Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis.

Predicting the mechanical properties of polymer materials using machine learning is essential for the design of next-generation of polymers. However, the strong relationship between the higher-order structure of polymers and their mechanical properties hinders the mechanical property predictions based on their primary structures. To incorporate information on higher-order structures into the prediction model, X-ray diffraction (XRD) can be used. This study proposes a strategy to generate appropriate descriptors from the XRD analysis of the injection-molded polypropylene samples, which were prepared under almost the same injection molding conditions. To this end, first, Bayesian spectral deconvolution is used to automatically create high-dimensional descriptors. Second, informative descriptors are selected to achieve highly accurate predictions by implementing the black-box optimization method using Ising machine. This approach was applied to custom-built polymer datasets containing data on homo- polypropylene and derived composite polymers with the addition of elastomers. Results show that reasonable accuracy of predictions for seven mechanical properties can be achieved using only XRD.

This study proposes a strategy to generate appropriate descriptors, which realize highly accurate predictions of mechanical properties via machine learning from the XRD analysis of the molded polypropylene samples.

Characterization and classification of pathogenic bacteria using native fluorescence and spectral deconvolution.

Identification and classification of pathogenic bacterial strains is of current interest for the early treatment of diseases. In this work, protein fluorescence from eight different pathogenic bacterial strains were characterized using steady state and time resolved fluorescence spectroscopy. The spectral deconvolution method was also employed to decompose the emission contribution from different intrinsic fluorophores and extracted various key parameters, such as intensity, emission maxima, emission line width of the fluorophores, and optical redox ratio. The change in average lifetime values across different bacterial strains exhibits good statistical significance (p ≤ 0.01). The variations in the photophysical characteristics of bacterial strains are due to the different conformational states of the proteins. The stepwise multiple linear discriminate analysis of fluorescence emission spectra at 280 nm excitation across eight different bacterial strains classifies the original groups and cross validated group with 100% and 99.5% accuracy, respectively.

Screening Disinfection Byproducts in Arid-Coastal Wastewater: A Workflow Using GC×GC-TOFMS, Passive Sampling, and NMF Deconvolution Algorithm.

Disinfection during tertiary municipal wastewater treatment is a necessary step to control the spread of pathogens; unfortunately, it also gives rise to numerous disinfection byproducts (DBPs), only a few of which are regulated because of the analytical challenges associated with the vast number of potential DBPs. This study utilized polydimethylsiloxane (PDMS) passive samplers, comprehensive two-dimensional gas chromatography (GC×GC) coupled with time-of-flight mass spectrometry (TOFMS), and non-negative matrix factorization (NMF) spectral deconvolution for suspect screening of DBPs in treated wastewater. PDMS samplers were deployed upstream and downstream of the chlorination unit in a municipal wastewater treatment plant located in Abu Dhabi, and their extracts were analyzed using GC×GC-TOFMS. A workflow incorporating a multi-tiered, eight-filter screening process was developed, which successfully enabled the reliable isolation of 22 candidate DBPs from thousands of peaks. The NMF spectral deconvolution improved the match factor score of unknown mass spectra to the reference mass spectra available in the NIST library by 17% and facilitated the identification of seven additional DBPs. The close match of the first-dimension retention index data and the GC×GC elution patterns of DBPs, both predicted using the Abraham solvation model, with their respective experimental counterparts-with the measured data available in the NIST WebBook and the GC×GC elution patterns being those observed for the candidate peaks-significantly enhanced the accuracy of peak assignment. Isotopic pattern analysis revealed a close correspondence for 11 DBPs with clearly visible isotopologues in reference spectra, thereby further strengthening the confidence in the peak assignment of these DBPs. Brominated analogues were prevalent among the detected DBPs, possibly due to seawater intrusion. The fate, behavior, persistence, and toxicity of tentatively identified DBPs were assessed using EPI Suite™ and the CompTox Chemicals Dashboard. This revealed their significant toxicity to aquatic organisms, including developmental, mutagenic, and endocrine-disrupting effects in certain DBPs. Some DBPs also showed activity in various CompTox bioassays, implicating them in adverse molecular pathways. Additionally, 11 DBPs demonstrated high environmental persistence and resistance to biodegradation. This combined approach offers a powerful tool for future research and environmental monitoring, enabling accurate identification and assessment of DBPs and their potential risks.

Impact of imperfect data on protein secondary structure estimates from Far-UV circular dichroism spectra.

Circular dichroism [CD] is widely used to rapidly assess protein structure. Deconvolution of the far-UV CD spectrum is widely used to quantify the secondary structural elements [SSEs]. Multiple algorithms are available for this. Imperfections in the experimental CD spectra arising from spectral noise, instrument miscalibration, spectral offsets and non-linearity will impact on the accuracy and precision of derived secondary structure estimates. Analytical validation for use in regulated environments, such as biopharmaceuticals, requires that the impact of imperfect data on these estimates be understood. Limited information on the impact of poor data were available. A series of noise-free simulated spectral datasets with modified intensity, wavelength, noise and intensity linearity and offsets were created from entries in the Protein Circular Dichroism Data Bank. These datasets were analysed using the BeStSel, on-line resource to estimate secondary structure. Data imperfections caused significant change in SSEs, but the spectral range is also important. This study emphasises the importance of analytical method validation and justifiable estimates of uncertainty when reporting results. The datasets created are made available as a resource to validate other secondary structure estimation programs.

Quantitative 1H NMR with global spectral deconvolution approach for quality assessment of natural and cultured Cordyceps sinensis.

Cordyceps sinensis is a precious medicinal food which has been successfully cultivated indoors. It remains to be investigated for a simultaneous comparison on aqueous components of natural and cultivated samples. Herein, an approach of quantitative nuclear magnetic resonance (qNMR) analysis combined with global spectral deconvolution (GSD) was established for simultaneous quantification of 26 aqueous components in C. sinensis. Processed by GSD, the distorted baselines of 1H NMR spectra were greatly improved, and overlapped signals were also well separated so as to achieve accurate identification and quantitation of components in C. sinensis. Method validation by UHPLC-QTOF-MS and TOF-SIMS analysis revealed that qNMR combined with GSD is a reliable approach for simultaneous quantification of multiple components including characteristic markers of glutamine, GABA and trehalose in authentic and fake C. sinensis. The well-established qNMR approach can be used for quality assessment of natural and cultivated C. sinensis as well as differentiation from fake ones.

Historical Silk: A Novel Method to Evaluate Degumming with Non-Invasive Infrared Spectroscopy and Spectral Deconvolution.

To correctly manage a collection of historical silks, it is important to detect if the yarn has been originally subjected to degumming. This process is generally applied to eliminate sericin; the obtained fiber is named soft silk, in contrast with hard silk which is unprocessed. The distinction between hard and soft silk gives both historical information and useful indications for informed conservation. With this aim, 32 samples of silk textiles from traditional Japanese samurai armors (15th-20th century) were characterized in a non-invasive way. ATR-FTIR spectroscopy has been previously used to detect hard silk, but data interpretation is challenging. To overcome this difficulty, an innovative analytical protocol based on external reflection FTIR (ER-FTIR) spectroscopy was employed, coupled with spectral deconvolution and multivariate data analysis. The ER-FTIR technique is rapid, portable, and widely employed in the cultural heritage field, but rarely applied to the study of textiles. The ER-FTIR band assignment for silk was discussed for the first time. Then, the evaluation of the OH stretching signals allowed for a reliable distinction between hard and soft silk. Such an innovative point of view, which exploits a "weakness" of FTIR spectroscopy-the strong absorption from water molecules-to indirectly obtain the results, can have industrial applications too.

Raman microspectroscopy identifies fibrotic tissues in collagen-related disorders via deconvoluted collagen type I spectra.

Fibrosis is a consequence of the pathological remodeling of extracellular matrix (ECM) structures in the connective tissue of an organ. It is often caused by chronic inflammation, which over time, progressively leads to an excess deposition of collagen type I (COL I) that replaces healthy tissue structures, in many cases leaving a stiff scar. Increasing fibrosis can lead to organ failure and death; therefore, developing methods that potentially allow real-time monitoring of early onset or progression of fibrosis are highly valuable. In this study, the ECM structures of diseased and healthy human tissue from multiple organs were investigated for the presence of fibrosis using routine histology and marker-independent Raman microspectroscopy and Raman imaging. Spectral deconvolution of COL I Raman spectra allowed the discrimination of fibrotic and non-fibrotic COL I fibers. Statistically significant differences were identified in the amide I region of the spectral subpeak at 1608 cm-1, which was deemed to be representative for structural changes in COL I fibers in all examined fibrotic tissues. Raman spectroscopy-based methods in combination with this newly discovered spectroscopic biomarker potentially offer a diagnostic approach to non-invasively track and monitor the progression of fibrosis. STATEMENT OF SIGNIFICANCE: Current diagnosis of fibrosis still relies on histopathological examination with invasive biopsy procedures. Although, several non-invasive imaging techniques such as positron emission tomography, single-photon emission computed tomography and second harmonic generation are gradually employed in preclinical or clinical studies, these techniques are limited in spatial resolution and the morphological interpretation highly relies on individual experience and knowledge. In this study, we propose a non-destructive technique, Raman microspectroscopy, to discriminate fibrotic changes of collagen type I based on a molecular biomarker. The changes of the secondary structure of collagen type I can be identified by spectral deconvolution, which potentially can provide an automatic diagnosis for fibrotic tissues in the clinical applicaion.

Quantitative 1H NMR with global spectral deconvolution approach for the determination of gamma-aminobutyric acid in Chinese yam (Dioscorea polystachya Turczaninow).

We developed a quantitative proton nuclear magnetic resonance (qNMR) with global spectral deconvolution (GSD) method to determine the gamma-aminobutyric acid content in Chinese yam with the proton signal at δH 2.30. Trimethylsilyl-2,2,3,3-tetradeuteropropionic acid sodium salt was set as the internal standard. The method was validated and showed admissible stability, repeatability, and precision. Compared to the traditional high-performance liquid chromatography method, this method did not involve tedious pre-treatment and expensive standard. Compared to ordinary qNMR, GSD algorithm could effectively remove the effect of noise, baseline distortions and signal overlapping. Overall, qNMR with GSD method is a rapid, simple and reliable method to quantitatively determine functional metabolites even overlapped with other compounds in herbs or foods.

Chemical profiling and identification of anti-osteoporosis chemical-markers of Cinnamomum cassia (L.) presl extracts using GC-MS and spectrum-activity analyses.

Cinnamomum cassia (L.) Presl (cinnamon), an important folk medicine is widely used to prevent osteoporosis for long time in China. Our study aimed to investigate the anti-osteoporosis activity and mechanisms of cinnamon extracts obtained by supercritical CO2 extraction (SFE) and identify activity associated chemical components by gas chromatography-mass spectrometry. The cinnamon SFE exhibited superior anti-osteoporosis efficacy in an ovariectomised mice model to common alcohol extracts. It could induce calcified nodules and ALP activity, upregulate the mRNA expression of ALP, BMP-2, and RUNX2 in MC3T3-E1 cells. The major chemical classes of cinnamon extracts were alcohol esters (28.2%), and terpenes (16.1%). The spectrum-activity analysis indicated that the potential chemical-markers of extracts could be (E)-Cinnamaldehyde, γ-Sitosterol, and (Z, Z)-9,12-Octadecadienoic acid, which could induce the proliferation and ALP activity in MC3T3-E1 cells. Our study revealed the promising applications of the cinnamon SFE in prevention of osteoporosis, and identified its anti-osteoporosis associated compounds.

Spectroelectrochemistry as a new tool for the quantification of UV filters in sun creams.

This study focuses on the spectroelectrochemical quantification of four UV filters, butylmethoxy dibenzoylmethane (BM), benzophenone-3 (BP3), ethylhexyl methoxycinnamate (EM) and octocrylene (OC) used as sunscreens in cosmetics. Three of them exhibited electrochemical activity resulting in the modification of their absorption spectrum under the application of an oxidizing potential of +1.8 V vs. Ag. When working with a mixture containing both electroactive and nonelectroactive UV filters, UV-vis absorption spectra recorded before and after the application of the potential differed. The combination of spectral deconvolution of the spectra pair allowed a more accurate identification and quantification of UV filters than spectral deconvolution of the initial absorbance spectrum alone. This method was effective for the assessment of UV filters in model mixtures and commercial sun creams.

Top-Down Mass Spectrometry Data Analysis Using TopPIC Suite.

With the advances of mass spectrometry (MS) techniques, top-down MS-based proteomics has gained increasing attention because of its advantages over bottom-up MS in studying complex proteoforms. TopPIC Suite is a widely used software package for top-down MS-based proteoform identification and quantification. Here, we present the methods for top-down MS data analysis using TopPIC Suite.

A Methodology for Reconstructing Source Properties of a Conical Piezoelectric Actuator Using Array-Based Methods.

We investigated the force produced by a conical piezoelectric (PZT, lead zirconate titanate) transducer actuated by high voltage pulses (HVP) in contact with a steel transfer plate. Using elastic wave propagation theory in a semi-infinite plate, we aimed to quantify the magnitude and estimate the shape of the force-time function via the body waves produced in the transfer plate using the displacement field recorded on an array of 20 absolutely calibrated PZT receivers. We first calibrated the receiver array using glass capillary fracture. We proceeded to use a conical PZT transducer to actively produce a source at the origin, allowing us to study the displacement field produced on the now calibrated PZT receiver array. We studied two types of HVP: An impulsive and step source. The calibrated receiver array was used to estimate the general shape of the force-time functions for each type of HVP. From our hypothesized force-time functions we were able to estimate the peak force produced by the PZT actuator: The impulsive source generated a force of f peak = 2.90 ± 0.42 N and the step source generated f peak = 1.79 ± 0.30 N, respectively, for a peak applied voltage of 273 V. This translates to an applied force of ∼ 0.011 N/V and 0.007 N/V for the impulse and step force-time functions, respectively, which is similar to estimates found in the literature for other conical transducers in contact with metallic transfer media. This measurement was verified directly by independent measurements of the peak force f peak using a dynamic force transducer. We found that our methodology correctly estimated the magnitude of the force but is limited to transducers with incident angles θ < 53 ∘ . Beyond this angle, overestimates of the force were observed due to the lack of body wave energy produced by the source. These results allow us to quantitatively determine the forces produced by active PZT techniques using only the measurement of the displacement field captured on a calibrated conical PZT array. Quantitative understanding of active PZT sources additionally constrains the transfer functions approach, which is commonly used in the non-destructive testing of materials and in other fields, such as rock physics and laboratory seismology.

FIGS: Featured Ion-Guided Stoichiometry for Data-Independent Proteomics through Dynamic Deconvolution.

Data-independent acquisition (DIA) has significant advantages for mass spectrometry (MS)-based peptide quantification, while mixed spectra remain challenging for precise stoichiometry. We here choose to analyze the library spectra in specific sets preferentially and locally. Accordingly, the featured ions are defined as the fragment ions uniquely assigned to corresponding precursors in a given spectrum set, which are generated by dynamic deconvolution of the mixed mass spectra. Then, we present featured ion-guided stoichiometry (FIGS), a universal method for accurate and robust peptide quantification for the DIA-MS data. We validate the high performance on the quantification sensitivity, accuracy, and efficiency of FIGS. Notably, our FIGS dramatically improves the quantification accuracy for the full dynamic range, especially for low-abundance peptides.

Spectral deconvolution of redox species in the crotonyl-CoA-dependent NADH:ferredoxin oxidoreductase from Megasphaera elsdenii. A flavin-dependent bifurcating enzyme.

We have undertaken a spectral deconvolution of the three FADs of EtfAB/bcd to the spectral changes seen in the course of reduction, including the spectrally distinct anionic and neutral semiquinone states of electron-transferring and bcd flavins. We also demonstrate that, unlike similar systems, no charge-transfer complex is observed on titration of the reduced M. elsdenii EtfAB with NAD+. Finally, and significantly, we find that removal of the et FAD from EtfAB results in an uncrossing of the half-potentials of the bifurcating FAD that remains in the protein, as reflected in the accumulation of substantial FAD•- in the course of reductive titrations of the depleted EtfAB with sodium dithionite.

Algebraic reconstruction of 3D spatial EPR images from high numbers of noisy projections: An improved image reconstruction technique for high resolution fast scan EPR imaging.

A novel method for reconstructing 3D spatial EPR images from large numbers of noisy projections was developed that minimizes mean square error between the experimental projections and those from the reconstructed image. The method utilizes raw projection data and zero gradient spectrum to account for EPR line shape and hyperfine structure of the paramagnetic probe without the need for deconvolution techniques that are poorly suited for processing of high noise projections. A numerical phantom was reconstructed for method validation. Reconstruction time for the matrix of 1283 voxels and 16,384 noiseless projections was 4.6 min for a single iteration. The algorithm converged quickly, reaching R2 ~ 0.99975 after the very first iteration. An experimental phantom sample with nitroxyl radical was measured. With 16,384 projections and a field gradient of 8 G/cm, resolutions of 0.4 mm were achieved for a cubical area of 25 × 25 × 25 mm3. Reconstruction was sufficiently fast and memory efficient making it suitable for applications with large 3D matrices and fully determined system of equations. The developed algorithm can be used with any gradient distribution and does not require adjustable filter parameters that makes for simple application. A thorough analysis of the strengths and limitations of this method for 3D spatial EPR imaging is provided.

A new strategy implementing mass spectrometry in the diagnosis of congenital disorders of N-glycosylation (CDG).

Objectives: Congenital disorders of N-glycosylation (CDG) are a large group of rare metabolic disorders caused by defects in the most common post-translational modification of proteins. CDGs are often difficult to diagnose as they are manifested with non-specific symptoms and signs. Analysis of serum transferrin (TRF) isoforms, as the classical procedure used to identify a CDG patient, enables to predict pathological steps in the N-linked glycosylation process. Methods: We devised a new strategy based on liquid chromatography-mass spectrometry (LC-MS) for the analysis of TRF isoforms by combining a simple and fast sample preparation with a specific chromatographic cleanup/separation step followed by mass-spectrometric measurement. Single TRF isoform masses were obtained through reconstruction of multiply charged electrospray data collected by quadrupole-MS technology. Hereby, we report the first analyzed serum samples obtained from 20 CDG patients and 100 controls. Results: The ratio of desialylated isoforms to total TRF was calculated for patients and controls. CDG-Type I patients showed higher amounts of bi-sialo isoform (range: 6.7-29.6%) compared to controls (<5.5%, mean percentage 3.9%). CDG-Type II pattern showed an increased peak of tri-sialo isoforms. The mean percentage of tri-sialo-TRF was 9.3% (range: 2.9-12.9%) in controls, which was lower than that obtained from two patients with COG5-CDG and MAN1B1-CDG (18.5 and 24.5%). Intraday and between-day imprecisions were less than 9 and 16%, respectively, for bi-sialo- and less than 3 and 6% for tri-sialo-TRF. Conclusions: This LC-MS-based approach provides a simple, sensitive and fast analytical tool for characterizing CDG disorders in a routine clinical biochemistry while improving diagnostic accuracy and speeding clinical decision-making.

A period of 10 weeks of increased protein consumption does not alter faecal microbiota or volatile metabolites in healthy older men: a randomised controlled trial.

Diet has a major influence on the composition and metabolic output of the gut microbiome. Higher-protein diets are often recommended for older consumers; however, the effect of high-protein diets on the gut microbiota and faecal volatile organic compounds (VOC) of elderly participants is unknown. The purpose of the study was to establish if the faecal microbiota composition and VOC in older men are different after a diet containing the recommended dietary intake (RDA) of protein compared with a diet containing twice the RDA (2RDA). Healthy males (74⋅2 (sd 3⋅6) years; n 28) were randomised to consume the RDA of protein (0⋅8 g protein/kg body weight per d) or 2RDA, for 10 weeks. Dietary protein was provided via whole foods rather than supplementation or fortification. The diets were matched for dietary fibre from fruit and vegetables. Faecal samples were collected pre- and post-intervention for microbiota profiling by 16S ribosomal RNA amplicon sequencing and VOC analysis by head space/solid-phase microextraction/GC-MS. After correcting for multiple comparisons, no significant differences in the abundance of faecal microbiota or VOC associated with protein fermentation were evident between the RDA and 2RDA diets. Therefore, in the present study, a twofold difference in dietary protein intake did not alter gut microbiota or VOC indicative of altered protein fermentation.

Protein Conformational Changes in Breast Cancer Sera Using Infrared Spectroscopic Analysis.

Protein structural alterations, including misfolding and aggregation, are a hallmark of several diseases, including cancer. However, the possible clinical application of protein conformational analysis using infrared spectroscopy to detect cancer-associated structural changes in proteins has not been established yet. The present study investigates the applicability of Fourier transform infrared spectroscopy in distinguishing the sera of healthy individuals and breast cancer patients. The cancer-associated alterations in the protein structure were analyzed by fitting the amide I (1600-1700 cm-1) band of experimental curves, as well as by comparing the ratio of the absorbance values at the amide II and amide III bands, assigning those as the infrared spectral signatures. The snapshot of the breast cancer-associated alteration in circulating DNA and RNA was also evaluated by extending the spectral fitting protocol to the complex region of carbohydrates and nucleic acids, 1140-1000 cm-1. The sensitivity and specificity of these signatures, representing the ratio of the α-helix and ß-pleated sheet in proteins, were both 90%. Likewise, the ratio of amides II and amide III (I1556/I1295) had a sensitivity and specificity of 100% and 80%, respectively. Thus, infrared spectroscopy can serve as a powerful tool to understand the protein structural alterations besides distinguishing breast cancer and healthy serum samples.

Study on a peak shape fitting model for the analysis of alpha-particle spectra.

In this study it is developed a model for the detailed and automatic study of the alpha-particle spectra coming from detection systems. The fitting of a typical shape of the alpha peak is performed by a Gaussian function for the right side of the peak and a sum of two Gaussian functions for its left tail. The model takes into account the entire spectrum background and, particular attention is posed to the analysis of overlapped peaks, background noise and peaks with low statistic counting. The effectiveness of the proposed model is supported by several tests.

Polarization gating technique extracts depth resolved fluorescence redox ratio in oral cancer diagnostics.

Mortality of oral cancer is often due to late diagnosis. Effective non-invasive diagnostic techniques may increase the survival rate based on an earlier diagnosis.. We report on the application of the polarization gating technique for isolating weakly scattered and highly scattered components of fluorescence emission from the superficial and deeper layers of tissue due to intrinsic fluorophores NADH and FAD. The fluorescence polarization spectra were collected from 21 normal and 67 oral squamous cell carcinoma biopsy tissues. The tissues were excited at 350 nm and the fluorescence emission had two peaks corresponding to NADH, and FAD respectively. The spectra were analyzed using the polarization gating technique along with the spectral deconvolution method to derive the optical redox ratio from different layers of tissue. The fractional change in redox ratio between superficial and deeper layers of tissue exhibits excellent statistical significance (p<10-3) which may be due to a shift in the metabolic pathway from oxidative phosphorylation to glycolysis in the cancer cell. Further, variation in collagen intensity in deeper layers of tissue is observed which may be attributed to the breakdown of collagen fibers in the stroma. Linear discriminant analysis showed that oral cancer tissue is discriminated with a better accuracy using polarization gating technique than that of conventional fluorescence spectroscopy.