dataesr/scientific-paragraphs-categorization huggingface dataset:
db. aist. go. jp) allows downloading of peak-picked data, assignments, and bitmap images; Chemspider (www. chemspider. com) is a free but not open database of chemical compounds that provides NMR raw les as subsidiary data for a limited number of compounds; The Human Metabolome Database (http://www. hmdb. ca/)  386 ocuses on human metabolites and contains raw NMR data for selected compounds; Biological Magnetic Resonance Data Bank (http://www. bmrb. wisc. edu/) 387 seeks to provide qualitative and quantitative NMR data (processed, assigned; not raw) of biological macromolecules and metabolites; the Open Spectral Data Base (http://osdb. info/)  388 is an open source project intended to be extended, enhanced, and used for open science data sharing by its users; C6H6. org is an open source project, built using recent technologies and running inside a web browser, offers means to store, share, analyze, and interact with raw NMR data.
Independently from, or in parallel to, classical publications outlets, authors can take immediate action by depositing raw NMR data into publicly accessible repositories. Institutional (e. g. , university and research institution based) and global (e. g. , Harvard Dataverse; dataverse. harvard. edu) solutions exist already for this purpose and offer sufficient exibility to share raw NMR data today, while allowing for their inclusion into a global repository envisioned for the future. 10. 4. 3 Unied global repository. Envisioned collectively by all authors of this review, the ultimate action item is the implementation of a global, ideally unied repository for raw NMR data. In its ideal implementation, such a repository will integrate the collective experience, the most suitable features, as well as all available data from existing repositories and projects, as outlined and summarized in Section 10.
3. As rationalized in the same section above, an evolutionary design should be employed when building this new, open-access repository, aimed at fullling all the key features. Such a unied repository should be all-inclusive with regard to the type of collected NMR data and avoid any bias towards certain approaches regarding the utility and/or future applications of the data. Importantly, the repository should support equally all methods for NMR-based structural dereplication such as 1D 1 H, 1D  13  C, 2D HSQC, and any hybrid approaches. Notably, the foremost feature of the envisioned global repository is long term sustainability, as it represents the quintessential challenge research operations in general and databases in particular for environments that depend on extramural funding and lack independent revenue streams.
Accordingly, it shall be emphasized that, while the raw data (FID) archive proposed by this Raw Data Initiative is both a prerequisite and a major undertaking, it still represents a minimalist version of a greater NMR data collection for all kinds of purposes, including dereplication. A subsequent evolutionary development of methods is still needed to make (additional) use of the raw data, producing new insights and/or accelerating current processes. For example, while making a 1D 1 H NMR FID publicly available today provides a highly conclusive means of dereplication, it still requires a tool that translates the non human-readable FID into a human readable spectrum. While more rapid and/or human intuitive methods can be developed in subsequent steps to achieve the same goal, they will most likely depend on the same and/or additional raw NMR data.
Many plant tissues can be observed thanks to autofluorescence of their cell wall components. Hyperspectral autofluorescence imaging using confocal microscopy is a fast and efficient way of mapping fluorescent compounds in samples with a high spatial resolution. However a huge spectral overlap is observed between molecular species. As a consequence, a new data analysis approach is needed in order to fully exploit the potential of this spectroscopic technique and extract unbiased chemical information about complex biological samples. The objective of this work is to evaluate multi-excitation hyperspectral autofluorescence imaging to identify biological components in wheat grains during their development through their spectral profiles and corresponding contribution maps using Multivariate Curve Resolution -Alternating Least-Squares (MCR-ALS), a signal unmixing algorithm under proper constraints.
For this purpose two different scenarios are used: 1) analyzing the total spectral domain of data sets using MCR-ALS under non negativity constraint in both spectral and spatial modes; 2) analyzing a reduced spectral domain of data sets using MCR-ALS under non negativity in both modes and trilinearity constraint in spectral mode. Considering the original instrumental setup and our data analysis approach, we will demonstrate that extracted contribution maps and spectral profiles of constituents can provide complementary information used to identify molecules in complex biological samples.
Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is one of the signal unmixing techniques that can provide pure spectra and contribution maps of different components in the samples. 21 It is an iterative algorithm that solves the bilinear model with no prior knowledge. This algorithm extracts from the data set pure contributions (concentration and spectral profiles) of all compounds present in the sample from suitable alternating least-squares optimization subjected to different constraints. 3] [24] [25]  When data sets are analyzed by MCR-ALS, the result might be challenging due to lack of unique solutions, which is an intrinsic characteristic of bilinear matrix decompositions if incomplete information is available about the system. 26 The resulting uncertainty may be dramatically large in certain cases, e.
g. , when extensive profile overlap occurs in one of the data modes. 8] [29] [30] [31]  A Trilinearity constraint can guarantee accurate unique profiles for constituents with trilinear structure. 32, 33 To fulfill the trilinearity condition, the augmented data matrix should contain some similar factors in its sub-matrices, factors which should not be affected by experimental conditions so that they share a common spectral shape. Only their areas (and vertical heights) should proportionately change according to the constituent concentration. It is a very strong constraint that forces the decompositions to give unique solutions under mild conditions. The objective of this work is to evaluate the method of multi-excitation hyperspectral imaging by confocal microscopy combined with the exploration of second order data sets with the MCR-ALS approach to identify different components in the developing wheat grain based on their autofluorescence properties.
Contribution maps and spectral profiles of different compounds are complementary information which will be extracted from the exploration of this kind of data sets. For this purpose two different scenarios are used: 1) Analyzing the whole spectral domain -obtained by merging the data sets of each excitation wavelength -using MCR-ALS under non negativity constraint in both modes; 2) Analyzing the reduced spectral domaincommon to all excitation wavelength -using MCR-ALS under non negativity in both modes and trilinearity constraint in spectral mode in order to get second order advantage. The two approaches will be compared and discussed on the basis of extracted contribution maps and pure spectral profiles of all components. These results will be also compared with images generated from the standard method based on emission signal integration.
62 × 0. 62 µm. As a consequence each hyperspectral data cube contained 262,144 emission spectra. Moreover for each field of view, three hyperspectral images were recorded by collecting emitted light from 404 to 714 nm for UV excitation, 504 to 744 nm for blue excitation and 574 to 744 nm for green excitation with a 10 nm step between spectral variables. A dichroic mirror was used to filter the three excitation wavelengths. For each development stage, two or three serial sections were imaged. Two regions per section were analyzed: (1) opposite to the crease and (2) on the side of the grain, called dorsal and side respectively, with the objective to visualize all the tissues of the wheat grain (Figure  1 ). In the case of the youngest grains, one field of view was not always sufficient. In this case, two images were acquired to observe the whole region.
As a consequence, an excitation emission autofluorescence matrix is acquired for each pixel of the sample surface instead of only one emission spectra as it is usually the case for conventional fluorescence analysis. However, this matrix is only partial because the spectral domains corresponding to each excitation wavelength differs. In order to analyze the acquired data sets by using a curve resolution method like MCR-ALS, a 2D matrix of data is needed. Consequently each 3D autofluorescence hyperspectral image matrix corresponding to each excitation is unfolded to a matrix with x×y rows and λ em,i columns (i=1, 2 and 3) as shown in figure  2b. Unfolded version of the three cubes.
Data analysis -the MCR-ALS approach The MCR-ALS method is used to decompose simultaneously a hyperspectral image into the pure spectra of the image constituents and corresponding contribution maps. MCR-ALS is based on a bilinear model which assumes that each observed spectrum is a linear combination of pure components spectra present in the system. 36, 37 This model can be written in matrix form as: D=CS T + E (1) Where, D is the unfolded hyperspectral image data set, C is the matrix of the relative amounts or contributions, S T is the pure spectra matrix and E is the matrix associated to non-modeled part of data and potentially only noise. Naturally signal intensity in every pixel of the image should not be negative and neither should the contributions of the different constituents. It is therefore logical to use non-negativity constraint during alternating least squares optimization.
The spectral matrix S T is also normalized to avoid scale ambiguity. At the end of the ALS optimization procedure, the optimized C matrix can be refolded, to recover a 2D image (distribution map) of the contributions of every component. To evaluate the quality of extracted profiles obtained from the MCR-ALS procedure, the percentage of lack of fit (lof) and the percentage of explained variance (R 2 ) are calculated according to the two following equations: lof (%) = 100 × ∑ ∑ (2) where is the element of the hyperspectral image data set D, and is the corresponding element of this data matrix recalculated by MCR-ALS. This lack of fit value gives a measure of the fit quality in relative terms with the same units as the measured data, and comparable to the experimental relative error estimation.
For the explained variance, R 2 is calculated as: R 2 = 100 × 1 - ∑ ∑ (3) where are the elements of the E matrix. The main advantage of this algorithm is the amount of information that can be included in the optimization process and the ability of working with either a single data matrix or multiset data structures i. e. using simultaneously several data cubes. However it is known that such an approach does not always extract unique solutions if not well managed. Therefore additional chemical information and well-selected constraints used in the decomposition process can significantly reduce or even eliminate this uncertainty. One of the most well-known constraints to ensure uniqueness of extracted solutions in MCR-ALS is trilinearity. Due to the trilinearity constraint, data arrays should contain some similar factors in their modes while factors should be independent from experimental conditions.
37 Trilinearity is a very strong constraint that forces trilinear decompositions to have unique solutions under mild conditions. 38, 39 An important achievement in the analysis of complex data matrices resulted when Multivariate Curve Resolution methods were applied to several data matrices simultaneously to the so-called Matrix Augmented -Multivariate Curve Resolution -Alternating Least Squares, MA-MCR-ALS. 40 Generally speaking, resolution ambiguities and rank deficiency problems in the analysis of two-way data sets can be reduced significantly if it is possible to analyze data structures with more information. MA-MCR-ALS, as an extension of MCR-ALS, can be easily adapted to the trilinear analysis of the three-way data sets generated from data matrices with the same row or column, taking advantage of their structure.
When the trilinearity constraint is used in the MA-MCR-ALS framework, profiles of a component in different data matrices are forced to have a common shape during each ALS optimization but may differ by a scaling factor. When this constraint is inserted into the ALS iterative optimization procedure, it forces the shape of the loading vectors to be the same in all sub-matrices. An interesting aspect of the trilinearity constraint in the ALS optimization is that its use is optional for each component of the system. MCR-ALS optimization always starts with the generation of initial estimates of either concentration or spectral profiles. In general, the use of chemically meaningful estimates is an essential factor that can lead to not only a rapid convergence of the extractions but also a decrease in ambiguity of solutions in some cases.
Different methods can be used to find suitable initial estimates to start the MCR-ALS calculation. The so-called SIMPLISMA based on the concept of purest variables is usually used to calculate them. 41 However it was not possible to use it in this work due to the very high number of spectra in the considered multiset analysis. We have, therefore, decided to use the Kennard-Stone (KS) algorithm in order to generate initial estimates. 42 This algorithm allows for the selection of spectra from all parts of the data space even in the case of nonhomogeneous distribution. It starts by finding the two most distant spectra in the data set using Euclidean distance, and then continues with other points until the selected rank is reached. The rank of the data set i. e. its total number of independent signals is usually obtained from Principal Component Analysis (PCA) considering the magnitude of singular values.
Singular values related to chemical contributions are typically large, whereas singular values related to noise are smaller and similar among themselves. This diagnostic can be complemented by looking at the emergence of noisy patterns in scores and loadings profiles, typical in noiserelated components. When in doubt, few MCR models with different number of components can be calculated. The final model is selected as the smallest one providing an optimal model fit and chemically meaningful resolved profiles. 43 In this work for most of the data sets several MCR models were calculated and the selected one had minimum residuals and optimal fit. In order to extract spectral profiles and contribution maps of all active components in the sample, MCR-ALS approach has been applied following two different scenarios.
In a first scenario, an augmented data set D was obtained by considering the whole spectral domain of each data cube and merging the corresponding three 2D unfolded matrices as shown in figure  3b. In this case, MCR-ALS was applied under non-negativity constraint only in order to extract spectral autofluorescence emission profiles and contribution maps of active components. As explained in the data collection section, several regions were observed for each grain section and several serial sections were also observed for each development stage, resulting in several multisets (in red and orange in figure  3a ). In the figure  3b , purple, blue and green cubes correspond to the three different excitation wavelengths used while light and dark colors correspond to two sample regions of interest.
Then we use column-and row-wise data augmentation to fuse all matrices and generate a global matrix D. Lastly, the augmented data set is used to extract pure concentration and spectral matrices using MCR-ALS (Figure  3c ). To tackle the large volume of data from the 40 multi-excitation hyperspectral images (i. e. more than 10 million of emission spectra), augmented data sets were built and analyzed separately for each development stage that still correspond to the multivariate analysis of 1. 3 million spectra each. A second scenario has been considered in this work because rotational ambiguity (i. e. uncertainty of MCR-ALS results) cannot be eliminated in the majority of cases only using the non-negativity constraint. Therefore it has been decided to apply additionally the trilinearity constraint in order to reach uniqueness of solutions in MCR-ALS, or at least verify if other extractions are obtained in these new conditions.
However, to analyze the acquired data set under the trilinearity constraint, it must have a trilinear structure at least for one mode. In the case of fluorescence, the shape of the emission spectrum for a given fluorophore is largely independent from excitation wavelength. Therefore the recorded autofluorescence hyperspectral images using different excitation wavelengths are potentially suitable for applying trilinearity constraint in spectral mode. However due to our specific instrumental setup, hyperspectral images have not exactly the same spectral domain, a necessary condition for applying this new constraint in MCR-ALS. As a consequence, a reduced spectral domain (574-714 nm) common to the three unfolded matrices has been considered. This common spectral window is represented by black open rectangles in Figure  3.
The complexity of the data structure is even more striking when looking at spectra extracted from specific areas of the considered wheat section. Although these four specific tissues are known to contain different fluorescent molecules or different concentration of several ones, the global shape of emission spectra is almost the same, only relative intensity changes being observed for different excitation wavelengths. In conclusion, a simple integration approach does not allow us to obtain unbiased information about the fluorescent molecules present in the grain tissues due to a huge spectral overlap of fluorescence. 3, 6, 45 As a consequence, it was decided to apply the signal unmixing approach MCR-ALS in order to extract pure fluorescence signatures of molecules in the next section of the paper.
Signal unmixing using MCR-ALS using the whole spectral domain All acquired data sets of each specific stage (more than 1. 3 million of emission spectra) were analyzed together considering a column-wise data augmentation. In other words, we had eight augmented data sets corresponding to the eight development stages. Each data set has been analyzed separately using MCR-ALS under proper constraint. The chemical rank for each data set has been determined using singular value decomposition (SVD). 22 However it was sometimes necessary to calculate different MCR models with different number of components when SVD information was not so clear. Considering the principle of parsimony, the final MCR model was selected as the one with the smallest chemical rank, a good model fit and chemically meaningful resolved profiles.
Signal unmixing using MCR-ALS using trilinearity constraint Trilinearity constraint can potentially guaranty accuracy of unique extractions from MCR-ALS. However, due to the trilinearity constraint, the augmented data set should contain some similar factors in their modes while the factors should be independent from experimental conditions. Thus profiles of a component in different data matrices are forced to have common shape during each ALS optimization but may differ by a scaling factor. During the ALS iterative optimization procedure, the constraint forces the shape of the loading vectors to be the same in all sub-matrices. In order to analyze the above mentioned data sets using MCR-ALS under trilinearity constraint, it was necessary to have the same spectral domain of emission. The spectral region of emission (574-714 nm) common to the three excitations was then considered.
The common part of three recorded data sets were augmented row-wise and analyzed using MCR-ALS under non-negativity in both modes and trilinearity in spectral mode. Under these new conditions, only six components have been extracted with MCR-ALS with a mean LOF value of 4% and a mean R 2 value of 0. 99. Their presence and absence were exactly similar to the ones given in Table  1  except for component G which was absent. In fact, component G was effectively present in grain sections but the reduction of the spectral domain prior MCR in this second scenario has led to the loss of specific spectral information. MCR-ALS extracted spectral profiles under non-negativity and trilinearity constraints of the six components A-F are shown in figure  8. Generally speaking, we can observe that for a given component, extracted spectral profiles are more consistent between stages than the ones in Figure  6.
Unraveling the complex structure and functioning of microbial communities is essential to accurately predict the impact of perturbations and/or environmental changes. From all molecular tools available today to resolve the dynamics of microbial communities, metaproteomics stands out, allowing the establishment of phenotype-genotype linkages. Despite its rapid development, this technology has faced many technical challenges that still hamper its potential power. How to maximize the number of protein identification, improve quality of protein annotation, and provide reliable ecological interpretation are questions of immediate urgency. In our study, we used a robust metaproteomic workflow combining two protein fractionation approaches (gel-based versus gel-free) and four protein search databases derived from the same metagenome to analyze the same seawater sample.
The resulting eight metaproteomes provided different outcomes in terms of (i) total protein numbers, (ii) taxonomic structures, and (iii) protein functions. The characterization and/or representativeness of numerous proteins from ecologically relevant taxa such as Pelagibacterales, Rhodobacterales, and Synechococcales, as well as crucial environmental processes, such as nutrient uptake, nitrogen assimilation, light harvesting, and oxidative stress response, were found to be particularly affected by the methodology. Our results provide clear evidences that the use of different protein search databases significantly alters the biological conclusions in both gel-free and gel-based approaches. Our findings emphasize the importance of diversifying the experimental workflow for a comprehensive metaproteomic study.
, 2016) , or (iii) microcosms  (Russo et al. , 2016)  approaches. Metaproteomics on marine ecosystems is a rapidly expanding field that involves a series of challenging steps and critical decisions in its workflow  (Wilmes et al. , 2015; Heyer et al. , 2017; Matallana-Surget et al. , 2018; Saito et al. , 2019). The marine metaproteomic workflow consists mainly of four steps: (i) sampling and protein extraction, (ii) protein separation, (iii) mass spectrometry, and (iv) protein identification/annotation  (Wöhlbrand et al. , 2013). Until now, standardized experimental protocols are still missing, leading to methodological inconsistencies and data interpretation biases across metaproteomic studies  (Leary et al. , 2013; Tanca et al. , 2013; Timmins-Schiffman et al. , 2017). Protein identification strongly relies on both the quality of experimental mass spectra (MS) and the comprehensiveness of the protein search database (DB)  (Wöhlbrand et al.