Convergence Properties of Piecewise Power Approximations

Abstract We address the problem of convergence of approximations obtained from two versions of the piecewise power-law representations arisen in Systems Biology. The most important cases of meansquare and uniform convergence are studied in detail. Advantages and drawbacks of the representations as well as properties of both kinds of convergence are discussed. Numerical approximation algorithms related to piecewise power-law representations are described in Appendix

THE USE OF "GREEN" STANDARDS IN THE IMPLEMENTATION OF INVESTMENT AND CONSTRUCTION PROJECTS

В статье рассматривается использование «зеленых» стандартов в реализации инвестиционно-строительных проектов для улучшения экологической обстановки увеличение прибыли от продажи недвижимости со знаком «эко».The article discusses the use of "green" standards in the implementation of investment and construction projects to improve the environmental situation, increase the profit from the sale of real estate with the sign "eco"

Merging FT-IR and NGS for simultaneous phenotypic and genotypic identification of pathogenic Candida species

The rapid and accurate identification of pathogen yeast species is crucial for clinical diagnosis due to the high level of mortality and morbidity induced, even after antifungal therapy. For this purpose, new rapid, high-throughput and reliable identification methods are required. In this work we described a combined approach based on two high-throughput techniques in order to improve the identification of pathogenic yeast strains. Next Generation Sequencing (NGS) of ITS and D1/D2 LSU marker regions together with FTIR spectroscopy were applied to identify 256 strains belonging to Candida genus isolated in nosocomial environments. Multivariate data analysis (MVA) was carried out on NGS and FT-IR datasets, separately. Strains of Candida albicans, C. parapsilosis, C. glabrata and C. tropicalis, were identified with high-throughput NGS sequencing of ITS and LSU markers and then with FTIR. Inter-and intra-species variability was investigated by consensus principal component analysis (CPCA) which combines high-dimensional data of the two complementary analytical approaches in concatenated PCA blocks normalized to the same weight. The total percentage of correct identification reached around 97.4% for C. albicans and 74% for C. parapsilosis while the other two species showed lower identification rates. Results suggested that the identification success increases with the increasing number of strains actually used in the PLS analysis. The absence of reliable FT-IR libraries in the current scenario is the major limitation in FTIR-based identification of strains, although this metabolomics fingerprint represents a valid and affordable aid to rapid and high-throughput to clinical diagnosis. According to our data, FT-IR libraries should include some tens of certified strains per species, possibly over 50, deriving from diverse sources and collected over an extensive time period. This implies a multidisciplinary effort of specialists working in strain isolation and maintenance, molecular taxonomy, FT-IR technique and chemo-metrics, data management and data basing

Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains

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Analysis of Allergenic Pollen by FTIR Microspectroscopy

Fourier transform infrared (FTIR) spectroscopy is a powerful tool for the identification and characterization of pollen and spores. However, interpretation and multivariate analysis of infrared microscopy spectra of single pollen grains are hampered by Mie-type scattering. In this paper, we introduce a novel sampling setup for infrared microspectroscopy of pollens preventing strong Mie-type scattering. Pollen samples were embedded in a soft paraffin layer between two sheets of polyethylene foils without any further sample pretreatment. Single-grain infrared spectra of 13 different pollen samples, belonging to 11 species, were obtained and analyzed by the new approach and classified by sparse partial least-squares regression (PLSR). For the classification, chemical and physical information were separated by extended multiplicative signal correction and used together to build a classification model. A training set of 260 spectra and an independent test set of 130 spectra were used. Robust sparse classification models allowing the biochemical interpretation of the classification were obtained by the sparse PLSR, because only a subset of variables was retained for the analysis. With accuracy values of 95% and 98%, for the independent test set and full cross-validation respectively, the method is outperforming the previously published studies on development of an automated pollen analysis. Since the method is compatible with standard air-samplers, it can be employed with minimal modification in regular aerobiology studies. When compared with optical microscopy, which is the benchmark method in pollen analysis, the infrared microspectroscopy method offers better taxonomic resolution, as well as faster, more economical, and bias-free measurement

Characterisation of cartilage damage via fusing mid-infrared, near-infrared, and Raman spectroscopic data

Abstract Mid-infrared spectroscopy (MIR), near-infrared spectroscopy (NIR), and Raman spectroscopy are all well-established analytical techniques in biomedical applications. Since they provide complementary chemical information, we aimed to determine whether combining them amplifies their strengths and mitigates their weaknesses. This study investigates the feasibility of the fusion of MIR, NIR, and Raman spectroscopic data for characterising articular cartilage integrity. Osteochondral specimens from bovine patellae were subjected to mechanical and enzymatic damage, and then MIR, NIR, and Raman data were acquired from the damaged and control specimens. We assessed the capacity of individual spectroscopic methods to classify the samples into damage or control groups using Partial Least Squares Discriminant Analysis (PLS-DA). Multi-block PLS-DA was carried out to assess the potential of data fusion by combining the dataset by applying two-block (MIR and NIR, MIR and Raman, NIR and Raman) and three-block approaches (MIR, NIR, and Raman). The results of the one-block models show a higher classification accuracy for NIR (93%) and MIR (92%) than for Raman (76%) spectroscopy. In contrast, we observed the highest classification efficiency of 94% and 93% for the two-block (MIR and NIR) and three-block models, respectively. The detailed correlative analysis of the spectral features contributing to the discrimination in the three-block models adds considerably more insight into the molecular origin of cartilage damage

Obesity-Related Metabolome and Gut Microbiota Profiles of Juvenile Göttingen Minipigs—Long-Term Intake of Fructose and Resistant Starch

The metabolome and gut microbiota were investigated in a juvenile Göttingen minipig model. This study aimed to explore the metabolic effects of two carbohydrate sources with different degrees of risk in obesity development when associated with a high fat intake. A high-risk (HR) high-fat diet containing 20% fructose was compared to a control lower-risk (LR) high-fat diet where a similar amount of carbohydrate was provided as a mix of digestible and resistant starch from high amylose maize. Both diets were fed ad libitum. Non-targeted metabolomics was used to explore plasma, urine, and feces samples over five months. Plasma and fecal short-chain fatty acids were targeted and quantified. Fecal microbiota was analyzed using genomic sequencing. Data analysis was performed using sparse multi-block partial least squares regression. The LR diet increased concentrations of fecal and plasma total short-chain fatty acids, primarily acetate, and there was a higher relative abundance of microbiota associated with acetate production such as Bacteroidetes and Ruminococcus. A higher proportion of Firmicutes was measured with the HR diet, together with a lower alpha diversity compared to the LR diet. Irrespective of diet, the ad libitum exposure to the high-energy diets was accompanied by well-known biomarkers associated with obesity and diabetes, particularly branched-chain amino acids, keto acids, and other catabolism metabolites

Infrared spectroscopy is suitable for objective assessment of articular cartilage health

Abstract Objective: To evaluate the feasibility of Fourier transform infrared attenuated total reflectance (FTIR-ATR) spectroscopy to detect cartilage degradation due to osteoarthritis and to validate the methodology with osteochondral human cartilage samples for future development towards clinical use. Design: Cylindrical (d ​= ​4 ​mm) osteochondral samples (n ​= ​349) were prepared from nine human cadavers and measured with FTIR-ATR spectroscopy. Afterwards, the samples were assessed with Osteoarthritis Research Society International (OARSI) osteoarthritis cartilage histopathology assessment system and divided into two groups: 1) healthy (OARSI 0–2) and 2) osteoarthritic (OARSI 2.5–6). The classification was done with partial least squares discriminant analysis model utilizing cross-model validation. Receiver operating characteristics curve analysis was performed and the area under curve (AUC) was calculated. Results: For all samples combined, classification accuracy was 73% with AUC of 0.79. Femoral samples had accuracy of 74% and AUC of 0.77, while tibial samples had accuracy of 66%, and AUC of 0.74. Patellar samples had accuracy of 84% and AUC of 0.91. Conclusions: The results indicate that FTIR-ATR spectroscopy can differentiate between healthy and osteoarthritic femoral, tibial and patellar human tissue. If combined with a fiber optic probe, FTIR-ATR spectroscopy could provide additional objective intraoperative information during arthroscopic surgeries, which could improve clinical outcomes