Method For Analysis Of Mixtures Research Articles

Forest disturbance regimes and trends in continental Spain (1985–2023) using dense landsat time series

Forest disturbance regimes across biomes are being altered by interactive effects of global change. Establishing baselines for assessing change requires detailed quantitative data on past disturbance events, but such data are scarce and difficult to obtain over large spatial and temporal scales. The integration of remote sensing with dense time series analysis and cloud computing platforms is enhancing the ability to monitor historical disturbances, and especially non-stand replacing events along climatic gradients. Since the integration of such tools is still scarce in Mediterranean regions, here, we combine dense Landsat time series and the Continuous Change Detection and Classification - Spectral Mixture Analysis (CCDC-SMA) method to monitor forest disturbance in continental Spain from 1985 to 2023. We adapted the CCDC-SMA method for improved disturbance detection creating new spectral libraries representative of the study region, and quantified the year, month, severity, return interval, and type of disturbance (stand replacing, non-stand replacing) at a 30 m resolution. In addition, we characterised forest disturbance regimes and trends (patch size and severity, and frequency of events) of events larger than 0.5 ha at the national scale by biome (Mediterranean and temperate) and forest type (broadleaf, needleleaf and mixed). We quantified more than 2.9 million patches of disturbed forest, covering 4.6 Mha over the region and period studied. Forest disturbances were on average larger but less severe in the Mediterranean than in the temperate biome, and significantly larger and more severe in needleleaf than in mixed and broadleaf forests. Since the late 1980s, forest disturbances have decreased in size and severity while increasing in frequency across all biomes and forest types. These results have important implications as they confirm that disturbance regimes in continental Spain are changing and should therefore be considered in forest strategic planning for policy development and implementation.

Relaxation Editing in NMR Using a New Two-Dimensional Long-Lived Coherence Method for Mixture Analysis.

The exploration of metabolomics and targeted segments of proteins stands as a pivotal facet of Nuclear Magnetic Resonance (NMR) analysis, furnishing valuable insights into molecular architectures and potential therapeutic applications. The issue of spectral congestion frequently presents challenges in ascribing distinct peaks within the confines of both one-dimensional (1D) and two-dimensional (2D) NMR spectra. Numerous strategies have been proposed to resolve specific resonances in NMR spectra differentially. Among these approaches, relaxation editing emerges as a viable solution. In the realm of relaxation phenomena within NMR, Long-Lived States (LLS) and Long-Lived Coherences (LLC) manifest as promising phenomena, offering enhanced relaxation lifetimes in comparison to the traditional longitudinal (T1) and transverse (T2) relaxation times for coupled nuclear spins. Notably, LLC presents a pathway to attenuate uncoupled high-intensity peaks, effectively diminishing their impact. The foundation of this technique rests upon the premise that the relaxation lifetime in the rotating frame (T1ρ) remains smaller than TLLC. In pursuit of refining spectral assignments within complex mixtures, we introduce a new pulse sequence tailored for LLC Total Correlation Spectroscopy (LLC-TOCSY). This demonstrates efficacy in extracting LLC signals within configurations involving multiple coupled spins, thereby decluttering the spectrum and enhancing the accuracy of peak assignments. To validate the effectiveness of this method, a collection of samples was subjected to scrutiny, yielding compelling results.

Association between parabens exposure and neurodevelopment in children

ObjectiveParabens are a group of substances commonly employed as antimicrobial preservatives. The effect of parabens on the development of neurotoxicity in children is still controversial. This study aimed to explore the associations between parabens exposure and children's neurodevelopmental performance, emphasizing potential sex differences and the combined effects of parabens. MethodsWe used the long-term follow-up study of Taiwanese generation, Taiwan Birth Panel Study II (TBPS II). We recruited the group of children at 6–8 years old. And, we measured parabens in children urine, including methylparaben (MP), ethylparaben (EP), propylparaben (PP) and butylparaben (BP). Children's attention-related performance was evaluated using the Conners Kiddie Continuous Performance Test 2nd Edition (K-CPT 2). The study employed both linear regression and mixture analysis quantile g-computation (QGC) methods to discern associations. A stratified analysis by sex and QGC was implemented to delve deeper into the cumulative effects of parabens. ResultsA total of 446 subjects completed both the parabens analysis and the K-CPT 2 survey. The overall association between parabens and neurodevelopmental performance was not pronounced, but discernible sex differences emerged. In the single pollutant analysis, elevated PP concentrations were associated with higher K-CPT 2 scores particularly in detectability (d') (β = 0.92 [95 % CI = 0.15 to 1.69]) and commissions (β = 0.95 [95 % CI = 0.12 to 1.78]), among girls. Further, in the mixture analysis, a significant association between PP and detectability (d') was observed in girls (β = 1.68 [95 % CI = 0.11 to 3.26]). ConclusionsThis study identified sex-specific associations between parabens and attention performance. Consistent outcomes across single and mixture analysis methods. Further research is crucial to clarify these causal associations.

Long-term exposure to varying-sized particulate matters and kidney disease in middle-aged and elder adults: A 8-year nationwide cohort study in China

Evidence for the causal relationship of particulate matters (PMs) exposure with kidney disease, especially PM1, PM1–2.5 and PM2.5–10, remained scarce among developing countries with severe pollution. We conducted a longitudinal cohort study involving 13,041 adults with free kidney disease from 150 Chinese counties. PMs concentrations were generated using a well-established satellite-based spatiotemporal model. And the time-varying Cox regression model along with stratified analyses were performed to determine the association and potential modifiers, respectively. We also calculated the population-attributable fraction to evaluate the burden of kidney disease attributable to PMs pollution. Between Jan 2011 and Dec 2018, 985 kidney disease incidents were identified with an incidence rate of 12.69 per 1000 person-years. Significant dose-response relationships were observed for all 5 kinds PMs. Specifically, an increased risk of kidney disease was associated with per 10 μg/m3 increment of PM1 (HR = 1.187, 95%CI: 1.114 to 1.265), PM1–2.5 (1.326, 1.212 to 1.452), PM2.5 (1.197, 1.139 to 1.258), PM2.5–10 (1.297, 1.240 to 1.357), and PM10 (1.137, 1.108 to 1.166). A mixture analysis method of weighted quantile regression model revealed that PM2.5–10 predominated the PMs mixture index (57.1 %), and followed with PM10 (26.4 %). Stratified analyses indicated the elder, overweight persons, smokers, respiratory patients and urban residents were more vulnerable to PMs pollution than their counterparts. Calculated population attributable fractions of kidney disease attributable to PMs pollution was 16.67–39.47 %. Higher PMs pollution was associated with the increased risk of kidney disease development in China. Acceleration of efforts to reduce PMs pollution was therefore urgently needed to alleviate kidney disease burden.

Mapping Coastal Wetlands and Their Dynamics in the Yellow River Delta over Last Three Decades: Based on a Spectral Endmember Space

The accurate mapping and analysis of coastal wetlands and their dynamics are crucial for local coastal wetland protection, sustainable social development, and biodiversity preservation. However, detailed mapping and comprehensive analysis of coastal wetlands remain scarce. In this study, we utilized Landsat-TM/OLI remote sensing data and employed the linear spectral mixture analysis (LSMA) method to map changes in coastal wetlands and analyze their dynamics in the Yellow River Delta (YRD) from 1991 to 2020. Our mapping results demonstrate high accuracy and are consistent with previous studies, boasting an overall accuracy exceeding 96%. During the period of 1991–2020, the YRD estuary expanded by approximately 8744.58 ha towards the east and north. The vegetation of P. australis and S. salsa underwent transformation due to agricultural practices or degradation to bare flats. Moreover, these areas saw extensive colonization by the invasive species S. alterniflora. Over the three decades, S. alterniflora expanded approximately 5 km along the coast, significantly impacting the local coastal wetland biodiversity. Furthermore, a considerable number of natural wetlands transitioned into human-made wetlands from 1991 to 2014. In particular, bare flats underwent substantial changes, transforming into aquaculture sites and salt exploitation areas. These dynamics in coastal wetlands had significant repercussions on local ecosystems, including wetland fragmentation, biodiversity depletion, and water pollution. However, post-2014, numerous wetland protection strategies were implemented, resulting in the restoration of natural wetlands. Detailed wetland mapping and dynamic analysis furnish valuable insights for the management, protection, and sustainable utilization of diverse coastal wetlands.

Estimating Agricultural Cropping Intensity Using a New Temporal Mixture Analysis Method from Time Series MODIS

Agricultural cropping intensity plays an important role in evaluating the food security and the sustainable development of agriculture. The existing indicators measuring cropping intensity include cropping frequency and multiple cropping index. As a nominal measurement, cropping frequency classifies crop patterns into single-cropping and/or double-cropping and leads to information loss. Multiple cropping index is calculated on the basis of statistical data, ignoring the spatial heterogeneity within the administrative region. Neither of these indicators can meet the requirements of precision agriculture, and new methods for fine cropping intensity mapping are still lacking. Time series remote sensing data provide vegetation phenology information and reveal temporal development of vegetation, which can be used to facilitate the fine cropping intensity mapping. In this study, a new temporal mixture analysis method is introduced to estimate the abundance level cropping intensity from time series remote sensing data. By analyzing phenological characteristics of major land-cover types in time series vegetatiosacan indices, a novel feature space was constructed by using the selected PCA components, and three unique endmembers (double-cropping, natural vegetations and water bodies) were found. Then, a linear spectral mixture analysis model was applied to decompose mixed pixels by replacing spectral data with multi-temporal data. The spatio-temporal continuous, fine resolution, abundance level cropping intensity maps were produced for the North China Plain and the middle and lower reaches of the Yangtze River Valley. The experiments indicate a good result at both county and pixel level validation. The method of manually delineating endmembers can well balance the accuracy and efficiency. We also found the size of the study area has little effect on the unmixing accuracy. The results demonstrated that the proposed method can model cropping intensity finely at large scale and long temporal span, at the same time with high efficiency and ease of implementation.

Grain yield prediction using multi-temporal UAV-based multispectral vegetation indices and endmember abundance in rice

Timely and accurately predicting grain yield before harvest is of great importance for rice production management and grain trade. Numerous methods based on remote sensing (RS) technology have explored to estimate rice grain yield. However, in most cases, these methods are negatively affected by the mixed pixels of RS images due to the effect of background and panicles. To resolve such issues, the abundance information of leaves, soil and water background and rice panicles were extracted from the multispectral images of unmanned aerial vehicle (UAV) for the multiple endmember spectral mixture analysis (MESMA) method. Based on the analysis of contribution of vegetation index (VI) and abundance (ABD) to rice grain yield, a rice grain yield prediction model was developed by combining multi-stage time-series, ABD and VI. Results showed that MESMA can mitigate endmember variability in the estimation of rice yield, and achieve higher accuracy than conventional spectral mixture analysis (SMA). The NDRE and the sum of leaf and panicle abundance (ABDL+P) can be well correlated to grain yield before heading stage (R2 = 0.75) and at heading stage (R2 = 0.72), respectively. In comparison to them, the multi-stage time-series model consisted of VI and ABD [∑(VI&ABD)] produces the highest correlation (R2 = 0.80). It could also resolve the issues about the underestimation of yield using different datasets from various multi-spectral camera in comparison to the single-parameter models ∑VI and ∑ABD, and produce the good validation accuracy with R2 = 0.73, RRMSE= 0.22 and R2 = 0.75, RRMSE= 0.15, respectively. This study suggests that the combination of UAV multi-temporal VI and ABD data can achieve accurate prediction of rice grain yield. This method effectively utilizes the optical information of leaves and rice panicles and reduces background effects, which provides a new idea for accurate rice yield prediction.

Application of mixture analysis methods in association between metals mixture exposure and DNA oxidative damage

Objectives: To study the association between metals mixture exposure and DNA oxidative damage using mixture analysis methods, and to explore the most significant exposure factors that cause DNA oxidative damage. Methods: Workers from steel enterprises were recruited in Shandong Province. Urinary metals were measured by using the inductively coupled plasma mass spectrometry method. The level of urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) was determined by using the ultra-high performance liquid chromatography-mass spectrometry method. Bayesian kernel machine regression (BKMR), elastic net regression and quantile g-computation regression were used to analyze the association between urinary metals and urinary 8-OHdG. Results: A total of 768 subjects aged (36.15±7.40) years old were included in the study. BKMR, elastic net regression and quantile g-computation all revealed an overall positive association between the mixture concentration and increased urinary 8-OHdG. The quantile g-computation results showed that with a 25% increase in metal mixtures, the urinary 8-OHdG level increased by 77.60%. The elastic net regression showed that with a 25% increase in exposure risk score, the urinary 8-OHdG level increased by 26%. The BKMR summarized the contribution of individual exposures to the response, and selenium, zinc, and nickel were significant contributors to the urinary 8-OHdG elevation. Conclusion: Exposure to mixed metals causes elevated levels of DNA oxidative damage, and selenium, zinc, and nickel are significant exposure factors.

Ultrahigh performance LC/FT-MS non-targeted screening for biomass burning organic aerosol with MZmine2 and MFAssignR

In recent years, ultrahigh performance liquid chromatography Fourier transform mass spectrometry (LC/FT-MS) based non-targeted screening (NTS) methods have become increasingly popular for comprehensive analysis of complex organic mixtures. However, applying these methods for environmental complex mixture analysis is challenging due to the extreme complexity of natural samples and a lack of standard samples or surrogates for environmental complex mixtures. Furthermore, limited molecular markers in the databases and insufficient data processing software workflows make the application of these methods more challenging for environmental complex mixtures. In this work, we implement a new NTS data processing workflow to process data collected from ultrahigh performance liquid chromatography and Fourier transform Orbitrap Elite Mass Spectrometry (LC/FT-MS) by combining MZmine2 and MFAssignR, two opensource data processing tools and commercial Mesquite liquid smoke as a surrogate for biomass burning organic aerosol. MZmine2.53 data extraction followed MFAssignR molecular formula assignment offered noise free and highly accurate 1733 individual molecular formulas presented in liquid smoke with 4906 molecular species, including isomers. The results of this new approach were consistent with the results of direct infusion FT-MS analysis confirming its reliability. Over 90% of the molecular formulas presented in mesquite liquid smoke were matched with the molecular formulas of ambient biomass burning organic aerosol. This suggests the potential use of commercial liquid smoke is an acceptable surrogate for biomass burning organic aerosol research. The presented method significantly improves the identification of the molecular composition of biomass burning organic aerosol by successfully addressing some of the limitations related to the data analysis and giving a semi quantitative insight into the analysis.

Environmentally sustainable computationally spectrophotometric resolution strategy for analysis single-tablet regimens of antihypertension with overlapped spectra

Single tablet regimens (STRs) for hypertension suppression improve patient satisfaction, quality of life, medication and adherence compared to multi-tablet regimens (MTRs). Development of simple cost effective and at the same time green an ecofriendly method for analysis and resolution of multicomponent and complex mixtures is an increasingly important issue in the field of pharmaceutical industry in general and analytical chemistry in specific. Thus, the presented research deals with two main goals, the first one is concerned with developing and applying a fully validated resolution plan using numerical data of signals on manipulated spectra for successive extraction of parent spectra of drugs in single-tablet Regimens (STRs) used in antihypertensive treatment. The STRs contains three drugs with deconvoluted spectra, Atenolol (ATL), Amiloride (AMD) and Chlortalidone (CLN). The resolution plan encompasses two simple and affordable spectrophotometric methods employing resolving spectra either normalized spectrum (NS) or factorized spectrum (FS) which acts as a master key for unlocking the overlapped spectra of ternary mixture with no need for any preliminary physical separation, sample pretreatment or pH adjustment. These two main methods namely, dual amplitude difference (DAD) and derivative ratio transformation (DDT). The presented approaches required a simple spectrophotometer with its built-in program using primitive mathematical operations to restore the parent spectrum for individually drug cited in the mixture understudy. The proposed methods validity was checked and evaluated through the ICH guidelines and displayed linearity within concentration range 4.0-40.0μg/mL for ATL, 3.0-20.0μg/mL for both AMD and CLN. While the specificity evaluation was performed via assaying the three drugs accurately and precisely in their synthetic mixtures and in their STRs. Generally, these methods could be recruited for the fast and effective analysis and determination of the purity index for AMD, CLN and ATL in bulk materials and available market formulation, Teklo ® tablets.The second main goal of the research was the evaluation of the methods greenness and whiteness where the applied UV-methods and the reported HPLC one had significantly different greenness scores when compared using three metrics: the Green Certificate Classification (GCC), the Complex Green Analytical Procedure Index (Complex-GAPI), and the Analytical Greenness metric approach (AGREE).Also, the whiteness scores, which reflected the degree of the achieved sustainability for the HPLC reported technique and the produced UV ones were done by using the ideologies of the white analytical chemistry (WAC) tool. All the differences between the four aforementioned metrics are discussed in this study.

Nanoelectromechanical Infrared Spectroscopy with In Situ Separation by Thermal Desorption: NEMS-IR-TD.

We present a novel method for the quantitative analysis of mixtures of semivolatile chemical compounds. For the first time, thermal desorption is integrated directly with nanoelectromechanical infrared spectroscopy (NEMS-IR-TD). In this new technique, an analyte mixture is deposited via nebulization on the surface of a NEMS sensor and subsequently desorbed using heating under vacuum. The desorption process is monitored in situ via infrared spectroscopy and thermogravimetric analysis. The resulting spectro-temporal maps allow for selective identification and analysis of the mixture. In addition, the corresponding thermogravimetric data allow for analysis of the desorption dynamics of the mixture components. As a demonstration, caffeine and theobromine were selectively identified and quantified from a mixture with a detection limit of less than 6 pg (about 30 fmol). With its exceptional sensitivity, NEMS-IR-TD allows for the analysis of low abundance and complex analytes with potential applications ranging from environmental sensing to life sciences.

Theoretical analysis of flow effects in spatially encoded diffusion NMR.

The measurement of translational diffusion coefficients by nuclear magnetic resonance (NMR) spectroscopy is essential in a broad range of fields, including organic, inorganic, polymer, and supramolecular chemistry. It is also a powerful method for mixture analysis. Spatially encoded diffusion NMR (SPEN DNMR)" is a time efficient technique to collect diffusion NMR data, which is particularly relevant for the analysis of samples that evolve in time. In many cases, motion other than diffusion is present in NMR samples. This is, for example, the case of flow NMR experiments, such as in online reaction monitoring and in the presence of sample convection. Such motion is deleterious for the accuracy of DNMR experiments in general and for SPEN DNMR in particular. Limited theoretical understanding of flow effects in SPEN DNMR experiments is an obstacle for their broader experimental implementation. Here, we present a detailed theoretical analysis of flow effects in SPEN DNMR and of their compensation, throughout the relevant pulse sequences. This analysis is validated by comparison with numerical simulation performed with the Fokker-Planck formalism. We then consider, through numerical simulation, the specific cases of constant, laminar, and convection flow and the accuracy of SPEN DNMR experiments in these contexts. This analysis will be useful for the design and implementation of fast diffusion NMR experiments and for their applications.

Linear spectral mixture analysis of land cover for assessment level sub-pixel using PlanetScope imagery in Dieng Plateau

Information on the land cover is essential as thematic information for planning, controlling, and spatial planning to create sustainable development. Remote sensing is a tool used to solve a problem related to space, environment, and zoning. One of the obstacles in using remote sensing data is the presence of mixed pixels. This research was conducted in the Dieng Plateau. This research aims to calculate accuracy assessment and land cover map fraction using PlanetScope imagery in Dieng Plateau. The remote sensing image is PlanetScope, recorded November 19, 2020. Topographic correction is carried out using the cosine correction method. The Linear Spectral Mixture Analysis (LSMA) method determines each endmember percentage land cover fraction. The field survey was conducted with 74 samples for accuracy testing. Each sample plot measures 10 m x 10 m. The topographic correction results can significantly affect land cover classification results. The result of the LSMA method classification used PlanetScope images to determine the percentage land cover map fraction of bare soil, impervious surface, vegetation, and water in one pixel with an accuracy of MAE 99.76%.

Hazardous and Noxious Substances (HNSs) Styrene Detection Using Spectral Matching and Mixture Analysis Methods

Hazardous and Noxious Substances (HNSs) Styrene Detection Using Spectral Matching and Mixture Analysis Methods

A SUB-PIXEL VISUALIZATION METHOD TO DISPLAY FUZZY PHENOMENA USING RGB COLOR COMPOSITE (CASE STUDY: MANGROVES FOREST)

Natural phenomena boundaries and complexity of features in an urban area due to the low spatial resolution, lead to more pixels of satellite images included in reflectance of multiple land-cover/object components. The sub-pixel information extracting model outputs are fractional cover maps of interested class (end-member), with membership values between zero and one. These maps represented gradient change in only one fuzzy phenomenon boundaries such as vegetation cover. However, in multiple fuzzy class area or complex fuzzy phenomena such as mangrove forests, in the northwest of the Qeshm Island, Hormozgan, Iran, displaying several fractional covers may cause confusion and misunderstanding for the end-user. In this study, an additive color composite and spectral mixture analysis method is utilized for multiple fractional cover representation. The proposed method is implemented on images acquired from Operational Land Imager (OLI) sensor in the Landsat 8 satellite to extract three fractional covers (water, vegetation, and soil). An RGB color composite was used for each type and percentage of fractional cover for given pixel to display fractional cover separately. Based on such RGB color composite represented both quantitative and qualitative information, we used the RGB color solid cube as map legend for better understanding and map interpretation. The result of this study showed that suggested sub-pixel visualization method, gives new vision to the end-user understanding of fuzzy phenomena.

Verification of Protein Changes Determined by 2D-DIGE Based Proteomics Using Immunofluorescence Microscopy.

Fluorescence two-dimensional difference gel electrophoresis (2D-DIGE) is a key biochemical method for the comparative analysis of complex protein mixtures. The technique focuses on the identification and characterization of individual protein species following gel electrophoretic separation making it an important analytical tool of top-down proteomics. In order to verify changes in the expression levels of a particular protein, as determined by 2D-DIGE analysis, and evaluate the subcellular localization of the proteoform of interest, immunofluorescence microscopy is very well suited. This chapter describes in detail the preparation of tissue specimens and the process of cryo-sectioning, as well as incubation with primary antibodies and fluorescently labeled secondary antibodies, followed by image analysis. As illustrative examples, the co-detection of immuno-labeled dystrophin and the Y-chromosome in skeletal muscle are shown, and the localization of calbindin in the cerebellum is presented.

Extraction of rocky desertification information in karst area by using different multispectral sensor data and multiple endmember spectral mixture analysis method

As a typical form of land degradation, karst rocky desertification seriously restricts the development of the regional social economy and seriously threatens the living environment of residents. Fractional vegetation cover (fVC) and bare rock fraction (fBR) are important indicators to identify and evaluate rocky desertification. However, it is a great challenge to obtain fVC and fBR due to the complex terrain and fragmentation of karst rocky desertification areas. In this study, comparisons between Sentinel-2A Multispectral Instrument (Sentinel-2), Landsat-8 Operational Land Imager (Landsat-8), and GF-6 Wide Field View (GF-6) sensors for retrieving fVC and fBR are presented. The multiple endmember spectral mixture analysis (MESMA) and measured spectral dates were used to overcome the limitations of Spectral mixture analysis (SMA). Subsequently, fVC and fBR were validated using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results showed that: 1) Sentinel-2 performed best in estimating fVC and obtained the highest accuracy (R2 = 0.6259, root mean square error = 0.1568, mean absolute error = 0.1215), followed by GF-6 and Landsat 8; in the extraction of the fBR, the performance was relatively the same, and the implementation of Sentinel-2 was also the best (R2 = 0.4911, root mean square error = 0.0714, mean absolute error = 0.0539), followed by GF-6 and Landsat 8. 2) Sentinel-2 images have higher resolution, the narrowest band range, and the most significant number of bands, which can better extract information about fVC and fBR in rocky desertification areas. 3) For the three optical sensors, the spatial resolution of the images is more important to extract the information of fVC and fBR in the rocky desertification areas. 4) In general, the extraction accuracy of fBR is not as good as that of fVC. The complicated ecological and geological environment of decertified areas has more influence on the effect of extraction of the fBR. 5) The Sentinel-2 achieves high accuracy for both fVC and fBR under different-level application scenarios. It thus has great potential for application in rocky desertification information extraction.

Nonlinear manipulation and analysis of large DNA datasets.

Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a type of biased competition is one of the simplest models of lateral inhibition and competition among biological neurons. It has been implemented as DNA-based neural networks, for example, to mimic pattern recognition. However, the utility of DNA-based computation in information processing for real biotechnological applications remains to be demonstrated. In this paper, a biased competition method for nonlinear manipulation and analysis of mixtures of DNA sequences was developed. Unlike conventional biological experiments, selected species were not directly subjected to analysis. Instead, parallel computation among a myriad of different DNA sequences was carried out to reduce the information entropy. The method could be used for various oligonucleotide-encoded libraries, as we have demonstrated its application in decoding and data analysis for selection experiments with DNA-encoded chemical libraries against protein targets.

Online Reaction Monitoring with Fast and Flow-Compatible Diffusion NMR Spectroscopy.

Online monitoring by flow NMR spectroscopy is a powerful approach to study chemical reactions and processes, which can provide mechanistic understanding, and drive optimisations. However, some of the most useful methods for mixture analysis and reaction monitoring are not directly applicable in flow conditions. This is the case of classic diffusion-ordered NMR spectroscopy (DOSY) methods, which can be used to separate the spectral information for mixture's components. We describe a fast and flow-compatible diffusion NMR experiment that makes it possible to collect accurate diffusion data for samples flowing at up to 3 mL/min. We use it to monitor the synthesis of a Schiff base with a flow-tube with a time resolution of approximately 2 minutes. The one-shot flow-compatible diffusion NMR described here open many avenues for reaction monitoring applications.

Evaluation of Greenness of LC-MS Chromatographic Methods for Simultaneous Analysis of Mixtures of Serotonin, Dopamine, Acetylcholine, GABA and Glutamate: AGREE Tool Application

The analytical GREEnness metric (AGREE) tool is widely used as a reliable greenness assessment method for chromatographic analyses. The AGREE tool has the ability to determine the greenness of analytical methods in terms of both quality and quantity, whereas other commonly used methods assess the greenness either quantitatively or qualitatively. Greenness profiles of six chromatographic methods for simultaneous estimations of serotonin, dopamine, acetylcholine, GABA and glutamate were assessed using AGREE and NEMI tools as a case study. The AGREE assessment tool proved to be user-friendly, and provides a full profile of assessment, hence it can be described as the tool of choice for the assessment of LC-MS chromatographic methods. For optimum application, the weights of 4 of the 12 assessment criteria were set high (weight of four) due to their importance, namely criteria number 7 (waste), number 8 (analysis thruput/number of analytes per run), number 11 (toxicity) and number 12 (operator’s safety). Setting proper weights of the assessment criteria contributed significantly to the discrimination of greenness of the compared methods. The selected greenest method for the analysis of the proposed quinary mixture showed an AGREE tool pictogram with a 0.66 score. Additionally, the selected method allows simultaneous estimation of seven constituents in total. It offers high sensitivity, allowing detection of acetylcholine, serotonin and glutamate at levels as low as 2 pg, and dopamine, norepinephrine, GABA and glycine at levels as low as 10 pg, and finally offers fast analysis where all components can be analyzed within 5 min.