Sensing rice drought stress is crucial for agriculture, and chlorophyll a fluorescence (ChlF) is often used. However, existing techniques usually rely on defined feature points on the OJIP induction curve, which ignores the rich physiological information in the entire curve. Independent Component Analysis (ICA) can effectively preserve independent features, making it suitable for capturing drought-induced physiological changes. This study applies ICA and Support Vector Machine (SVM) to classify drought levels using the entire OJIP curve. The results show that the 20-dimensional ChlF features obtained by ICA provide superior classification performance, with Accuracy, Precision, Recall, F1-score, and Kappa coefficient improving by 18.15%, 0.18, 0.17, 0.17, and 0.22, respectively, compared to the entire curve. This work provides a rice drought stress levels determination method and highlights the importance of applying dimension reduction methods for ChlF analysis. This work is expected to enhance stress detection using ChlF.

• Klíčová slova
• chlorophyll a fluorescence, dimension reduction, drought, rice,
• MeSH
• analýza hlavních komponent MeSH
• chlorofyl a * metabolismus   MeSH
• chlorofyl * metabolismus   MeSH
• fluorescence MeSH
• fyziologický stres * MeSH
• období sucha * MeSH
• rýže (rod) * fyziologie   metabolismus   MeSH
• support vector machine MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• chlorofyl a * MeSH
• chlorofyl * MeSH

BACKGROUND: Persimmon (Diospyros kaki L.) belongs to the Ebenaceae family, which includes six genera and about 400 species. This study evaluated the genetic diversity of 100 persimmon accessions from Hatay province, Türkiye using 42 morphological and pomological traits, along with inter simple sequence repeat (ISSR) markers and multivariate analysis. RESULTS: Statistical analysis revealed significant differences among the accessions (ANOVA, p < 0.05). The coefficient of variation ranged from 19.24% for leaf length to 133.89% for fruit calyx groove end, with 97.62% of traits showing more than 20% variation. This indicates high genetic variability. Fruit weight, length, and diameter varied greatly, with strong positive correlations between fruit weight and other traits. Principal component analysis explained 72.42% of the total variance, while cluster analysis showed varying levels of similarity among accessions. ISSR analysis identified 139 bands, 128 of which were polymorphic. The similarity index ranged from 0.41 to 0.96. Notably, accessions 'P78', 'P7', 'P48', 'P29', 'P44', 'P25', 'P5', 'P98', 'P80', 'P50', 'P37', 'P77', 'P57', 'P56', 'P41', 'P73', 'P39', 'P65', 'P72', and 'P61' were identified as promising candidates for further study. CONCLUSIONS: This study demonstrates significant genetic diversity in persimmon accessions from Hatay. The high variability supports adaptability and resilience. Positive correlations among traits, especially fruit weight, are useful for breeding. ISSR markers highlighted valuable polymorphic bands, underlining the importance of local germplasm for developing resilient cultivars. Variations in growth vigor and ripening dates offer opportunities for customized cultivation practices, contributing to sustainable agriculture.

• Klíčová slova
• Diospyros kaki, Genetic diversity, Inter simple sequence repeats, Multivariate analysis, Türkiye,
• MeSH
• analýza hlavních komponent MeSH
• Diospyros * genetika   MeSH
• fylogeneze MeSH
• genetická variace * MeSH
• listy rostlin genetika   anatomie a histologie   růst a vývoj   MeSH
• mikrosatelitní repetice * genetika   MeSH
• ovoce genetika   anatomie a histologie   růst a vývoj   MeSH
• polymorfismus genetický MeSH
• Publikační typ
• časopisecké články MeSH

Lung carcinoma remains the leading cause of cancer death worldwide. The tactic to change this unfortunate rate may be a timely and rapid diagnostic, which may in many cases improve patient prognosis. In our study, we focus on the comparison of two novel methods of rapid lung carcinoma diagnostics, label-free in vivo and ex vivo Raman spectroscopy of the epithelial tissue, and assess their feasibility in clinical practice. As these techniques are sensitive not only to the basic molecular composition of the analyzed sample but also to the secondary structure of large biomolecules, such as tissue proteins, they represent suitable candidate methods for epithelial cancer diagnostics. During routine bronchoscopy, we collected 78 in vivo Raman spectra of normal and cancerous lung tissue and 37 samples of endobronchial pathologies, which were subsequently analyzed ex vivo. Using machine learning techniques, namely principal component analysis (PCA) and support vector machines (SVM), we were able to reach 87.2% (95% CI, 79.8-94.6%) and 100.0% (95% CI, 92.1-100.0%) of diagnostic accuracy for in vivo and ex vivo setup, respectively. Although the ex vivo approach provided superior results, the rapidity of in vivo Raman spectroscopy might become unmatchable in the acceleration of the diagnostic process.

• Klíčová slova
• Endoscopy, Ex vivo diagnostics, In vivo diagnostics, Lung cancer, Machine learning, Optical biopsy, Raman spectroscopy,
• MeSH
• analýza hlavních komponent * MeSH
• lidé středního věku MeSH
• lidé MeSH
• nádory plic * diagnóza   patologie   MeSH
• Ramanova spektroskopie * metody   MeSH
• senioři MeSH
• support vector machine MeSH
• Check Tag
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Quantitative maps of rotating frame relaxation (RFR) time constants are sensitive and useful magnetic resonance imaging tools with which to evaluate tissue integrity in vivo. However, to date, only moderate image resolutions of 1.6 x 1.6 x 3.6 mm3 have been used for whole-brain coverage RFR mapping in humans at 3 T. For more precise morphometrical examinations, higher spatial resolutions are desirable. Towards achieving the long-term goal of increasing the spatial resolution of RFR mapping without increasing scan times, we explore the use of the recently introduced Transform domain NOise Reduction with DIstribution Corrected principal component analysis (T-NORDIC) algorithm for thermal noise reduction. RFR acquisitions at 3 T were obtained from eight healthy participants (seven males and one female) aged 52 ± 20 years, including adiabatic T1ρ, T2ρ, and nonadiabatic Relaxation Along a Fictitious Field (RAFF) in the rotating frame of rank n = 4 (RAFF4) with both 1.6 x 1.6 x 3.6 mm3 and 1.25 x 1.25 x 2 mm3 image resolutions. We compared RFR values and their confidence intervals (CIs) obtained from fitting the denoised versus nondenoised images, at both voxel and regional levels separately for each resolution and RFR metric. The comparison of metrics obtained from denoised versus nondenoised images was performed with a two-sample paired t-test and statistical significance was set at p less than 0.05 after Bonferroni correction for multiple comparisons. The use of T-NORDIC on the RFR images prior to the fitting procedure decreases the uncertainty of parameter estimation (lower CIs) at both spatial resolutions. The effect was particularly prominent at high-spatial resolution for RAFF4. Moreover, T-NORDIC did not degrade map quality, and it had minimal impact on the RFR values. Denoising RFR images with T-NORDIC improves parameter estimation while preserving the image quality and accuracy of all RFR maps, ultimately enabling high-resolution RFR mapping in scan times that are suitable for clinical settings.

• Klíčová slova
• NORDIC, brain mapping, denoising, quantitative MRI, rotating frame relaxation,
• MeSH
• algoritmy MeSH
• analýza hlavních komponent MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mapování mozku MeSH
• mozek * diagnostické zobrazování   MeSH
• poměr signál - šum * MeSH
• rotace MeSH
• senioři MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Natural compounds are important source of desired biological activity which helps to improve nutritional status and brings many health benefits. Ilex paraguariensis St. Hill. which belongs to the family Aquifoliaceae is a plant rich in bioactive substances (polyphenols, saponins, alkaloids) with therapeutic potential including hepatic and digestive disorders, arthritis, rheumatism, and other inflammatory diseases, obesity, hypertension, hypercholesterolemia. In terms of phytochemical research I. paraguariensis has been the subject of most intensive investigations among Ilex species. Therefore, we concentrated on other available Ilex varieties and focused on the content of fatty acids of these shrubs. The fatty acid compounds present in Ilex sp. samples were analyzed by GC-MS. 27 different fatty acids were identified in the extracts. The results showed that many constituents with significant commercial or medicinal importance were present in high concentrations. The primary component in all samples was α linolenic acid(18:3 Δ9,12,15). Differences of this component concentration were observed between cultivars and extensively analyzed by PCA, one- way ANOVA and Kruskal-Wallis ANOVA. Significant correlations between compound concentrations were reported.

• Klíčová slova
• GC-MS, Ilex sp., PCA, correlation matrix, fatty acids,
• MeSH
• analýza hlavních komponent MeSH
• fytonutrienty analýza   chemie   MeSH
• Ilex paraguariensis chemie   MeSH
• Ilex * chemie   MeSH
• mastné kyseliny * analýza   chemie   MeSH
• plynová chromatografie s hmotnostně spektrometrickou detekcí * MeSH
• rostlinné extrakty chemie   analýza   MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• fytonutrienty MeSH
• mastné kyseliny * MeSH
• rostlinné extrakty MeSH

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths in the world. HCC is often diagnosed late because patients with early-stage cancer have no apparent symptoms. Therefore, it is desirable to find a reliable method for an early diagnosis based on the detection of metabolites - biomarkers, that can be detected in the early stages of the disease. Untargeted metabolomics is often used as a tool to find a suitable biomarker for several diseases. In this work, untargeted metabolomics was performed on blood plasma samples of HCC patients and compared with healthy individuals and patients with liver cirrhosis. A combination of liquid chromatography and high-resolution mass spectrometry was used as an analytical method. More than a thousand peaks were detected in the blood plasma samples, from which mainly amino acids, carboxylic acids, lipids, and their derivatives were evaluated as potential biomarkers. The data obtained were statistically processed using the analysis of variance, correlation analysis, and principal component analysis.

• Klíčová slova
• Biomarkers, Cirrhosis, Hepatocellular carcinoma, LC-MS, Metabolomics,
• MeSH
• analýza hlavních komponent MeSH
• chromatografie kapalinová metody   MeSH
• dospělí MeSH
• hepatocelulární karcinom * krev   MeSH
• hmotnostní spektrometrie metody   MeSH
• jaterní cirhóza krev   diagnóza   MeSH
• lidé středního věku MeSH
• lidé MeSH
• metabolomika * metody   MeSH
• nádorové biomarkery * krev   MeSH
• nádory jater * krev   MeSH
• senioři MeSH
• studie případů a kontrol MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• nádorové biomarkery * MeSH

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

• Klíčová slova
• MRI, bipolar disorder, body mass index, obesity, principal component analysis, psychiatry,
• MeSH
• analýza hlavních komponent * MeSH
• bipolární porucha * diagnostické zobrazování   farmakoterapie   patologie   MeSH
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• magnetická rezonanční tomografie * metody   MeSH
• mladý dospělý MeSH
• mozek diagnostické zobrazování   patologie   MeSH
• mozková kůra diagnostické zobrazování   patologie   MeSH
• obezita * diagnostické zobrazování   MeSH
• schizofrenie diagnostické zobrazování   patologie   farmakoterapie   patofyziologie   MeSH
• shluková analýza MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

Wall paintings are integral to cultural heritage and offer rich insights into historical and religious beliefs. There exist various wall painting techniques that pose challenges in binder and pigment identification, especially in the case of egg/oil-based binders. GC-MS identification of lipidic binders relies routinely on parameters like the ratios of fatty acids within the plaster. However, the reliability of these ratios for binder identification is severely limited, as demonstrated in this manuscript. Therefore, a more reliable tool for effective differentiation between egg and oil binders based on a combination of diagnostic values, specific markers (cholesterol oxidation products), and PCA is presented in this study. Reference samples of wall paintings with egg and linseed oil binders with six different pigments were subjected to modern artificial ageing methods and subsequently analysed using two GC-MS instruments. A statistically significant difference (at a 95% confidence level) between the egg and oil binders and between the results from two GC-MS instruments was observed. These discrepancies between the results from the two GC-MS instruments are likely attributed to the heterogeneity of the samples with egg and oil binders. This study highlights the complexities in identifying wall painting binders and the need for innovative and revised analytical methods in conservation efforts.

• Klíčová slova
• P/S ratio, binding media, cholesta-3,5-dien-7-one, dicarboxylic acids, egg, gas chromatography–mass spectrometry, linseed oil, principal component analysis,
• MeSH
• analýza hlavních komponent MeSH
• mastné kyseliny * MeSH
• plynová chromatografie s hmotnostně spektrometrickou detekcí MeSH
• reprodukovatelnost výsledků MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• mastné kyseliny * MeSH

This article presents an attempt to discriminate between human male and female hair samples using a single strand of scalp hair. The methodology involves the non-destructive application of ATR-FTIR spectroscopy coupled with chemometric analysis. A total of 96 hair samples, evenly distributed between 48 male and 48 female volunteers from India, were collected. Spectral analysis revealed subtle differences between the two groups, and reliance on visual interpretation might introduce biasness. To avoid subjective biases, chemometric techniques such as principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were employed for enhanced data visualization and separation. PCA results revealed that the first 10 principal components accounted for 93% of the total variance, with three significant PCs. The PLS-DA model demonstrated a remarkable sensitivity and specificity in sex discrimination from hair samples, establishing its efficacy as a robust classification tool. Furthermore, the proposed model exhibited 100% accuracy in predicting unknown samples, underscoring its potential applicability in real-world scenarios. These outcomes affirm the viability of our approach for non-invasive classification of human male and female hair based on single-strand scalp hair analysis.

• Klíčová slova
• ATR-FTIR, Chemometrics, Human hair, Forensic analysis, Sex discrimination,
• MeSH
• analýza hlavních komponent MeSH
• ATM protein analýza   MeSH
• chemometrika * MeSH
• diskriminační analýza MeSH
• lidé MeSH
• spektroskopie infračervená s Fourierovou transformací metody   MeSH
• vlasy, chlupy * chemie   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• ATM protein MeSH
• ATR protein, human MeSH Prohlížeč

Improving the early diagnosis of gastrointestinal cancers is a crucial step in reducing their mortality. Given the non-specificity of the initial symptoms, the ability of any diagnostic method to differentiate between various types of gastrointestinal cancers also needs to be addressed. To detect disease-specific alterations in biomolecular structure and composition of the blood plasma, we have implemented an approach combining Raman spectroscopy and its conformation-sensitive polarized version, Raman optical activity, to analyze blood plasma samples of patients suffering from three different types of gastrointestinal cancer - hepatocellular, colorectal and pancreatic. First, we aimed to discriminate any type of gastrointestinal cancer from healthy control individuals; inthenext step, the focus was on differentiating among the three cancer types studied. The more straightforward of the two statistical approaches tested, the combination of linear discriminant analysis and principal component analysis applied to the entire spectral dataset, allowed the discrimination of cancer and control samples with 87% accuracy. The three gastrointestinal cancers were classified with an overall accuracy of 76%. The second method, the linear discriminant analysis applied to a selection of spectral bands, yielded even higher values. Cancer and control samples were distinguished with 89% accuracy and hepatocellular, colorectal and pancreatic cancer with an overall accuracy of 87%. The results obtained in our study suggest that the proposed approach may become a disease-specific diagnostic tool in daily clinical practice.

• Klíčová slova
• Blood plasma, Colorectal cancer, Hepatocellular carcinoma, Pancreatic cancer, Raman optical activity, Raman spectroscopy,
• MeSH
• analýza hlavních komponent MeSH
• diferenciální diagnóza MeSH
• diskriminační analýza MeSH
• gastrointestinální nádory * MeSH
• kolorektální nádory * MeSH
• krevní plazma chemie   MeSH
• lidé MeSH
• optická otáčivost MeSH
• Ramanova spektroskopie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH