0.92, skin: 0.92; pulp: 0.95, seed: 0.95). Using self-learning artificial intelligence (SL-AI), the following parameters were predicted for both grapes: soluble solids content (%; MAPE <13%), puncture force (N; MAPE <29%), chlorophyll content (a.u.; MAPE <29%), and anthocyanin content (a.u.; MAPE <17%, Vinhão only). When comparing observed values with predicted skin, pulp, and seed spectra, Vinhão showed no statistical differences for most parameters, except pulp chlorophyll on one DAV in the final maturation stage. The same was done with the Loureiro cultivar. Although Loureiro mostly showed no statistical differences in assessed parameters across tissues and dates, variations were found in pulp and skin chlorophyll content and puncture force. This tomography-like approach based on tissue maturation can help viticulturists to access instant data on grape maturation, supporting informed decision-making and promoting more sustainable agricultural practices.

LDER: a classification framework based on ERP enhancement in RSVP task

Objective. Rapid serial visual presentation (RSVP) based on electroencephalography (EEG) has been widely used in the target detection field, which distinguishes target and non-target by detecting event-related potential (ERP) components. However, the classification performance of the RSVP task is limited by the variability of ERP components, which is a great challenge in developing RSVP for real-life applications. Approach. To tackle this issue, a classification framework based on the ERP feature enhancement to offset the negative impact of the variability of ERP components for RSVP task classification named latency detection and EEG reconstruction was proposed in this paper. First, a spatial-temporal similarity measurement approach was proposed for latency detection. Subsequently, we constructed a single-trial EEG signal model containing ERP latency information. Then, according to the latency information detected in the first step, the model can be solved to obtain the corrected ERP signal and realize the enhancement of ERP features. Finally, the EEG signal after ERP enhancement can be processed by most of the existing feature extraction and classification methods of the RSVP task in this framework. Main results. Nine subjects were recruited to participate in the RSVP experiment on vehicle detection. Four popular algorithms (spatially weighted Fisher linear discrimination-principal component analysis (PCA), hierarchical discriminant PCA, hierarchical discriminant component analysis, and spatial-temporal hybrid common spatial pattern-PCA) in RSVP-based brain–computer interface for feature extraction were selected to verify the performance of our proposed framework. Experimental results showed that our proposed framework significantly outperforms the conventional classification framework in terms of area under curve, balanced accuracy, true positive rate, and false positive rate in four feature extraction methods. Additionally, statistical results showed that our proposed framework enables better performance with fewer training samples, channel numbers, and shorter temporal window sizes. Significance. As a result, the classification performance of the RSVP task was significantly improved by using our proposed framework. Our proposed classification framework will significantly promote the practical application of the RSVP task.

S-27-3: HYPERTENSIVE NEPHROPATHY: GLOMERULAR PROTEOMIC PROFILING OF KIDNEY BIOPSIES REVEALS A SIGNATURE OF DISEASE PROGRESSION

Objective: Hypertensive nephropathy, a major cause of chronic kidney disease (CKD) worldwide, requires kidney biopsy as the diagnostic gold-standard. However, histological findings are unspecific and specific prognostic markers are missing. Accordingly, our objective was to detect candidate markers based on glomerular protein signatures from the diagnostic kidney biopsy that predict subsequent disease progression. Design and method: We studied adult patients (n = 17) with an eGFR &gt; 30 ml/min/1.73m2 and proteinuria &lt; 3 g/d from the Norwegian Kidney Biopsy Registry, including stable non-progressing patients (n = 9) and patients progressing (n = 8) to end-stage kidney disease (ESKD) within 20 years. Glomerular cross-sections from archival kidney biopsy sections were microdissected and processed for protein extraction. Proteomic analyses were performed using Q-exactive HF mass spectrometer and relative glomerular protein abundances were compared between progressive and non-progressive patients. Results: Amongst n = 1870 quality filtered proteins, n = 58 were differentially expressed in progressive and non-progressive glomerular samples, with absolute fold changes ≥ 1.5, p ≤ 0.05. However, only by using n = 17 glomerular proteins with absolute fold changes ≥ 2 and p ≤ 0.05, the hierarchical clustering and principal component analysis (PCA) effectively separated progressor and non-progressor patient samples. Supervised K nearest neighbor (KNN) analysis with leave-one-out internal cross validation was employed to develop a prognostic five protein classifier signature of hypertensive nephropathy progression. Thereby, Gamma-butyrobetaine dioxygenase (BBOX1, O75936) and Cadherin 16 (CDH16, O75309), overexpressed in progressors, and Eosinophil peroxidase (EPX, P11678), DnaJ homolog subfamily B member 1 (DNAJB1, P25685) and Alpha-1-syntrophin (SNTA1, Q13424), overexpressed in non-progressive glomeruli, correctly classified 16/17 samples into progressors and non-progressors. Respective classifier results, including area under the ROC curve of 0.99, hierarchical cluster analysis and PCA, are depicted in Figure 1 A-D (P = progressors; NP = non-progressors). Immunohistochemistry confirmed results of BBOX1 and CDH16. Geneset Enrichment Analysis (GSEA) showed that metabolic pathways were enriched in progressors, and structural cell pathways in non-progressors. Pathway analysis identified Epithelial Adherens Junction Signaling as most affected canonical pathway. We then evaluated protein signatures associated with hypertensive nephropathy progression with those associated with IgA nephropathy progression from our published dataset (Paunas T. Clin Proteom 2017). The expression of only three proteins were altered in a similar direction in both datasets. KNN classifier proteins of progression of HN were either not detected in IgAN or were not significantly altered during progression of IgAN. Conclusion: Glomerular proteomic profiling from the initial diagnostic kidney biopsy can be used to discriminate progressive from non-progressive patients with hypertensive nephropathy.

Seed priming and exogenous application of citric acid enhance seedling growth and photosynthetic pigments and mitigate oxidative damage of soybean (Glycine max) under salt stress

Seed priming and citric acid (CA) supplementation on germination and seedling growth of soybeans were investigated. Soybean seeds were primed with distilled water (control), 1 mM CA (CA1), or 2 mM CA (CA2) and then placed for germination in Petri dishes containing distilled water or 150 mM NaCl (SS), alone or in combination with 1 mM or 2 mM CA. Germinated seeds were placed in hydroponic pots using a similar treatment regimen to that specified for the Petri dishes to obtain seedling growth and biochemical parameters. Salt stress significantly lowered germination, growth traits, relative water content (RWC), and photosynthetic pigment. When soybean seeds were primed with CA under salt stress, the germination rate, final germination percentage, seed vigor index, and number of lateral roots significantly increased. Moreover, supplementation of CA significantly increased fresh and dry shoot and root weight, plant height, RWC, and photosynthetic pigments compared to salt-treated plants. The results also displayed that salt stress considerably increased hydrogen peroxide (H2O2) and malondialdehyde (MDA) contents compared to control plants. Spraying of CA1 and CA2 significantly lowered the levels of H2O2 and MDA in salt-treated plants. Both hierarchical clustering and PCA revealed that the effects of salt stress and CA on germination, growth characteristics, photosynthetic pigments, H2O2, and MDA concentrations strongly interacted with one another. According to the findings, CA could be applied as a seed priming and exogenous agent to help soybeans grow more quickly when exposed to salt stress.

Statistical framework to assess long-term spatio-temporal climate changes: East River mountainous watershed case study

Evaluation of long-term temporal and spatial climatic change in mountainous regions is a critical challenge because of the interactive effects of multiple land and climatic factors and processes. Here we present the application of the statistical framework to the assessment of changes of climatic conditions, using data from 17 meteorological stations across the East River watershed near Crested Butte, Colorado, USA, and spanning the period from 1966 to 2021. The framework is developed based on (1) a time-series analysis of daily, monthly, and yearly averaged meteorological parameters (temperature, relative humidity, precipitation, wind speed, etc.), (2) evaluation and time series analysis of potential evapotranspiration (ETo), actual evapotranspiration (ET), aridity index (AI), standard precipitation index (SPI) and standard precipitation-evapotranspiration index (SPEI), and (3) a temporal-spatial climatic zonation of the studied area based on the hierarchical clustering and PCA analysis of the SPEI, because the SPEI can be considered an integrative characteristic of the changes of climatic conditions. The Budyko model, with the application of the Penman–Monteith equation for the estimation of ETo, was used to determine the ET. The time series analysis of the AI is used to identify the periods with energy limited and water limited conditions. Hierarchical clustering of site locations for the three temporal segments of the SPEI showed a significant temporal-spatial shifts, indicating that dynamic climatic processes drive zonation patterns. Therefore, the watershed climatic zonation requires periodic re-evaluation based on the structural time series analysis of meteorological and water balance data.

Investigation of pollutant metals in road dust in a post-industrial city: Case study from Detroit, Michigan

Inhalation and/or ingestion of toxic heavy metals enriched in road dust have toxic effects on humans. From inhalation and ingestion of dust, release of readily bioavailable elements sorbed on to dust, could lead to health issues such as lung or skin cancer, kidney dysfunction, hypertension, dermal lesions, peripheral neuropathy, and vascular disease. On 49 road dust and soil samples collected in the Metro Detroit area, we analyzed a suite of trace metals in &amp;lt;63 μm and size-separated fractions to quantify the extent of metal enrichment and contamination status. We evaluated Enrichment Factor (EF), Pollution Index (PI), Geoaccumulation Index (GI), and Pollution Load Index (PLI). The average Chromium (Cr) concentration of 198 ppm is the highest reported for any global city in literature while in ∼80% samples, Barium (Ba), Cr, Lead (Pb), and Zinc (Zn) concentrations exceeded the USEPA limit. The EF for Barium (Ba) was found to be ≥9 in all samples, and for the rest of other elements was &amp;gt;1.5. The PI values for Cr, Ba, Zinc (Zn), Lead (Pb), and Selenium (Se) indicate elevated levels and for Ba, Cr, and Zn up to a moderate level. PLI values in &amp;gt;50% of road dusts were ≥3, indicating many sites are ‘heavily polluted’. The PLI value in Detroit road dust is higher than those reported for some other global cities including Shanghai. Observed strong correlations between Cr-Zn and Cadmium (Cd)-Silver (Ag) likely indicate a common metal source. Cluster analysis of data for Cd, Arsenic (As), Pb, and Zn appears to be distinct between different proximally related clusters, while Nickel (Ni) and Ba are potentially more distinct between property type clusters. In general, metal concentrations of industrial samples are more commonly distinct with over half of metals (≥6 of 11) differing from residential and/or soil samples. Three generalized “groups” were identified from the combination of hierarchical cluster and PCA analyses followed by varimax rotation: Group 1 characterized by Cr, Cd, Ni, Ag, Zn, and Copper (Cu); Group 2 characterized by Pb, As, Mercury (Hg), and minor Ba; and Group 3 characterized by Se. This is the first study to report all four metrics of metal pollution along with rigorous statistical analysis.

Fish Diversity along the Mekong River and Delta Inferred by Environmental-DNA in a Period of Dam Building and Downstream Salinization

The Mekong River is one of the largest rivers in the world and hosts the second greatest fish diversity in the world after the Amazon. However, despite the importance of this diversity and its associated biomass for human food security and the economy, different anthropogenic pressures threaten the sustainability of the Mekong River and fish diversity, including the intense damming of the main river. Both the increase in salt-water penetration into the Mekong Delta and the disrupted connectivity of the river may have serious impacts on the numerous freshwater and migratory species. To evaluate the potential of an eDNA approach for monitoring fish diversity, water was sampled at 15 sites along the salinity gradient in the Mekong Delta and along 1500 km of the main stream, from Vietnam to Thailand and Laos. A total of 287 OTUs were recovered, of which 158 were identified to the species level using both reference sequences available in GenBank and references obtained locally. Agglomerative hierarchical clustering and PCA identified up to three main species assemblages in our samples. If the transition from brackish to freshwater conditions represents the main barrier between two of these assemblages, more surprisingly, the two other assemblages were observed in the freshwater Mekong, with a spatial disjunction that did not match any biogeographic ecoregion or the Khone falls, the latter thought to be an important fish dispersion barrier. Between 60% and 95% of the freshwater species were potamodromous. This pioneer eDNA study in the Mekong River at this geographical and ecological scale clearly confirmed the potential of this approach for ecological and diversity monitoring. It also demonstrated the need to rapidly build an exhaustive Mekong fish barcode library to enable more accurate species’ assignment. More eDNA surveys can now be expected to better describe the ecological niche of different species, which is crucial for any models aimed at predicting the impact of future damming of the Mekong.

Spectrum-effect relationship between HPLC fingerprints and inhibitory activity in MUC5AC mucin of Pinelliae Rhizoma Praeparatum Cum Alumine

Purpose: To investigate the spectrum-effect relationship between HPLC fingerprints and the inhibitory effect on MUC5AC mucin of Pinelliae Rhizoma Praeparatum Cum Alumine (PRPCA).Methods: The fingerprints of 20 PRPCA batches were established using HPLC and their similarities or differences were analyzed using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The inhibitory effects of MUC5AC mucin were evaluated in LPS-treated NCI-H292 cells. The spectrum-effect relationship between common chromatographic peaks and MUC5AC inhibition was established using a partial least squares-discriminant analysis (PLS-DA).Results: Fifteen common chromatographic peaks were identified by analyzing HPLC fingerprints, with uridine, tyrosine, uracil, and inosine found as possible markers to distinguish the PRPCA from different sources. Spectrum-effect relationship analysis showed that the chromatographic peaks 5, 6, 10 (vernine), 12 (5-hydroxymethylfurfural), 14 (tryptophan) and 15 (adenosine) were closely associated with the inhibitory effect on MUC5AC mucin.Conclusion: The spectrum-effect relationship between HPLC fingerprints and the inhibitory effect on MUC5AC mucin of PRPCA was successfully established in the present study. Our findings further reveal the material basis of PRPCA and provide an effective method for its quality control.

Cellular immunophenotype of major spine surgery in adults.

ASD reconstructions are a major, sterile traumatic insult, likely causing perturbations to the immune systems. The immune response to surgery is associated with outcomes. The purpose of this study was to examine for a detectable immune signature associated with ASD surgery. Consecutive patients undergoing ASD surgery were approached and enrolled. Peripheral blood was drawn before incision, 4h after, and 24h after incision. Blood was stabilized and comprehensive flow cytometric immunophenotyping performed. Leukocyte population frequency, absolute number and activation marker expression were defined. Immunologic features were defined and analyzed by hierarchical clustering and principal component analysis (PCA). Changes over time were evaluated by repeated measures ANOVA (RMANOVA) and were corrected for a 1% false discovery rate. Post hoc testing was by Dunn's test. p values of < = 0.05 were considered significant. Thirteen patients were enrolled; 11(85%) F, 65.4years (± 7.5), surgical duration 418 ± 83min, EBL 1928 ± 1253mL. Hierarchical clustering and PCA found consistent time from incision-dependent changes. HLA-DR and activating co-stimulatory molecule CD86 were depressed at 4h and furthermore at 24h on monocyte surfaces. CD4 + HLA-DR + T cells, but not CD8 +, increased over time with increased expression of PD-1 at 4 and 24h. Despite surgery and patient heterogeneity, we identified an immune signature associated with the sterile trauma of ASD surgery. Circulating leukocyte populations change in composition and signaling protein expression after incision and persisting to 24h after incision, suggesting an immunocompromised state. Further work may determine relationships between this state and poor outcomes after surgery.

Utilization of nano-hmC-seal technology to detect epigenetic signatures of peritoneal metastasis in cell-free DNA (cfDNA) in patients with colorectal and high-grade appendiceal cancer.

e15510 Background: Peritoneal metastases from lower GI tumors are common and cause significant morbidity. Assays of circulating-tumor DNA (ctDNA) to identify peritoneal recurrence after curative-surgical intent are limited due to the low abundance of DNA shed by these tumors, especially mucinous tumors. We demonstrate for the first time an ultra-sensitive test to detect biomarkers modified by the epigenetic mark 5-hydroxymethylcytosine (5hmC) in human cfDNA utilizing nano-hmC seal technology to identify patients with peritoneal metastases (PM) after curative-intent resection. Methods: Patients treated with curative intent resection of colorectal (CRC) and high-grade appendiceal (HGA) cancers underwent prospective plasma collection. Peritoneal disease was documented by direct visual inspection (laparoscopy/laparotomy). Plasma samples were analysed using nano-hmC-seal technique to obtain 5hmC signatures. Genome-wide differential analysis was used to identify significant 5hmC- differentially modified gene body regions (p-value &lt; 0.01 and log-fold change &gt; 10%). Functional annotation analysis was performed to further characterize differentially 5hmC -modified genes noted on multivariate logistic models. Results: Of 64 patients included in the study (CRC = 26; HGA = 38), 47 (73%) were PM+. The median DNA extracted per sample was 23.5 ng (IQR: 15.8-38.3), and no sample failed sequencing criteria. After genome-wide analysis, 39 gene body regions were identified as differentially 5hmC-modified, and 14 genes remained significant after multivariate adjustment. The signatures for PM+ patients were distinguishable from PM- patients using hierarchical clustering and principal component analyses. Functional annotation analysis based on the 14 genes point to alterations in immune and microbiome-related pathways in PM+ group (Table). Conclusions: We report for the first time the detection of peritoneal metastasis in cfDNA from human lower GI cancers using nano-5hmC seal epigenomic technology. This technology could be exploited to detect recurrences in tumors that shed very low levels of cfDNA. This signature is being validated in a prospective clinical trial (NCT 04157322).[Table: see text]

Genomic analysis of clear cell carcinoma.

5548 Background: Clear cell carcinomas (CCC) are rare histologies outside of the kidney and are typically less sensitive to standard treatments. Genomic alterations in chromatin remodeling pathways involving ARID1A or the intracellular PI3K-mTOR signaling pathway are found in both renal and ovarian CCC. It is unclear whether CCCs originating from different anatomic sites share a common genomic landscape. This CARIS Precision Oncology Alliance project sought to determine whether CCC of different organs shared similar genomic signatures and to identify potential pathways that could be targeted in a tumor-agnostic clinical trial. Methods: CCCs (N = 861) from multiple primary tumor sites, including kidney (30.5%), ovary (39%), endometrium (23.9%), other gynecologic sites (e.g., cervix, fallopian tube, 3.3%), and miscellaneous (non-kidney or gynecologic sites, 3.3%) were analyzed at the Caris Life Sciences Laboratory (Phoenix, AZ). Using hierarchical clustering (HC) and principal component analysis (PCA), the samples were compared across 648 total genes from five metabolic gene sets consisting of angiogenesis, glycolysis, hypoxia, oxidative phosphorylation, and fatty acid metabolism. Gene Set Enrichment Analysis (GSEA) was further conducted on the samples across fifty hallmark gene sets representing specific biologic processes and expression. Samples were also analyzed for individual genomic alterations and immune-oncology associated biomarkers. PD-L1 (SP142) expression was evaluated by immunohistochemistry (positive threshold: 2+ stain intensity and ≥ 5% tumor cells). Results: HC and PCA demonstrated that renal CCC formed distinct clusters compared to non-renal CCC. Tumors from gynecologic sites could not be separated into distinct clusters. GSEA showed that the hypoxia gene set was significantly upregulated in the renal but not in non-renal CCCs. Mutations involving TP53, ARID1A, PIK3CA were found to be the most altered genes in endometrial (62%, 26%, 31%), ovarian (13%, 55%, 48%), other gynecological sites (33%, 38%, 44%), and non-gynecologic CCC (13%, 17%, 12%) respectively. PD-L1 expression, high tumor mutational burden (≥10 mutations/Mb), and deficient mismatch repair/microsatellite instability rates across sites were: kidney (11%, 2%, 2%), endometrium (13%, 12%, 7%), ovary (9%, 4%, 3%), other gynecological sites (31%, 11%, 11%), and miscellaneous sites (11%, 19%, 4%). Conclusions: Initial metabolic gene expression clustering analysis shows that CCCs do not separate by organ of origin beyond renal versus extra-renal. TP53, ARID1A, and PIK3CA were the most frequently altered genes in non-renal CCC. Out of fifty hallmark gene sets, only two were statistically significantly different among gynecological CCCs. This similarity between gynecological CCC can be leveraged by targeting pathways such as PI3K-AKT-mTOR, DNA repair, and MYC targets in a site agnostic manner. Furthermore, high PD-L1 expression is found in other gynecological sites.

Combining network pharmacology with chromatographic fingerprinting and multicomponent quantitative analysis for the quality evaluation of Astragali Radix.

Nowadays, cultivated variants and adulterants of Astragali Radix (AR) have flooded the market, causing the quality assurance of AR to be challenging. To address this issue, we combined network pharmacology with chromatographic fingerprinting and multicomponent quantitative analysis for the quality evaluation of AR. Specifically, through network pharmacology, a complete understanding of the active components and pharmacological activities of AR was established. In addition, establishing fingerprint profiles and multicomponent quantitation by high-performance liquid chromatography (HPLC) is convenient and comprehensive, and can more fully reflect the overall situation of the distribution of various chemical components. To evaluate and differentiate AR from different origins, hierarchical cluster analysis and principal component analysis were performed. The result showed that AR acts synergistically through multiple targets and pathways. The content of chemical components in AR from different origins varied significantly. Combining network pharmacology and multicomponent quantification results, astragaloside II and IV and formononetin can be used as quality markers for the quality control of AR. This study provides a comprehensive and reliable strategy for the quality evaluation of AR and identifies its quality markers to ensure the quality of the herb.

Unraveling Heat Tolerance in Upland Cotton (Gossypium hirsutum L.) Using Univariate and Multivariate Analysis.

The ever-changing global environment currently includes an increasing ambient temperature that can be a devastating stress for organisms. Plants, being sessile, are adversely affected by heat stress in their physiology, development, growth, and ultimately yield. Since little is known about the response of biochemical traits to high-temperature ambiance, we evaluated eight parental lines (five lines and three testers) and their 15 F1 hybrids under normal and high-temperature stress to assess the impact of these conditions over 2 consecutive years. The research was performed under a triplicate randomized complete block design including a split-plot arrangement. Data were recorded for agronomic, biochemical, and fiber quality traits. Mean values of agronomic traits were significantly reduced under heat stress conditions, while hydrogen peroxide, peroxidase, total soluble protein, superoxide dismutase, catalase (CAT), carotenoids, and fiber strength displayed higher mean values under heat stress conditions. Under both conditions, high genetic advance and high heritability were observed for seed cotton yield (SCY), CAT, micronaire value, plant height, and chlorophyll-a and b content, indicating that an additive type of gene action controls these traits under both the conditions. For more insights into variation, Pearson correlation analysis and principal component analysis (PCA) were performed. Significant positive associations were observed among agronomic, biochemical, and fiber quality-related traits. The multivariate analyses involving hierarchical clustering and PCA classified the 23 experimental genotypes into four groups under normal and high-temperature stress conditions. Under both conditions, the F1 hybrid genotype FB-SHAHEEN × JSQ WHITE GOLD followed by Ghuari-1, CCRI-24, Eagle-2 × FB-Falcon, Ghuari-1 × JSQ White Gold, and Eagle-2 exhibited better performance in response to high-temperature stress regarding the agronomic and fiber quality-related traits. The mentioned genotypes could be utilized in future cotton breeding programs to enhance heat tolerance and improve cotton yield and productivity through resistance to environmental stressors.

Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis

Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis

Assessing the Quality of Calyx of Physalis alkekengi L. var. franchetii Based on Quantitative Analysis of Q-Marker Combined with Chemometrics and Machine Learning Algorithms

Physalis alkekengi L. var. franchetii (PALF) is a traditional Chinese medicine, which is well known for its antimicrobial, anti-inflammatory, antipyretic, and expectorant properties. Its fruits and fruiting calyxes are used as dietary supplements and traditional herbs in China. However, the quality of calyxes is uneven, and it is prone to getting moldy or moth-eaten during storage. High-performance liquid chromatography (HPLC) fingerprints and multivariate chemometric methods were combined to evaluate quality, and three representative compounds were chosen as the quality markers (Q-markers). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) provided a clear discrimination of PALF samples. Through further verification by partial least squares discriminant analysis (PLS-DA), backpropagation artificial neural network (BP-ANN), machine learning, and combination with the determination of the content, biology, and pharmacology effect judgment, galuteolin, rutin, and physalin O could be used as Q-markers that their contents affect the quality of PALF grade evaluation. A simple method was established to rapidly assess the quality of PALF that is important for its clinical application and storage and provide a reference for evaluating the quality of materials used in Chinese medicine.