Transcriptome Temporal and Functional Analysis of Liver Regeneration Termination

BackgroundMicroRNAs are a class of small regulatory RNAs that modulate a variety of biological processes, including cellular differentiation, apoptosis, metabolism and proliferation. This study aims to explore the effect of miR-34a in hepatocyte proliferation and its potential role in liver regeneration termination.Methodology/Principal FindingMiR-34a was highly induced after partial hepatectomy. Overexpression of miR-34a in BRL-3A cells could significantly inhibit cell proliferation and down-regulate the expression of inhibin βB (INHBB) and Met. In BRL-3A cells, INHBB was identified as a direct target of miR-34a by luciferase reporter assay. More importantly, INHBB siRNA significantly repressed cell proliferation. A decrease of INHBB and Met was detected in regenerating liver.Conclusion/SignificanceMiR-34a expression was upregulated during the late phase of liver regeneration. MiR-34a-mediated regulation of INHBB and Met may collectively contribute to the suppression of hepatocyte proliferation.

Rheumatoid arthritis (RA) is one of the most prevalent chronic inflammatory and angiogenic diseases. The aim of this study was to evaluate the anti-inflammatory and anti-angiogenic activities in vitro of eight diterpenoids isolated from Daphne genkwa. LC-MS was used to identify diterpenes isolated from D. genkwa. The anti-inflammatory and anti-angiogenic activities of eight diterpenoids were evaluated on LPS-induced macrophage RAW264.7 cells and TNF-α-stimulated human umbilical vein endothelial cells (HUVECs) using hierarchical cluster analysis (HCA) and principal component analysis (PCA). The eight diterpenes isolated from D. genkwa were identified as yuanhuaphnin, isoyuanhuacine, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuagine, isoyuanhuadine, yuanhuadine, yuanhuaoate C and yuanhuacine. All the eight diterpenes significantly down-regulated the excessive secretion of TNF-α, IL-6, IL-1β and NO in LPS-induced RAW264.7 macrophages. However, only 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl markedly reduced production of VEGF, MMP-3, ICAM and VCAM in TNF-α-stimulated HUVECs. HCA obtained 4 clusters, containing 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, isoyuanhuacine, isoyuanhuadine and five other compounds. PCA showed that the ranking of diterpenes sorted by efficacy from highest to lowest was 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl, yuanhuaphnin, isoyuanhuacine, yuanhuacine, yuanhuaoate C, yuanhuagine, isoyuanhuadine, yuanhuadine. In conclusion, eight diterpenes isolated from D. genkwa showed different levels of activity in LPS-induced RAW264.7 cells and TNF-α-stimulated HUVECs. The comprehensive evaluation of activity by HCA and PCA indicated that of the eight diterpenes, 12-O-(2'E,4'E-decadienoyl)-4-hydroxyphorbol-13-acetyl was the best, and can be developed as a new drug for RA therapy.

Principal component analysis and hierarchical cluster analysis were applied to investigate physicochemical and instrumental textural properties of fresh kashar cheese. Four different principal components sufficiently explained the variability in the cheese samples. In addition, hierarchical cluster analysis was performed to group the kashar cheese samples regarding physicochemical and instrumental textural properties. Instrumental textural properties indicated greater variability than chemical composition of cheese samples. Principal component analysis revealed that color parameters were positively correlated with textural and chemical parameters. The results of this study revealed that other parameters rather than chemical composition would be effective on the instrumental textural properties. It was proved that principal component analysis was a very effective statistical tool to determine quality of cheese samples. According to the principal component analysis and hierarchical cluster analysis results, the attributes defining the kashar cheese samples were determined to be primarily the texture profile analysis parameters.

BackgroundCerebrovascular reactivity has been identified as a key contributor to secondary injury following traumatic brain injury (TBI). Prevalent intracranial pressure (ICP) based indices of cerebrovascular reactivity are limited by their invasive nature and poor spatial resolution. Fortunately, interest has been building around near infrared spectroscopy (NIRS) based measures of cerebrovascular reactivity that utilize regional cerebral oxygen saturation (rSO2) as a surrogate for pulsatile cerebral blood volume (CBV). In this study, the relationship between ICP- and rSO2-based indices of cerebrovascular reactivity, in a cohort of critically ill TBI patients, is explored using classical machine learning clustering techniques and multivariate time-series analysis.MethodsHigh-resolution physiologic data were collected in a cohort of adult moderate to severe TBI patients at a single quaternary care site. From this data both ICP- and rSO2-based indices of cerebrovascular reactivity were derived. Utilizing agglomerative hierarchical clustering and principal component analysis, the relationship between these indices in higher dimensional physiologic space was examined. Additionally, using vector autoregressive modeling, the response of change in ICP and rSO2 (ΔICP and ΔrSO2, respectively) to an impulse in change in arterial blood pressure (ΔABP) was also examined for similarities.ResultsA total of 83 patients with 428,775 min of unique and complete physiologic data were obtained. Through agglomerative hierarchical clustering and principal component analysis, there was higher order clustering between rSO2- and ICP-based indices, separate from other physiologic parameters. Additionally, modeled responses of ΔICP and ΔrSO2 to impulses in ΔABP were similar, indicating that ΔrSO2 may be a valid surrogate for pulsatile CBV.ConclusionsrSO2- and ICP-based indices of cerebrovascular reactivity relate to one another in higher dimensional physiologic space. ΔICP and ΔrSO2 behave similar in modeled responses to impulses in ΔABP. This work strengthens the body of evidence supporting the similarities between ICP-based and rSO2-based indices of cerebrovascular reactivity and opens the door to cerebrovascular reactivity monitoring in settings where invasive ICP monitoring is not feasible.

ABSTRACTPeppers (Capsicum annuum L.) are a vegetable that is widely cultivated in various regions of the world. Despite the economic importance of peppers, their commercialization is hindered by their limited postharvest durability, primarily due to moisture loss during storage. This study evaluated the effectiveness of different packaging methods and storage conditions in preserving the physicochemical and morphological quality of peppers during 21 days. Six treatments were tested, combining two types of packaging (thermo‐sealable and macro‐perforated) with two storage conditions (8°C/95% RH and 25°C/60% RH), plus an unpackaged control. Variables assessed included color, soluble solids, pH, pigments, dimensions, and mass loss. Data were analyzed using principal component analysis (PCA), hierarchical cluster analysis (HCA), and Kohonen neural networks (KNN). The first three principal components (PCs) explained 67.2% of total variance (PC1—40.88%, PC2—15.11%, PC3—11.17%). PC1 was strongly associated with mass and size losses (up to 73%), whereas PC2 and PC3 explained 77.4% of h* and 84.9% of C*, respectively. HCA and KNN revealed similar groupings. Samples stored at 8°C clustered together regardless of packaging, indicating minimal quality loss. At 25°C, unpackaged and macro‐perforated samples showed similar degradation. Thermo‐sealable packaging at 25°C formed a distinct cluster, indicating improved protection. This treatment also showed reduced quality losses, though not as effective as refrigeration. The agreement among PCA, HCA, and KNN confirms the reliability of findings. These results highlight the value of combining conservation strategies with multivariate tools to guide efficient, sustainable postharvest practices and extend shelf life in the pepper supply chain.Practical ApplicationThis study proposes a solution for the horticultural industry by combining heat‐shrink packaging and refrigeration for pepper preservation. This method significantly reduces physical and biochemical losses, extends shelf life, and maintains quality. It has the potential to transform the logistics of production and distribution, delivering fresh, high‐quality peppers. The use of advanced techniques like PCA and neural networks enables more informed and efficient decision‐making, allowing for customized preservation strategies. This approach meets the growing demand for fresh food, offering a sustainable, cost‐effective alternative for postharvest preservation, and may provide a competitive advantage in the global market.

Naphthalene (NAP) is found as a pollutant in water, soil, and air, and adsorption is the most prominent removal process of this compound, among the methods studied. A study concerning the types of adsorbents and the parameters with the greatest influence on the adsorption process is interesting to direct future works on new adsorbents. The use of multivariate data analysis tools becomes an appealing way to compile data obtained from bibliographic reviews and to establish a behavior in NAP adsorption. This work aims to evaluate the parameters with greater influence on NAP adsorption process regarding adsorption capacity (qeexp) with the principal component analysis (PCA), and to group common NAP adsorbents by chemical characteristics through hierarchical cluster analysis (HCA). The variables qeexp, S, [NAP]0, T, CT, and [Ads] were used to perform PCA with correlation matrix. For the HCA, the variables S, [NAP]0, T, CT, and [Ads] with average linkage method (UPGMA) and Euclidean distance were used. Through PCA, it is possible to infer that S and [NAP]0 are the factors with greater influence in qeexp of NAP, while T, CT, and [Ads] have little correlation. PCA also shows that activated charcoal is the adsorbent with higher qeexp. HCA grouped the adsorbents into four groups by their chemical classes, except group A. Both PCA and HCA methods show themselves as potential tools to evaluate a data set of NAP adsorption processes.

In France, alluvial aquifers provide 45% of the freshwater used for drinking, agriculture, and industry (Maréchal and Rouillard, 2020). These aquifers are often hydraulically connected to rivers and are particularly vulnerable due to their proximity to the surface. This proximity makes them sensitive to anthropogenic pressures both quantitatively and qualitatively. The current study aims to better understand groundwater-surface water interactions in an anthropized alluvial aquifer in the lower Rhône Valley. This goal will be achieved using multivariate statistical methods on hydrogeochemical and stable water isotope data. Two datasets were analyzed. One dataset contained inert tracers (Cl−, Br−, δ2H and δ18O) and included 667 water samples (40 rainwater, 110 surface water and 517 groundwater). The other dataset contained major and minor ions (Ca2+, Mg2+, K+, Na+, Cl−, SO42-, alkalinity, NO3−, Br− and U(VI)) and stable water isotopes (δ2H and δ18O) data., and included 374 water samples (37 surface water and 337 groundwater). First, a hierarchical cluster analysis (HCA) was applied to both datasets to better understand groundwater recharge and the geochemical processes that control groundwater chemistry in the study area. We used the recently developed t-distributed stochastic neighbor embedding (t-SNE) (Van Der Maaten and Hinton, 2008) method and principal component analysis (PCA) to assist with the cluster analysis and visualization. When HCA, PCA, and t-SNE were applied to inert tracers dataset, the results first revealed the distribution of groundwater on the study site between two recharge sources: the Rhône River and rainfall. Water samples collected along the Rhône River boundary are characterized by highly depleted δ2H and δ18O signatures, indicating the significant influence (up to 80%) of the Rhône on the recharge of the alluvial aquifer in this area. In contrast, groundwater samples collected in the northern and northwestern parts of the site showed highly enriched δ2H and δ18O signatures, similar to those of rainfall indicating dominant recharge from local precipitation. Others water samples are characterized by intermediate δ2H and δ18O signatures, falling between the signatures of rainfall and the Rhône River, indicating mixed recharge. These water samples are predominantly distributed in the southern part of the site. Second, the results revealed the response of the alluvial groundwater to exceptional climatic events. In 2022, particular isotopic signatures with higher deuterium excess and high chloride concentrations were observed in rainwater and the Rhône River, which are reflected in groundwater. This suggests an influence of continental air masses originating from the Sahara Desert (Xu-Yang et al., 2025). When HCA, PCA, and t-SNE were applied to the second dataset containing hydrogeochemical and stable water isotope data, the results identified water samples with high uranium and chloride concentrations, likely due to historical pollution. This study shows that t-SNE is a promising tool for assessing groundwater-surface water interactions in an alluvial aquifer when used to assist in cluster analysis. Compared with PCA, t-SNE can better identify hidden information and perform much better with complex, nonlinear hydrogeochemical, and stable water isotope data. ReferencesMaréchal and Rouillard, 2020.https://doi.org/10.1007/978-3-030-32766-8_2Van Der Maaten and Hinton, 2008.Res.9,2579–2625.Xu-Yang, et al., 2025.https://doi.org/10.1126/sciadv.adr9192

The selection of superior strawberry genotypes is a complex process due to the high variability after hybridization that is caused by the octoploid nature and the heterozygosis, making the selection of multiple traits difficult. This study aimed to select strawberry hybrids with the potential for fresh consumption and/or processing by applying multivariate analysis to obtain traits of interest simultaneously. Hybrids were obtained from the crossing among seven commercial cultivars, defining a selection of 10% of them. The experimental design consisted of an augmented block design, with two commercial cultivars, Camarosa and Camino Real, as the controls. Different variables, including the number and average mass of commercial fruits, total fruit mass, pH, soluble solids (SS), titratable acidity (TA), SS/TA ratio, reducing sugars, pectin, ascorbic acid, phenolic compounds, and anthocyanin’s, were assessed. The selection of hybrids was based on the Mulamba and Mock rank-summation index, principal component analysis, and Ward’s hierarchical cluster analysis. The selection index was based on different weights being adopted for fresh market and processing. The assessed traits had high variability between hybrids. The highest selection gains were obtained for production traits, but the different weight assignment resulted in different classifications of hybrids for both fresh consumption and processing. Most of the hybrids selected by the index remained in the same group in the principal component and hierarchical cluster analyses, which indicates that multivariate analysis is a valuable tool for assisting in the selection of superior hybrids in the strawberry crop.

The current research aimed to assess the 179 BC2 population of alfalfa (Medicago sativa L. cv. Huaiyin) and PL34HQ multifoliate leaves and their parents to select genotypes based on morphological traits for future alfalfa breeding programs. A backcross breeding was carried out between the recurrent male parent and alfalfa PL34HQ as the non-recurrent female parent of alfalfa. In the pod setting stage, morphological traits were measured in the BC2 populations. Data were analyzed for descriptive statistics, correlation, principal component (PCA), and agglomerative hierarchical cluster analyses. The result of descriptive statistics revealed high variation in the means, minimum, and maximum values for most of the morphological traits. Therefore, high variability was observed in plant height (PH) 26-98 cm, number of branches per plant (NBP) 6-74, stem diameter (SD) 1.46-5.46 mm, second branch length SBL 0-62 cm, number of multifoliate leaves per plant (NMFLP) 0-95.7%, number of multifoliate leaves on the main branch (NMFLMB) 0-90.6%, number of nodes (NN) 12-24, fresh shoot weight (FSW) 3.10-359.4 g, and dry shoot weight (DSW) 0.40-108.6 g. The results showed that the comprehensive traits of the BC2 population tended to the recurrent parent, about 32.40% of the total population. However, many plants tended to the non-recurrent parent, about 67.40% of the total population, with a high multifoliate rate. Correlation analysis revealed a significant positive association of plant height with stem diameter, fresh shoot weight, dry shoot weight, second branch length, number of nodes, and branch number. Principal component analysis (PCA) discovered the first three components with an eigenvalue greater than one, contributing 78.98% of total variability for different traits. The PC1 showed favorable factor loading for all morphological traits. The contribution of dry shoot weight, fresh shoot weight, stem diameter, and plant height was highest in principal component 1. The average hierarchical cluster analysis method grouped all the genotypes into four main clusters. Alfalfa multifoliate leaf genotype clustering based on morphological traits would help identify potential genotypes for future breeding research and programs. Keywords: - Alfalfa multifoliate leaves, Principal component analysis, Correlation, Cluster analysis.

Ultra performance liquid chromatography coupled with principal component and cluster analysis of Swertia chirayita for adulteration check

The present study was carried out during summer and rainy (kharif) seasons of 2019 and 2020 at the research farm of Himachal Pradesh Krishi Vishvavidyalaya, Palampur, Himachal Pradesh to study the genetic diversity among 43 bacterial wilt tolerant genotypes of bell pepper (Capsicum annuum L. var. grossum Sendt), focusing on 12 quantitative and 3 qualitative traits along with disease resistance. The experiment was laid out in a randomized complete block design (RCBD) with 3 replications. Following hierarchical cluster analysis, the 43 unique genotypes were categorized into 5 separate clusters. Cluster I consisted of maximum number of genotypes (39), followed by clusters II, III, IV and V, each having only one genotype. The greatest inter-cluster distance was observed between cluster II and V (3090.70), while the smallest distance was noted between cluster II and III (1299.21). Cluster I displayed the greatest intra-cluster distance (803.75), while clusters II, III, IV and V showed the lowest distances (0.00). The primary contributors to genetic divergence were found to be capsanthin content (23.4%), followed by ascorbic acid (21.4%), fruit length (12.5%), plant survival (11.1%) and marketable fruit yield/plant (10.9%). Principal component analysis indicated that the initial six principal components, each possessing an eigen value surpassing one, jointly accounted for 70.62% of the total observed variability. PCA biplot revealed that, the traits namely, marketable fruits/plant, average fruit weight, harvest duration and plant height were the major yield contributing traits. The results underscored the importance of genetic characterization and the selection of diverse genotypes for hybridization, facilitating the development of improved bell pepper cultivars with enhanced productivity and quality.

Microarray gene expression profiling and bioinformatics analysis of premature ovarian failure in a rat model

This study aims to characterize and compare the chemical composition of patchouli oil from various regions in Aceh, Indonesia, using Fourier-transform Infrared Spectroscopy (FT-IR) combined with Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Patchouli oil samples from five distinct locations within West Aceh and Aceh Jaya Regencies were analyzed, focusing on the 1800-500 cm⁻¹ range. The FT-IR spectra revealed significant chemical markers, with notable absorption bands corresponding to aliphatic C-H, C=O, and C=C stretching vibrations. Multivariate analyses showed distinct clustering patterns, with four samples (POSM, POSP, POT, and POW) exhibiting high chemical similarity and clustering together, while one sample (POP) formed a separate cluster, indicating a lower degree of similarity. PCA analysis of the original spectra in the 1800-500 cm⁻¹ range yielded a cumulative PC1 and PC2 value of 89%. The HCA dendrogram highlighted a significant relative distance of 10 between the POP sample and the other four samples, indicating minimal relatedness, with the closest relatedness observed between POSM and POSP, having a relative distance of 1.1. This study demonstrates the effectiveness of FT-IR spectroscopy combined with PCA and HCA in distinguishing chemical profiles of patchouli oil from different regions, providing insights into geographical and varietal influences on oil composition. The findings are crucial for quality control and standardization in the patchouli oil industry, with further molecular studies recommended to corroborate and enhance understanding of genetic diversity among patchouli varieties in Aceh.

In the normal casework of Forensic Document Examiners (FDEs), a common type of forgery is the alteration of the date of creation of a document through the erasure and addition of another date with ballpoint pen of color similar to the original. The chronology of manuscripts in a document has been a concern within the FDE community. Recently, Infrared spectroscopy (IR) and multivariate statistical analysis have been used to differentiate pens, but this paper aimed to develop an IR method that could also date a manuscript by multivariate statistic. Ballpoint pen lines from different brands made in different kinds of papers (office white and recycled), with variable ages, were analyzed by Infrared Spectroscopy with Total Attenuated Reflectance Accessory. The spectra obtained were analyzed with multivariate statistical analysis, especially Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). This study concludes that different kinds of paper do not influence the characterization of the pens by IR-ATR. The method was able to characterize the variables that were responsible for the discrimination of ballpoint pens from different brands/models and for differentiation of samples of different ages. The HCA and PCA were able to group pens with similar ages, and differentiate lines recently wrote from older lines. Within the loadings that are responsible for the differentiation, it was possible to identify absorption peaks from 2-phenoxyethanol, a solvent whose evaporation from the ink could indicate the line age. Further studies regarding other pen samples and multivariate statistical approaches could be conducted to confirm these findings.

Numerous studies indicate mineral deficiencies in people on a gluten-free (GF) diet. These deficiencies may indicate that GF products are a less valuable source of minerals than gluten-containing products. In the study, the nutritional quality of 50 GF products is discussed taking into account the nutritional requirements for minerals expressed as percentage of recommended daily allowance (%RDA) or percentage of adequate intake (%AI) for a model celiac patient. Elements analyzed were calcium, potassium, magnesium, sodium, copper, iron, manganese, and zinc. Analysis of %RDA or %AI was performed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Using PCA, the differentiation between products based on rice, corn, potato, GF wheat starch and based on buckwheat, chickpea, millet, oats, amaranth, teff, quinoa, chestnut, and acorn was possible. In the HCA, four clusters were created. The main criterion determining the adherence of the sample to the cluster was the content of all minerals included to HCA (K, Mg, Cu, Fe, Mn); however, only the Mn content differentiated four formed groups. GF products made of buckwheat, chickpea, millet, oats, amaranth, teff, quinoa, chestnut, and acorn are better source of minerals than based on other GF raw materials, what was confirmed by PCA and HCA. © 2017 Society of Chemical Industry.