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In the present work, both direct and inverse problems are considered for a Fisher-type fractional diffusion equation, which is proposed to describe the phenomenon of cell migration. For the direct problem, a solution is given via the Fourier method and the Laplace transform. On the other hand, we solved the inverse problem from a Bayesian statistical framework using a set of data that are the result of a cell migration experiment on a wound closure assay. We estimated the parameters of the mathematical model via Markov Chain Monte Carlo methods.

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This study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim is to group similar samples together, revealing patterns and aiding in classification. The process begins by determining the number of clusters using the elbow method, resulting in five distinct clusters. Principal Component Analysis (PCA) is then applied to reduce the dataset's dimensionality by capturing its significant variances. Hierarchical Cluster Analysis (HCA) further refines the sample clusters. 20% of the data, representing identified clusters, is randomly selected for testing, while the remainder serves as training data for a deep learning algorithm employing a multilayer perceptron (MLP). Following training, the test data are evaluated, revealing an impressive 96.15% accuracy. Accuracy measures the machine learning model's ability to predict class labels for new data accurately. This approach offers reliable honey sample clustering without necessitating extensive preprocessing. Moreover, its swiftness and cost-effectiveness enhance its practicality. Ultimately, by leveraging FTIR spectral data, this method successfully identifies similarities among honey samples, enabling efficient categorization and demonstrating promise in the field of spectral analysis in food science.

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Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 µg/mL and >0.5 µg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

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INTRODUCTION: Dahlia pinnata Cav. is a flower native to Mexico that has many applications; in particular, its petals have been used for ornamental, food, and medicinal purposes, for example to treat skin rashes and skin cracks. It has been reported that the medicinal properties of plants are generally related to the phytochemical constituents they possess. However, there are few studies on black D. pinnata. OBJECTIVES: The present study was aimed at qualitatively and quantitatively determining the phytochemical profile of petals from black D. pinnata. METHODOLOGY: Phytochemicals from Dahlia petals were extracted by consecutive maceration (hexane, dichloromethane, and methanol); then, the extracts were analyzed through colorimetric assays and UV-Vis spectroscopy for qualitative identification and quantification of phytochemical compounds, respectively. The methanolic extract was analyzed by flow injection analysis-electrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry (FIA-ESI-FTICR-MS) in negative and positive mode. RESULTS: Quantitative phytochemical profiling of the methanolic extract by UV-Vis spectroscopy indicated high contents of phenolic compounds (34.35 ± 3.59 mg EQ/g plant) and sugars (23.91 ± 1.99 mg EQ/g plant), while the qualitative profiling by FIA-ESI-FTICR-MS allowed the tentative identification of several flavonoids and phenolic acids. Kaempferol-3-rutinoside, pelargonidin-3-(6″-malonylglucoside)-5-glucoside, rutin, kaempferol-3-(2″,3″-diacetyl-4″-p-coumaroylrhamnoside), and myricetin-3-(2‴-galloylrhamnoside) were the main compounds detected. CONCLUSION: The results expand our knowledge of the phytochemical constituents of petals from black D. pinnata.

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The quality of life and human survival is dependent on sustainable development and sanitation of water bodies in an environment. The present research focuses on cyclicity data of more than 750,000 records of parameters associated with the water quality from a rural-urban river monitoring stations in real-time from River Atoyac in Central Mexico. The events detected in the instrumental records correlated with 2528 laboratory and instrumental determinations. The 64 polluting compounds were grouped into inorganic compounds (metals and metalloids) and organic compounds (pesticides, herbicides, hydrocarbons). Metal associated compounds were grouped along mechanical, pharmaceutical and textile industries which associates itself with the entry of polluting components. The cyclicity of the events was detected through Discrete Fourier Transformation time series analysis identifying the predominant events in each station. These highlight the events at 23-26 h corresponding to a circadian pattern of the metabolism of the city. Likewise, pollution signals were detected at 3.3, 5.5, and 12-14 h, associated with discharges from economic activities. Multivariate statistical techniques were used to identify the circadian extremes of a regionalized cycle of polluting compounds in each of the stations. The results of this research allow pollution prevention using a mathematical analysis of time series of different quality parameters collected at monitoring stations in real-time as a tool for predicting polluting events. The DFT analysis makes it possible to prevent polluting events in different bodies of water, allowing to support the development of public policies based on the supervision and control of pollution.

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Este trabalho objetivou desenvolver, caracterizar e avaliar a atividade osteogênica de formulações de CP com diferentes proporções de óxido de Nióbio (Nb2O5), substituindo o Bi2O3. Foram utilizados três grupos, um controle e dois experimentais: GC (MTA Angelus®), F6 (75% CP, 20% Nb2O5 e 5% CaSO4) e F7 (75% CP, 10% Bi2O3, 10% Nb2O5e 5% CaSO4). As formulações foram submetidas às análises de Difração de RaioX (DRX), Microscopia Eletrônica de Varredura (MEV), Espectroscopia de Infravermelho com Transformada de Fourier (FTIR), Espectrometria de Fluorescência de Raios-X (FRX), pH, tempo de presa, radiopacidade, resistência a compressão, citotoxicidade e bioatividade. Os dados obtidos foram avaliados estatisticamente pelo teste de variância (ANOVA) com correção de Bonferroni (p<0,05%). O resultado do teste de pH: Nióbio 10% Imediato (12,205 ±0,304); 24h; (12,770 ± 0,226) 48h: (12,910 ± 0,169). Nióbio 20%: imediato (12,080 ± 0,282); 24 h: (12,350 ± 0,593); 48 h: (12,580 ± 0,73). Para o tempo de presa inicial em segundos: MTA (397,500 ±10,606); Nióbio 10% (294,333 + 90,897) e Nióbio 20% (279,000 + 15,874). O tempo de presa final para os grupos foram: MTA (15,000 + 49,497), Nióbio 10% (560 ±38,587), sendo menor quando comparado ao MTA (p<0,001) e Nióbio 20% (715,666 ± 30,664) (p<0,01). Os valores da radiopacidade em mm Al do Nióbio 10% (3,888 ±0,340); Nióbio 20% (3,713 ± 0,712). A resistência a compressão em MPa foi: Nióbio 10% (694,150 + 78,951) Nióbio 20% (699,295 + 47,672). A viabilidade celular não apresentou diferença entre o MTA e grupos experimentais (p<0,05). Os resultados da capacidade osteogênese das formulações a partir do ensaio da fosfatase alcalina (FAL) em UI/L por grama de proteína para cada grupo foi: MTA (1,9 + 1,227) e Nióbio 20% (1,784 + 1,342) (p>0,05). Nossos achados apontam propriedades relevantes para as formulações com Nb2O5 como, pH alcalino, radiopacidade, resistência a compressão e atividade da fosfatase alcalina (AU).

Work aimed to develop, characterize and evaluate the osteogenic activity of PC formulations with different proportions of Niobium oxide (Nb2O5), replacing Bi2O3. Three groups were used, one control and two experimental: GC (MTA Angelus®), F6 (75% CP, 20% Nb2O5 and 5% CaSO4) and F7 (75% CP, 10% Bi2O3, 10% Nb2O5e 5% CaSO4) . The formulations were submitted to X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Fluorescence Spectrometry (FRX), pH, setting, radiopacity, compressive strength, cytotoxicity and bioactivity. The data obtained were statistically evaluated by the test of variance (ANOVA) with Bonferroni correction (p<0.05%). The pH test result: Niobium 10% Immediate (12.205 ±0.304); 24h; (12.770 ± 0.226) 48h: (12.910 ± 0.169). Niobium 20%: immediate (12.080 ± 0.282); 24h: (12.350 ± 0.593); 48 h: (12.580 ± 0.73). For initial setting time in seconds: MTA (397,500 ±10,606); Niobium 10% (294,333 + 90,897) and Niobium 20% (279,000 + 15,874). The final setting time for the groups were: MTA (15,000 + 49,497), Niobium 10% (560 ± 38,587), being smaller when compared to MTA (p<0,001) and Niobium 20% (715,666 ± 30,664) (p<0 .01). Radiopacity values in mm Al of 10% Niobium (3.888 ±0.340); Niobium 20% (3.713 ± 0.712). The compressive strength in MPa was: Niobium 10% (694.150 + 78.951) Niobium 20% (699.295 + 47.672). Cell viability showed no difference between MTA and experimental groups (p<0.05). The results of the osteogenesis capacity of the formulations from the alkaline phosphatase assay (ALF) in IU/L per gram of protein for each group were: MTA (1.9 + 1.227) and Niobium 20% (1.784 + 1.342) (p> 0.05). Our findings point to relevant properties for Nb2O5 formulations such as alkaline pH, radiopacity, compressive strength and alkaline phosphatase activity (AU).

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Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 µL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

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GC × GC investigations are well known to generate a substantial amount of information-rich and structurally complex data, requiring advanced data processing strategies like chemometrics. Many workflows are available for data handling and processing, such as the peak-table and pixel-based approaches. The goal of this work is to present a solution based on method development to solve the missing pixel problem that may be encountered in experiments performed with GC and GC × GC coupled to the Fourier transform orbital ion trap (FT-Orbitrap) mass analyzer. Data input is vital for pixel-based chemometric analyses, as some post-processing solutions may lead to significant loss of chemical information in the data set. Hence, a key requisite is that the chemical information is consistently indexed in the data arrays for proper pixel-based data handling and analysis. In this study, we carefully evaluated the ion management parameters to preserve the intrinsic structure and information of the data arrays of the GC × GC-FT-Orbitrap for future pixel-oriented chemometric analysis. The most acceptable conditions yielded acquisition rates up to 42.6 spectra per s, while a routine setting of 24.7 Hz was successfully employed in analyses of different petroleum fractions, producing both consistent tensor sizes and acceptable peak reconstructions. A data acquisition rate of 24.7 spectra per s and a mass resolving power of 15 000 allowed the resolution of a mass split of only 0.004 Da - which is an interesting configuration for challenging applications in petroleomics. Using such advanced settings, the missing pixel problem was reduced from up to 30% to much less than 0.04% of the data array dimension. Thus, the proposed configuration can be employed in studies that require pixel-oriented multivariate data analysis.

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The use of phasors to analyze fluorescence data was first introduced for time-resolved studies for a simpler mathematical analysis of the fluorescence-decay curves. Recently, this approach was extended to steady-state experiments with the introduction of the spectral phasors (SP), derived from the Fourier transform of the fluorescence emission spectrum. In this work, we revise key mathematical aspects that lead to an interpretation of SP as the characteristic function of a probability distribution. This formalism allows us to introduce a new tool, called multi-dimensional spectral phasor (MdSP) that seize, not only the information from the emission spectrum, but from the full excitation-emission matrix (EEM). In addition, we developed a homemade open-source Java software to facilitate the MdSP data processing. Due to this mathematical conceptualization, we settled a mechanism for the use of MdSP as a tool to tackle spectral signal unmixing problems in a more accurate way than SP. As a proof of principle, with the use of MdSP we approach two important biophysical questions: protein conformational changes and protein-ligand interactions. Specifically, we experimentally measure the EEM changes upon denaturation of human serum albumin (HSA) or during its association with the fluorescence dye 1,8-anilinonaphtalene sulphate (ANS) detected via tryptophan-ANS Förster Resonance Energy Transfer (FRET). In this sense, MdSP allows us to obtain information of the system in a simpler and finer way than the traditional SP. Specifically, understanding a protein's EEM as a molecular fingerprint opens new doors for the use of MdSP as a tool to analyze and comprehend protein conformational changes and interactions.

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