Principal Component Cluster Analysis Research Articles

Population structure and risk assessment of invasive species Asystasia gangetica in urban area of Bandung, West Java, Indonesia

Abstract. Rosleine D, Khalishah AR. 2024. Population structure and risk assessment of invasive species Asystasia gangetica in urban area of Bandung, West Java, Indonesia. Biodiversitas 25: 2374-2381. Asystasia gangetica (L.) T. Anderson is a weed from Africa that has a high adaptation to wide ecological conditions. This research aims to assess the population structure of A. gangetica in Bandung City, West Java, Indonesia and to conduct risk assessment to determine management decisions. This research was conducted from January to February 2023 by establishing a total of 106 plots with size of 1 m2 to measure the population structure at adult and juvenile stages, and number of fruits. Plot sized 0.4 m2 was used to count the seed in a 10 cm soil depth. A risk assessment was conducted, which consisted of 12 questions of risk analysis (invasiveness, impact, and potential distribution) and 20 questions of management feasibility (control cost, distribution, and persistence). The results of principal component analysis and clustering analysis show three groups based on population structure (PC1 49.12% and PC2 28.44%). The first cluster consists of three sub-districts and shows a set of early colonized populations indicated by low juvenile stage, while the second cluster (14 sub-districts) and third cluster (13 sub-districts) show more established population yet still recently colonized, which is indicated with medium-high juvenile and reproductive (fruit) stage. The risk assessment results in 91.2 of the risk analysis score (medium) and 169.7 of the management feasibility score (negligible), implying the need to manage the site. Nevertheless, its wide-range of habitat tolerant and population characteristics potentially threaten biodiversity and harm the agricultural sector at once. Therefore, early countermeasure is needed as an effort to minimize greater risks in the future.

Nutritional Quality Assessment of Miscellaneous Cassava Tubers Using Principal Component Analysis and Cluster Analysis.

Cassava is a staple crop in developing countries because its starchy roots provide essential dietary carbohydrates. The aim of this research was to conduct a comprehensive inquiry and scientific evaluation of the nutritional value of cassava tubers. Eight nutritional characteristics were examined in native and imported cassava variants: starch, reduced sugar, anthocyanins, protein, dietary fiber, quinic acid, vitamin C, and dry matter content. Principal component analysis (PCA) was conducted to minimize the dimensionality of the nutritional markers. A scientific assessment technique was developed to calculate a composite score for the various cassava samples. Analysis of the data revealed noticeable variance among the samples' nutritional indicators, suggesting varying degrees of association. Starch had a substantial positive link with lower sugar, protein, and dry matter content (p < 0.01). Anthocyanins and quinic acid interacted favorably (p < 0.05), and a positive link between protein and dry matter content was observed (p < 0.05); however, protein and dietary fiber interacted negatively (p < 0.05). The contribution rate of the top three PCA factors was over 76%, demonstrating that these factors incorporated the primary information acquired from the eight original nutritional indices, while maintaining excellent representativeness and impartiality. The experimental results showed a preliminary nutritional grade for 22 cassava tuber samples. The top five types were Guangxi Muci, Gui Cassava 4, Glutinous Rice Cassava, Huifeng 60, and Dongguan Hongwei. In the cluster analysis, the levels of similarity between the data showed that the 22 types of cassava tubers could be grouped into five categories, each with their own set of nutrients. This study promotes the directed breeding of cassava species and offers a theoretical foundation for creating and using various cassava varieties. Furthermore, this work lays the groundwork for a systematic and dependable technique for the quality assessment, comprehensive evaluation, and reasonable classification of cassava species and similar crops.

Differential analysis of aqueous humor metabolomics between presenile cataract and senile cataract

Objective: To explore the differences in metabolites and metabolic pathways in the aqueous humor between patients with presenile cataracts and senile cataracts. Methods: This metabolomic study was conducted at Tianjin Medical University Eye Hospital from August 2020 to September 2022. Eight patients with presenile cataracts (8 eyes) and 8 patients with senile cataracts (9 eyes) were included. Data were collected, including age, gender, preoperative uncorrected visual acuity, intraocular pressure, lens dysfunction index, and axial length. Aqueous humor and anterior capsule tissue samples were obtained during cataract surgery. Metabolites in the aqueous humor were detected using Liquid Chromatography-Mass Spectrometry in a non-targeted approach. The principal component analysis, differential analysis, clustering analysis, and correlation analysis were performed to identify differentially expressed metabolites. These metabolites were ranked based on the fold change (FC). The receiver operating characteristic (ROC) curve analysis and metabolic enrichment analysis were used to identify differential pathways and potential biomarkers for presenile cataracts. Immunohistochemistry was conducted on anterior capsule tissues, and pyruvate levels were measured by colorimetry to validate metabolomic results. Results: Patients with presenile cataracts included 7 males and 1 female, with a mean age of (37.50±4.90) years. Patients with senile cataracts were 7 males and 1 female, with a mean age of (73.44±5.22) years. Except for age, there were no significant differences in baseline data (P>0.05). A total of 347 differential metabolites were identified, 10 of which were potential biomarkers for presenile cataract according to the ROC curve analysis (all P<0.05), including propoxycaine (log2FC=7.26), 2-methyl-2, 3, 4, 5-tetrahydro-1, 5-benzodiazepine-4-ketone (log2FC=6.35), l-pyroglutamic acid (log2FC=-1.72), leanly-proline (log2FC=-0.77), and choline (log2FC=-0.56) in the positive ion mode, and N-phenylacetyl glutamine (log2FC=-1.84), pyruvate (log2FC=1.07), ascorbic acid (log2FC=0.92), pseudouracil nucleoside (log2FC=-0.68), and palmitic acid (log2FC=-0.51) in the negative ion mode. The metabolic enrichment analysis identified 72 differential pathways (32 cationic and 40 anionic), with significant differences in glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle (P<0.05). The experimental validation showed reduced lactate dehydrogenase and increased pyruvate levels in patients with presenile cataracts (P<0.05). Conclusions: Pyruvate and nine other metabolites may serve as potential biomarkers for presenile cataracts. Pathways involving glutathione metabolism, cysteine and methionine metabolism, glycolysis or gluconeogenesis, pyruvate metabolism, and the citric acid cycle are notably dysregulated in patients with presenile cataracts.

Distribution Characteristics and Assessment of Heavy Metal Pollution in Sediments of Fengyuan River, Guangdong

Mining activities and industrial production discharges often generate heavy metal pollution and pose serious environmental impacts. It is of great significance to study the pollution status and sources of heavy metals in polluted rivers for environmental pollution control. This paper determines the concentration of 34 heavy metals, including As, Cd, Cr, Cu, Ni, Pb, and Zn, in the Fengyuan River and tailing pond in Yunfu City, Guangdong Province. The potential ecological risk index (RI) and integrated pollution index (IPI) were used to evaluate the spatial distribution and pollution levels of heavy metals, and the possible source and enrichment of heavy metal were identified and assessed using enrichment factor (EF). The results showed that Cd and Tl in the slag of the tailings pond were the most serious; The potential ecological risk index indicates that Cd in the sediment of the Fengyuan River is a very severe ecological risk, As and Pb caused heavy and moderate ecological risk, respectively, while, Co, Cr, Cu, Mn, Ni, V, and Zn constituted mild ecological risks; The integrated pollution index shows that all the sampling sites are above high pollution. The average enrichment factor of Cd in surface sediments reached 28.11. The EF values of As, Cu, Mn, Pb, and Zn were all greater than 1. The RI, IPI, and EF produced similar levels of heavy metal pollution in sediments, thereby confirming each other’s results, indicating that the Fengyuan River is heavily contaminated, and As, Cd, Mn, Pb, and Zn were likely to have an effect on the aquatic ecosystem. According to principal component and hierarchical cluster analyses, the Fengyuan River may contain heavy metals from a variety of sources, such as tailings ponds, industrial parks, chemical effluent, and more. In addition, correlation analysis indicates that total organic carbon is significantly correlated with the concentrations of As and Cr, respectively, and combining EF suggests that cluster 1 containing As, Mn, and Pb and cluster 2 containing Cd and Zn may be derived from similar anthropogenic sources. This study can provide meaningful basic information for environmental development planning and decision-making associated with human health in the region.

Evaluation of Genetic Variability in Different Genotypes of Finger Millet (Eleusine coracan L.) through Path and Multivariate Analysis

The experiment was carried out using a Randomized Block Design (RBD) with three replicates. The trials were entirely composed of fifty-two genotypes for yield and their contributing characters for 13 different characters. For every attribute under study, an analysis of variance showed a highly significant difference. Among the genotypes studied, the evaluated characteristics showed varying degrees of heritability, genetic advancement, and variability. Both the genotypic and phenotypic coefficients of variation (PCV and GCV, respectively) ranged from low to high. The modest GCV and PCV values particular for the percentage of fingers per ear (17.47% and 17.50%), ear weight per plant (g) (13.42% and 16.28%), and 1000 seed weight (g) (11.79% and 14.09%), were observed respectively. Plant height (cm) (81.60%, 28.47) had the greatest wide sense heritability value, with the highest genetic advance, followed by ear weight per plant (g) (67.95%, 20.13). Thus, the findings of this study indicate that these genotypes have diversity in yield and other yield-related features, which should be exploited in subsequent breeding. The correlation and path coefficient analyses for yield and yield characteristics showed that grain yield per plant showed a strong positive relationship both at the genotypic and phenotypic levels, with days to 50% flowering, days to maturity, ear weight per plant, and harvest index. Path analysis exhibited that for genotypic and phenotypic coefficients, harvest index (0.33, 0.46) and ear weight/plant (0.86, 0.39) showed the most positive direct effects. These characteristics might thus be applied as selection criteria to identify finger millet genotypes that show promising results in future breeding. Multivariate approaches such as principal component analysis and cluster analysis are crucial statistical tools for examining genetic diversity in plant breeding programs, as are their significant quantitative features. Based on principal component analysis, the entire variance was provided by six main components, of which PC-1 and PC-2 contributed 21.97% and 18.32%, respectively, to the total variability. Euclidean distance was used to divide the fifty-two genotypes into five groups. The largest number of genotypes (twenty-two) in Cluster I was followed by Cluster III, with 16 genotypes. As a result, genotypes from these clusters may be used as parents for hybridization.

Exploring rice genotypes for anaerobic germination: Towards sustainable direct seeding

Direct-seeded rice (DSR) production is the primary focus of this study, which explores the diversity of rice genotypes concerning their ability to withstand anaerobic germination conditions. Using advanced statistical analysis, a total of 250 rice cultivars were assessed for several anaerobic germination attributes. Significant variability was observed among genotypes for key traits including germination % (ARC14855, AG 387), shoot length and seedling dry weight, indicating potential donors for anaerobic germination tolerance. Trait association analysis, variability analysis, principal component analysis and cluster analysis were employed to uncover the genetic and phenotypic factors influencing anaerobic germination tolerance. The genotypes of cluster III could be used as donors for anaerobic germination studies, as the genotypes have revealed high average values for all the traits. The findings underscore the importance of genetic variability and heritability in breeding resilient rice varieties suited for DSR cultivation, offering valuable insights for enhancing rice cultivation practices in the face of challenges such as water scarcity and labour shortages. To improve the sustainability and productivity of paddy production systems, this research advances rice breeding procedures that aim to create varieties tolerant to anaerobic conditions.

Tracing sources of dissolved organic matter along the terrestrial-aquatic continuum in the Ore Mountains, Germany

There is growing concern about the rising levels of dissolved organic matter (DOM) in surface waters across the Northern hemisphere. However, only limited research has been conducted to unveil its precise origin. Compositional changes along terrestrial-aquatic pathways can help determine the terrestrial sources of DOM in streams. Stream water, soil water and soil horizons were sampled at four sites representing typical settings within a forested catchment in the Ore Mountains (Erzgebirge, Germany) from winter 2020 to spring 2022. The samples were analyzed using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). The resulting data were successfully subjected to semi-automatic processing of the molecular composition of DOM, reaching a percentage of identified peaks up to 98 %. Principal component analysis (PCA) and cluster analyses were carried out to identify distinct differences between DOM from the potential sources and in the streams. According to the PCA, organic soil horizons, soil water, and stream water samples could be clearly distinguished. Cluster analysis revealed that soil water DOM at all depths of Peats and deeper horizons of the Peaty Gleysols contributed the most to DOM in the stream section dominated by organic soils. In areas dominated by mineral soils, stream DOM resembled the DOM from the deeper mineral horizons of Cambisols and Podzols. Overall, our results suggested that most of the DOM exported from the catchment was derived from deeper mineral soil horizons, with little contribution of DOM derived from organic soils. Therefore, DOM fingerprint analysis of in-situ soil water proved to be a promising approach for tracing back the main sources of stream water DOM.

Comprehensive index analysis approach for ecological and human health risk assessment of a tributary river in Bangladesh

This study examined the water quality of the Turag River, an important tributary river in Dhaka, Bangladesh in terms of physicochemical characteristics and heavy metal contamination to assess the potential risks to both ecological systems and human health. The majority of the water samples complied with the acceptable limits established by the World Health Organization (WHO) for various parameters including pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (DO), chemical oxygen demand (COD), sodium adsorption ratio (SAR), and magnesium adsorption ratio (MAR), except total hardness (TH). The sodium (Na), potassium (K), calcium (Ca), magnesium (Mg), chloride (Cl−), fluoride (F−), nitrate (NO3−), and sulfate (SO42−) levels in the water samples were found to be within acceptable ranges for most cases. Moreover, heavy metals including lead (Pb), cadmium (Cd), chromium (Cr), nickel (Ni), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), arsenic (As), selenium (Se), and mercury (Hg) were analyzed and their mean concentrations (μg/L) were found in the order of Fe (244.72 ± 214.35) > Mn (28.93 ± 29.64) > Zn (22.97 ± 10.93) > Cu (8.28 ± 5.99) > Hg (8.23 ± 6.58) > As (1.34 ± 0.39) > Ni (1.20 ± 0.38) > Cr (0.67 ± 0.85) > Pb (0.61 ± 0.72) > Se (0.42 ± 0.48) > Cd (0.13 ± 0.09) which were within the acceptable limit, except Hg. The cumulative effect of all heavy metals was assessed through the heavy metal pollution index (HPI), contamination degree (Cd), and nemerow pollution index (PN). The mean value of HPI (682.38 ± 525.68) crossed the critical index value of 100, indicating an elevated level of pollution. The mean value of Cd (8.763 ± 6.48) indicates a low-moderate-significant level of contamination due to an elevated level of Hg, and for the PN it was found 174.27 ± 146.66, indicating a high level of pollution due to high level of Fe. Ecological risk index (ERI) indicated low levels of risk for Pb, Cd, Cr, Ni, Fe, Mn, As, Se, Cu, and Zn but a significantly high risk for Hg. The water was classified as good to excellent based on its physicochemical properties (pH, EC, TDS, COD, DO, F−, Cl−, NO3−, and SO42−) while it was deemed poor to unsuitable for heavy metals according to the water quality index (WQI). Among the carcinogenic constituents, As poses the greatest carcinogenic risk, particularly for children. The mean value of Cr, Mn, and As in the HQingestion for adult and child, and Cd, Hg for child exceeded the threshold value established by the United States Environmental Protection Agency (USEPA), while the HQdermal values remained below the maximum limit for all heavy metals. The value of HI at all locations exceeds the threshold of 1, as specified by USEPA. Principal component analysis (PCA) and cluster analysis revealed that the presence of heavy metals in the Turag River was mainly attributed to anthropogenic sources, including industrial effluent discharge from neighboring industries, domestic wastewater, and agricultural runoff containing agrochemicals from the surrounding lands.

Diversity study of Beauveria bassiana species for finding the most virulent strain to manage Bemisia tabaci in cotton

Beauveria bassiana (Bal.-Criv.) is an important entomopathogenic fungus being used for the management of various agricultural pests worldwide. However, all strains of B. bassiana may not be effective against whitefly, Bemisia tabaci, or other pests, and strains show diversity in their growth, sporulation, virulence features, and overall bioefficacy. Thus, to select the most effective strain, a comprehensive way needs to be devised. We studied the diversity among the 102 strains of B. bassiana isolated from 19 insect species based on their physiological features, virulence, and molecular phylogeny, to identify promising ones for the management of B. tabaci. Strains showed diversity in mycelial growth, conidial production, and their virulence against B. tabaci nymphs. The highest nymphal mortality (2nd and 3rd instar) was recorded with MTCC-4511 (95.1%), MTCC-6289 (93.8%), and MTCC-4565 (89.9%) at a concentration of 1 × 106 conidia ml−1 under polyhouse conditions. The highest bioefficacy index (BI) was in MTCC-4511 (78.3%), MTCC-4565 (68.2%), and MTCC-4543 (62.1%). MTCC-4511, MTCC-4565, and MTCC-4543 clustered with positive loading of eigenvalues for the first two principal components and the cluster analysis also corresponded well with PCA (principal component analysis) (nymphal mortality and BI). The molecular phylogeny could not draw any distinct relationship between physiological features, the virulence of B. bassiana strains with the host and location. The BI, PCA, and square Euclidean distance cluster were found the most useful tools for selecting potential entomopathogenic strains. The selected strains could be utilized for the management of the B. tabaci nymphal population in the field through the development of effective formulations.Key points• 102 B. bassiana strains showed diversity in growth and virulence against B. tabaci.• Bioefficacy index, PCA, and SED group are efficient tools for selecting potential strains.• MTCC-4511, 4565, and 4543 chosen as the most virulent strains to kill whitefly nymphs.

Combined effects of cultivar, extraction technology, and geographic origin on physicochemical traits of Moroccan olive oil as revealed by multivariate analysis

This study aimed at evaluating physicochemical traits of olive oil (‘Haouzia’ and ‘Moroccan Picholine’) according to extraction technologies (ETs: super-pressure SP, 2-phase 2 P, and 3-phase 3 P) across eight climatically contrasted Moroccan agroclimatic areas (AA). Our samples were classified as extra virgin olive oil (EVOO), whose traits varied (p < 0.05) among ETs, cultivars, and AA. EVOO from SP had high records of basic indices, most phytosterols, and trans fatty acids (TFA) due to partial oxidation during extraction, while continuous ETs (2 P and 3 P) displayed a better oil quality as demonstrated by routinely measured parameters. ‘Haouzia’ had the greatest saturated (SFA), monounsaturated (MUFA), polyunsaturated fatty acids (PUFA), and TFA. However, it had a lesser stability revealed by a low MUFA/PUFA and oleic/linoleic (O/L) as compared to ‘Moroccan Picholine’. Arid climatic areas had the highest palmitoleic, oleic acid, MUFA, MUFA/PUFA, and O/L and therefore the best oil stability as compared to the remaining agroclimatic areas. These variations were confirmed by principal component analysis, cluster analysis, and artificial neural networks. Important simple and multiple regression models with strong correlations were highlighted among the studied variables. In conclusion, environmental conditions have a key role to be considered when assessing olive oil quality and authenticity.

Soil pollution and elemental sources along Barapukuria coal mine, Bangladesh: Implications for eco-environmental and health risk assessment

For the first time, different pollution indices and a receptor model have been used to quantify eco-environmental and health risk assessments as well as identify the sources of potentially toxic elements in soil along the Barapukuria Coal Mine (BCM). Individual indices include enrichment and contamination factors showing the soil samples are moderately to highly contaminated by arsenic, cobalt, chromium, copper, lead, and zinc and heavily contaminated by sulfur. According to the geo-accumulation index, there is significant pollution with arsenic (1.24 ± 0.38), lead (1.49 ± 0.58), cobalt (1.49 ± 0.58), and sulfur (1.63 ± 0.38). Modified hazard quotient and ecological risk factor values also suggest low to moderate environmental risk hazards from the same elements. The nemerow pollution index, pollution load index, nemerow risk index, ecological risk index, and toxic risk index of soil range from 1.65 to 3.03, 0.82–1.23, 11–26, 77–165, and 6.82–11.76 suggest low toxic risk and moderate pollution, among other synergistic indices. Health risk assessment indicates that iron poses lower cancer risk for children than adults, while both face unacceptable cancer risks from inhaling chromium, cobalt, or arsenic. Principal component and phylogenetic cluster analysis extracted by the multiple linear regression with the absolute principal component score (APCS-MLR) model refer to the fact that manganese, iron, titanium, and nickel have originated from geogenic sources, while coal mine effluents enrich elements like arsenic, chromium, zinc, lead, uranium, sulfur, thorium, and zinc and phosphorous sourced from agriculture. In addition, geogenic and anthropogenic sources, including mine and agriculture activities, could potentially pollute the soil and ecosystem. The findings are crucial for regional and national planners in devising strategies to mitigate potentially toxic element pollution in soil along coal mine areas.

ASSESSMENT OF PHYSICOCHEMICAL PARAMETERS AND METAL CONTAMINATION IN SEDIMENTS OF THE PERI-URBAN AMAGBA-OKOROMA RIVER, BENIN CITY, NIGERIA.

The key role of sediments in aquatic systems is widely known, sediments are frequently neglected as a factor in the evaluation of water quality assessment. This study assessed the sediment quality of the Peri-urban Amagba-Okoroma River, Benin City, Nigeria, emphasizing physicochemical parameters and heavy metal contamination. Three sites (Upstream site 1, Midstream site 2 and Downstream site 3) were studied and river sediments were collected from each of these sites using Van veen grab. The sediments were collected three times each in the month of March and July, 2023. The results revealed temperature (25.67°C-26°C), slightly alkaline pH (7.55-7.68), and low electrical conductivity (49.33µS/cm-54.67µS/cm). Heavy metals evaluated were all within threshold/probable effect level guideline: Cd (0.09-0.10 mg/kg), Cr (5.61-6.10mg/kg), Cu (18.62-19.90mg/kg), Pb (0.33t-0.37mg/kg), and Zn (32.47-35.01mg/kg). However, Cu slightly exceeded thresholds-effect level limits at midstream site 2 and downstream site 3, indicating localized pollution sources. Principal component analysis and cluster analysis identified significant correlations between Cd-Cr and Cu-Zn, suggesting shared pollution sources likely from industrial or agricultural runoff. Cluster analysis reveal that sites 2 and 3 are more similar compare to site 1 (upstream). The heavy metal pollution indices collectively indicated no sediment contamination across the sites. Geo-accumulation index values for Cd, Cr, Cu, Pb, and Zn suggested unpolluted conditions across all sampling sites. Contamination factor values corroborated these findings, indicating low contamination levels for all metals. Contamination degree values further confirmed the sediment's low degree of contamination, aligning with Pollution load index values, indicating non-pollution. This study underscores the significance of regular monitoring and targeted mitigation efforts to sustain the ecological health of the river ecosystem amidst potential anthropogenic pressures.

Characterization And Genetic Relationship of Several Tomato (Solanum lycopersicum L.) Genotypes from Various Regions

The research aims to obtain information about the morphological characters and genetic relationships of several tomato genotypes from various regions. The study was conducted from May to October 2023 at the Experimental Garden and Plant Breeding Laboratory, Faculty of Agriculture, University of Riau. The research used a randomized block design (RBD) consisting of eight genotypes and four repeats, and 32 experimental units were obtained. The observations were made to observe some qualitative characters. Qualitative data were analyzed using principal component analysis and cluster analysis to see genetic relationships. The results showed that the Magelang genotype (T27) had a high average plant fruit weight of 619.74 g, the Pane tongah (T24) had an average thick fruit flesh of 6.05 mm, the Silimakuta (T22) had an average large fruit size of 63.28 g. In contrast, the Purba (T25) had an average number of large planting fruits of 15.41 pieces. Grouping eight tomato genotypes based on clustering of MC1 and MC2 and cutting the dendrogram at a 90% similarity rate resulted in six groups. Group One consists of three genotypes, specifically Silimakuta (T22), Alahan Panjang (T26), and Magelang (T27). Group Two has Raya (T23), Group Three has Purba (T25), Group Four has Pane tongah (T24), group five has Servo F1 and group six has Ratna. The closest kinship based on the smallest euclidiean distance value is the Silimakuta (T22) and Alahan panjang (T26), with a value of 19.44. The genotypes with the farthest similarity are Raya (T23) and Purba (T25) because they have a 46.24 genetic distance

Ecogeography and Climate Change in Forage Grasses from Arid and Semi-Arid Regions of Mexico

Arid and semi-arid areas are highly productive ecosystems that have a great diversity of species, among which grasses stand out. These species have ecological, environmental, and economic importance. The objective was to identify the ecological descriptors of 15 native species of grasses and diversity patterns through multivariate analysis techniques (principal component analysis, PCA, and cluster analysis, CA), in addition to identifying potential distribution areas (current and future) and regions of high and low diversity from 3841 accessions and 21 climatic variables. Among the main results, the extreme values, coefficient of variation, and median for each species could be determined. PCA determined that, with 10 variables, it was possible to explain 54.36% of the variation between the analyzed species. CA resulted in the formation of four statistically significant groups, with specific climatic characteristics. Regarding potential distribution areas and climate change, a reduction in distribution is expected (2050) on most of the current surface, with a possible opportunity zone in the north of the country. Finally, two hotspots (high diversity areas) and four coldspots (low diversity areas) were identified among the analyzed species. These results can be used to create strategies for sustainable use and the conservation of these resources.

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets.

This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0. Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis. The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis. These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

Investigating the Effects of Tube Current and Tube Voltage on Patient Dose in Computed Tomography Examinations with Principial Component Analysis and Cluster Analysis: Phantom Study.

The aim of this study was to investigate the effects of tube current and tube voltage choices on patient dose in adult and pediatric CT protocols by qualitative analysis using Principal Component Analysis (PCA), cluster analysis, and statistical analysis.Dose length product (DLP), Effective mAs (Eff. mAs), and volume-weighted CT dose index (CTDIvol) dose descriptors were obtained from 16 adult and pediatric head phantom CT examinations. Different tube voltage and tube current values were selected in both pediatric head and adult head CT imaging protocols, and PCA and cluster analysis were applied to the data obtained for qualitative analysis of the relationship between CTDIvol, Eff. mAs and Total DLP values. The two principial components (PC) with the highest values among those obtained as a result of the PCA method were used. PC1 was 70.97%, and PC2 was 28.03%. In the cluster analysis, it was observed that the values obtained from pediatric and adult phantom CT scans were classified into two different clusters. The correlation coefficient for adult patients was r = 0.998, and for pediatric patients, the correlation coefficient was r = 0.947. When the obtained clusters were examined, the degree of closeness or distance of the variables could be observed. In the study, as a result of the analysis of CTDIvol, Eff. mAs and Total DLP data based on manufacturer data at different kV and mA values with PCA and cluster analysis, it was shown that pediatric patients could be exposed to more radiation than the adult patients.

Screening of Germplasm and Construction of Evaluation System for Autotoxicity Tolerance during Seed Germination in Cucumber

Due to the widespread use of intensive cropping patterns, the problem of continuous cropping obstacle, which is dominated by autotoxicity, has been becoming more and more prominent. Although many methods have been proposed to overcome the continuous cropping obstacle of cucumber, no study has reported the screening and evaluation of cucumber germplasm resistant to autotoxicity. In this study, 28 physiological indices related to the cucumber bud stage under cinnamic acid (CA) treatment were determined. In total, 45 cucumber cultivars were classified into three groups using principal component analysis and cluster analysis, and a model for evaluating cucumber resistance to autotoxicity was developed. The evaluation model was validated using autotoxicity-tolerant and non-autotoxicity-tolerant cultivars. The results showed that the growth of non-autotoxicity-tolerant cultivars was significantly inhibited compared to autotoxicity-tolerant cultivars. This indicated that the evaluation model of cucumber autotoxicity tolerance is reliable. The results of this study provide a valuable reference for the application of cucumber autotoxicity-tolerant germplasm resources and the development of autotoxicity-tolerant genes.

Analyzing the impact of ocean-atmosphere teleconnections on rainfall variability in the Brazilian Legal Amazon via the Rainfall Anomaly Index (RAI)

Drought events are mainly associated with alterations in ocean-atmosphere patterns, leading to abnormally dry conditions due to reduced precipitation. This study aims to enhance the understanding of the spatiotemporal relationship of atmospheric teleconnections associated with droughts in the Brazilian Legal Amazon (BLA), based on the Rainfall Anomaly Index (RAI), utilizing 1675 precipitation grid points from the Global Precipitation Climatology Centre (GPCC) for the period 1970–2019. The results revealed that Principal Component Analysis (PCA) and cluster analysis of RAI demonstrated significant climatic variability, with 15 components explaining 68% of the total variance and identifying two homogeneous regions closely aligning with observed conditions. Atmospheric teleconnections showed substantial variation in spatial and temporal extent in extreme drought events in the years 2005, 2010, 2015–2016, and 2019 in the BLA. The findings indicate that on an interannual scale, the dominant teleconnections in the BLA are the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Tropical North Atlantic (TNA), Pacific North American (PNA) pattern, and Atlantic Meridional Overturning Circulation (AMOC). In contrast, on a decadal scale, the Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), Oceanic Niño Index (ONI), and Tropical South Atlantic (TSA) prevail. Our results provide valuable insights into the nature of drought events in the Amazon, including strategies for the early detection of their intensity and affected areas, demonstrating that atmospheric teleconnections significantly influence both interannual and decadal periods. These influences are concentrated on specific temporal scales and during particular years. Conclusively, this study underscores the critical role of atmospheric teleconnections in shaping drought dynamics in the BLA, offering a foundation for predictive models and mitigation strategies to combat climate-induced challenges in the region.

Comprehensive Evaluation and Selection of 192 Maize Accessions from Different Sources.

In the period 2022-2023, an analysis of fourteen phenotypic traits was conducted across 192 maize accessions in the Aral region of Xinjiang. The Shannon-Wiener diversity index was employed to quantify the phenotypic diversity among the accessions. Subsequently, a comprehensive evaluation of the index was performed utilizing correlation analysis, principal component analysis (PCA) and cluster analysis. The results highlighted significant findings: (1) A pronounced diversity was evident across the 192 maize accessions, accompanied by complex interrelationships among the traits. (2) The 14 phenotypic traits were transformed into 3 independent indicators through principal component analysis: spike factor, leaf width factor, and number of spikes per plant. (3) The 192 materials were divided into three groups using cluster analysis. The phenotypes in Group III exhibited the best performance, followed by those in Group I, and finally Group II. The selection of the three groups can vary depending on the breeding objectives. This study analysed the diversity of phenotypic traits in maize germplasm resources. Maize germplasm was categorised based on similar phenotypes. These findings provide theoretical insights for the study of maize accessions under analogous climatic conditions in Alar City, which lay the groundwork for the efficient utilization of existing germplasm as well as the development and selection of new varieties.

Comparative Study on Beta-Glucan Content and Proximate Composition of Spring Barley Seeds Genotypes Obtained in Different Crop Systems

This paper explores the proximate composition of spring barley seeds and the b-glucan’s content in various two-rowed spring barley genotypes cultivated using different agricultural systems. The genotypes originated from both autochthonous and foreign sources and were grown under conventional, ecological, and no-treatment conditions. The proximate analysis of spring barley seeds was performed using near-infrared spectrometry, while the b-glucan content was determined enzymatically. The data analysis included principal component analysis and cluster analysis to identify ideal candidates for specific applications. The results indicated that the conventional cultivation system yielded higher levels of starch, sugar, protein and ash content; conversely, the ecological and no-treatment systems showed variations in nutritional parameters, with the no-treatment approach resulting in significantly higher b-glucan and fat content. In line with previous research, this study highlights the influence of genotype and agricultural practices on the barley seeds’ composition. Overall, this research contributes to a better understanding of the nutritional composition of barley seeds and offers insights into selecting specific barley genotypes for various applications in the food, feed, and brewing industries; it also underscores the potential for utilizing spring barley's b-glucan to enhance the nutritional value of various products, promoting cardiovascular and digestive health.