dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Mass spectrometry remains the privileged method to characterize proteins. Nevertheless, most of the spectra generated by an experiment remain unidentified after their analysis, mostly because of the modifications they carry. Open Modification Search (OMS) methods offer a promising answer to this problem. However, assessing the quality of OMS identifications remains a difficult task. Methods: Aiming at better understanding the relationship between (1) similarity of pairs of spectra provided by OMS methods and (2) relevance of their corresponding peptide sequences, we used a dataset composed of theoretical spectra only, on which we applied two OMS strategies. We also introduced two appropriately defined measures for evaluating the above mentioned spectra/sequence relevance in this context: one is a color classification representing the level of difficulty to retrieve the proper sequence of the peptide that generated the identified spectrum ; the other, called LIPR, is the proportion of common masses, in a given Peptide Spectrum Match (PSM), that represent dissimilar sequences.
These two measures were also considered in conjunction with the False Discovery Rate (FDR). Results: According to our measures, the strategy that selects the best candidate by taking the mass difference between two spectra into account yields better quality results. Besides, although the FDR remains an interesting indicator in OMS methods (as shown by LIPR), it is questionable: indeed, our color classification shows that a non negligible proportion of relevant spectra/sequence interpretations corresponds to PSMs coming from the decoy database. Conclusions: The three above mentioned measures allowed us to clearly determine which of the two studied OMS strategies outperformed the other, both in terms of number of identifications and of accuracy of these identifications. Even though quality evaluation of PSMs in OMS methods remains challenging, the study of theoretical spectra is a favorable framework for going further in this direction.
Software usually infer the identification of an experimental spectrum from its similarity to reference spectra. When a peptide carries a modification, its mass is by nature modified. This mass modification prevents its identification by conventional methods, which compare each experimental spectrum with only a restricted set of reference spectra that approximately share the same mass in order to avoid excessive runtime. Some known modifications can be included in the modeling of reference spectra, but their number must remain low to circumscribe the search space. In 2015, the study conducted by Chick et al. [4]  renewed the interest in so-called Open Modification Search (OMS) methods known since 2005  [5, 6] , with the promise of revealing unexpected modifications which would have been lost otherwise, and consequently gaining better rates of spectra identifications.
Many scores exist to evaluate the similarity between two spectra, which all take into account, at a certain level, the number of peaks (or, equivalently, of masses) that are shared by the two spectra, a number called shared peaks count (SPC). Once the complete list of PSMs is sorted by score, a threshold based on a measure of statistical significance determines which PSMs are validated according to a false discovery rate (FDR)  [7]. Since 2015, several very rapid OMS methods have been developed. For a few of them, the reference spectra come from consensus templates already observed, identified and stored in spectral librairies  [8] [9] [10] [11] ; for others, the reference spectra, so-called theoretical spectra in that case, are generated from a protein database by simulating an ideal fragmentation of peptides (see Fig.
In the first strategy (called Strategy1 in this paper), the best PSM is chosen according to a score that does not take the m into account. The second strategy (called Strategy2) tries to improve the alignment-and thus the score (Fig. 2 )-of all the PSMs returned for a given experimental spectrum according to m before the choice of the best PSM. In order to determine the most efficient strategy, a prerequisite was to be able to implement both strategies using the same software, which implies the availability of very efficient spectra comparison and alignment algorithms. The SpecOMS software  [14] , which we have previously developed, fulfills these conditions. To compare in-depth the limits of each strategy, we decided to ground this study using the theoretical spectra derived from the human proteome, considering successively each theoretical spectrum in the role of an experimental spectrum.
Results We successively implemented Strategy1 and Strategy2 (see "Methods" section) to compare all the theoretical spectra generated from the human proteome (572,063 spectra) against a database merging the target and decoy human proteins (1,148,608 spectra). To denote unambiguously the role that each theoretical spectrum can alternately play, we call it bait when it plays the role of an experimental spectrum and hit when it represents the theoretical spectrum modeled from the protein database. By extension, we also designate by bait and hit the peptides that generated respectively these theoretical spectra. Any pair (bait,hit) having an SPC greater than or equal to our initial threshold 7 (see "Methods" section) is called a candidate PSM. Any PSM returned by our software will be called best PSM.
Thus, 3% to 10% of the theoretical spectra have at least one "neighbor peptide" that shares a sufficient number of masses so that their similarity is not estimated to be considered as the result of chance. It must be remembered that a hit is supposed to directly identify a bait for PSMs with m = 0. We know that it cannot be the case in our dataset, since we set SpecOMS so as to forbid PSMs involving the same peptide. Indeed, very few PSMs were validated by the target-decoy method in G 1 (672 PSMS with Strat- egy1, 201 PSMs with Strategy2), which is consistent with the composition of this group. On the opposite, the G 2 and G 3 groups represent most of the validated PSMs, but one may wonder to what extent the information given by these PSMs is enough to restore the correct amino acid sequence of the baits-a sine qua non condition to consider a posteriori an identification as correct.
Besides, we can note the high proportion of PSMs from the human proteome classified as Green when SPC ≥ 7. If all the hits associ- ated to those Green PSMs came from the target database, it would be come as very good news because these PSMs refer to peptides that differ by unambiguous editing operations from their "closest neighbor" (from 11% in Strategy1 to 24% in Strategy2). Since it is not the case, it means that the decoy database is not only constituted of "incorrect sequences" as it should be. Then, it is clear that for both strategies, the presence of many Green PSMs in the decoy database hinders the identification of the baits.
Hence, the fact that Strategy2 behaves better than Strategy1 (with respect to the FDR in this case) was not necessarily easily predictable. The performances obtained by Strategy2 also lead us to conclude that, among the two, Strategy2 is the one that should be implemented in OMS software. Spectral Alignment  [5]  was already based on a score involving m to select the best PSM by using, similarly to Strategy2, a realignment process. However, it should be noted that Strategy2 requires to realign very efficiently pairs of spectra if one wants to maintain fast execution time. More recently, MODPlus  [16]  and Open-pFind  [15]  select the set of candidate PSMs with sequence tags and then perform realignments. ANN-SoLo  [10, 11]  and the Hybrid search  [9]  approximate the improved score obtained taking modified peaks into account (ANN-SoLo's shifted dot product, hybrid search's cosine similarity).
We have conducted our study in a "strict" environment, namely comparing theoretical spectra only, and using a score (SPC) that only takes common peaks into account. Although this could be seen as a limitation, it can also be argued that comparing theoretical spectra is an opportunity to provide an overview of the proximity of tryptic peptides extracted from the human proteome from a mass spectrometry point of view. Besides, we place ourselves in an "adversary" context, where no redundancy is allowed, and thus where no PSM corresponds to an exact sequence matching. Concerning SPC, it can be noted that it is, in any method, systematically taken into account at some level in the scoring function. In our case, SPC allowed us to be context and data independent: we consider having no particular knowledge of the dataset nor the spectra at hand, and conducted our experiments in an "unsupervised" manner.
Moreover, Strategy2 (as implemented here) always determines the exact value for our score, whereas other OMS methods use approximate scores, slightly surestimating them, which may have an impact on the results. By comparing two OMS strategies with theoretical peptides and new indicators, we also developped an environment which allowed us to see and understand elements that are more difficult to see in an experimental context. This protocol could be used to understand principles that are at the heart of other (OMS) MS identification tools, perceive their strengths and weaknesses, in order to configure and calibrate them. For example, the idea behind the color classification is not specific to the SpecOMS software we used nor to the mass shifting implied by Strategy2, and could help assessing the efficiency of any OMS research tool.
Furthermore, we believe that our two indicators (used here in a context where both spectra are theoretical) can be adapted-to some extentto classical MS/MS experiments in which theoretical spectra need to be to experimental spectra. Our study also calls for several possible perspectives and extensions, which we briefly mention here: apply our study to experimental contexts (which would require to adapt LIPR and color classification), use a more elaborate score than SPC, improve our color classification algorithm so as to "explain" more PSMs from the current Red class, allow more than one location to explain m , and finally use other types of decoy database. Any combination of the above suggestions could also be considered.
Conclusion Despite recent progress, much remains to be done concerning the identification of MS/ MS spectra carrying modifications, and the quality evaluation of OMS methods. The OMS methods present different strategies to identify the best PSM and, in this paper, we compared two such strategies that are well represented in the recent methods. We led this study in a strictly identical environment for both strategies by implementing them in the same software (SpecOMS  [14] ) and on an ideal dataset formed by theoretical spectra only, that we compare to itself, in order to get rid of identification difficulties  [22]  due to the imperfection of experimental spectra (noise, missing peaks, etc. ). In addition to the conventional FDR, we introduced two new criteria to evaluate the quality of PSMs.
For each of the observed criteria, Strategy2, which attempts to align m between spectra before choosing the best candidate PSM, has always proven to be better. Overall, Strat-egy2 promotes the PSMs where the hit is derived from the bait by easily identifiable editing operations. Then, more often, the sequence of the bait can be properly determined with Strategy2. We can note that, regardless of the origin, a modification has the same impact on spectra and therefore, even though we did not perform the comparison on experimental spectra, we can extrapolate the superiority of Strategy2 in the analysis of any experimental dataset. According to these results, OMS methods should implement Strategy2 but we have to emphasize that Strategy2 requires an efficient algorithm to find the best alignment of spectra on a very large number of candidate PSMs, the likely reason why it is not done in all OMS methods.
Peptide identification using SpecOMS We implemented two strategies to find the best PSMs using the SpecOMS software  [14]. Next, we applied these two strategies to compare the large set of theoretical spectra generated from the human database against themselves. To perform this comparison between theoretical spectra, we added a new functionality to SpecOMS (in the form of a new search mode). A peptide that plays the role of an experimental spectrum in a peptide spectrum match (PSM) is called the bait, whereas a peptide associated to bait in a PSM is called a hit. Parameters were set in such a way that SpecOMS extracted from its data structure SpecTrees  [25]  all pairs of spectra of the form (bait,hit) whose shared peaks count (SPC) is greater than or equal to 7. The above threshold of 7 appears to be a good trade-off for our study: first, it should not be too low so as to avoid many PSMs with a relationship carrying little information between the hit and the bait.
Data The human proteome was downloaded from Ensembl 99, release GrCh38  [27]  on the Ensembl FTP server. Proteins predicted with the annotation "protein coding" were added to 116 contaminant proteins downloaded from the cRAP contaminant database. Proteins are processed by SpecOMS in order to find the best PSM for each peptide of the database. This is done by computing the shared peaks count (SPC) between every peptide (seen as a theoretical spectrum) of the initial database, compared to all peptides (except itself ) from both the target and decoy databases. In Strategy1, the best PSM is chosen according to the raw shared peaks count (or raw SPC). In Strategy2, an extra computation is realized for all candidate PSMs with m = 0 , consisting in shifting the peaks according to m at all possible locations, and keeping the best induced SPC (or shift SPC) Fig.
7  Determining the best PSM in each strategy. Suppose, fictitiously, that a given bait is to be compared to 4 peptides (called hits). Hit 1 is discarded as its raw SPC with bait is below the imposed threshold of 7. Hits 2, 3 and 4 are candidate PSMs for bait. If m = 0 for hits 2, 3 and 4, a shift may be applied, and in that case shift SPC is obtained (with shift SPC ≥ raw SPC by definition). In Strategy1, the best PSM for bait is hit 2, as it is based on raw SPC. In Strategy2, the best PSM for bait is hit 3, as it is based on shift SPC The resulting set of proteins is referred to as the target database. After in silico digestion by trypsin, which cleaves after lysine and arginine (K and R), peptides whose lengths are out of the range 7 to 30 amino acids (inclusive), together with those containing the letter 'X' (representing an unknown amino acid-about 3%) were removed.
Measuring the quality of PSMs Different measures were performed on the two datasets obtained with Strategy1 or Strategy2, in particular to determine to which extent the chosen identification strategy impacts the results. The first classical measure that we used is the number of PSMs we can validate at a given False Discovery Rate (FDR). We calculated the FDR as the proportion of best PSMs of the form (bait,hit) for which the hit is a decoy, over the total number of best PSMs. In this work, we were essentially interested by PSMs for which the FDR is less than 1%.
Number of PSMs obtained by Strategy2 according to shift SPCThe best PSMs provided by SpecOMS are separated into three groups based on m , and subdivided in each group by the origin of the hit (target or decoy database). An FDR < 1% is reached whenever shift SPC is greater than or equal to 21 (row in bold). Only results for odd minimum shift SPC are displayed here: indeed, shift SPC is always even, since a match always involves both a b-ion and a y-ion after realignment. The results we obtain are thus the same for consecutive odd and even shift SPC (except for a few exceptions due to overlapping masses).
Availability of data and materials The human proteome was downloaded from Ensembl 99, release GrCh38 on the Ensembl FTP server ftp://ftp. ensem bl. org/pub/relea se-99/fasta /homo sapie ns/pep/. Proteins predicted with the annotation "protein coding" were added to 116 contaminant proteins downloaded from the cRAP contaminant database ftp://ftp. thegp m. org/fasta /cRAP. The SpecOMS software is available at https ://githu b. com/domin ique-tessi er/SpecO MS.
Orchard sub-model The orchard sub-model is more complex because, due to the perennial nature of fruit species, it is essential to take into account their inter-annual dynamics. We are not aware of any research that has explored fruit tree dynamics growing in sub-optimal conditions. We therefore used data from pure crop systems, which we calibrated according to experts' knowledge to express their dynamics with a suboptimal management. The orchard sub-model was calibrated in a three-hour collective workshop in February 2020, bringing together 10 fruit tree experts (7 fruit tree technicians and 3 researchers. The production dynamics of each fruit tree species were characterized according to three scenarios: (i) optimal investment, corresponding to the systematic prioritization of trees in the system management, so that they develop optimally; (ii) minimal investment, where only essential operations such as irrigation and training are carried out; (iii) the intermediate scenario stands for an in between situation where tree operations are not done perfectly or are staggered in time.
Acknowledgments The present work was carried out with the support of the  Fondation de France  (grant  n°00064844 ) which is hereby thanked. We also thank  Pierre-Eric Lauri ,  François Warlop ,  Solène Borne ,  Thierry Girard ,  Laurent Poulet ,  Guilhem Severac ,  Hugues Reynold ,  Julia Frezel  and  Emmanuelle Filleron  for their contribution to the calibration workshop and their valuable advice. We would especially like to thank reviewer 1 for his/her very comprehensive and constructive review.
In the 12,000 years preceding the Industrial Revolution, human activities led to significant changes in land cover, plant and animal distributions, surface hydrology, and biochemical cycles. Earth system models suggest that this anthropogenic land cover change influenced regional and global climate. However, the representation of past land use in earth system models is currently oversimplified. As a result, there are large uncertainties in the current understanding of the past and current state of the earth system. In order to improve representation of the variety and scale of impacts that past land use had on the earth system, a global effort is underway to aggregate and synthesize archaeological and historical evidence of land use systems. Here we present a simple, hierarchical classification of land use systems designed to be used with archaeological and historical data at a global scale and a schema of codes that identify land use practices common to a range of systems, both implemented in a geospatial database.
The classification scheme and database resulted from an extensive process of consultation with researchers worldwide. Our scheme is designed to deliver consistent, empirically robust data for the improvement of land use models, while simultaneously allowing for a comparative, detailed mapping of land use relevant to the needs of historical scholars. To illustrate the benefits of the classification scheme and methods for mapping historical land use, we apply it to Mesopotamia and Arabia at 6 kya (c. 4000 BCE). The scheme will be used to describe land use by the Past Global Changes (PAGES) LandCover6k working group, an international project comprised of archaeologists, working group LandCover6k), which in turn received support from the U.
Weindl et al. 2017 Introduction: Earth systems models, land cover, and the past Although earth system models are often seen as tools for exploring the future, they rely in part on understandings of the past, including models of land cover change through time. It is the aim of the LandCover6k working group, formed in 2015 as a collaboration between archaeologists, historians, geographers, paleoecologists, and modelers, to improve the basis for incorporating past land cover change into earth system models (http://pastglobalchanges. org/science/ wg/landcover6k/intro). We seek to do this by producing reconstructions of past vegetation and human land use through time that are grounded in paleoenvironmental and archaeological data. In order to explain how paleoenvironmental and archaeological data can improve earth system models, it is necessary to review these models and the limitations of their approach to land cover.
All current ALCC scenarios lack thematic information on the effects of past human land use, such as those created by large-scale burning, plowing, irrigation, and livestock management. Finally, ALCC scenarios differ significantly from one another and compare poorly with independent reconstructions  [15]. In spite of these inherent limitations, ALCC models are used as part of Land Use Harmonization Models  [10]. It is therefore of crucial importance that ALCC models are improved, as ALCC scenarios provide data essential for earth system modeling of the past, present, and future. A major current research challenge crosscutting the social, biological, and physical sciences is thus to improve our understanding of the scope of early human land use, resultant changes in land cover, and consequent feedbacks to both cultural and climatic systems.
Generating such data is a difficult task, requiring the synthesis of heterogeneous qualitative and quantitative datasets into general regional narratives of historical land use through time and then translating such narratives into spatial form within a digital map and database. Additionally, such work should aggregate data on the effects of past land use systems, including changes in land cover and perturbations to biogeochemical cycling through water management, changes in species composition, and disturbance such as fire. Taking on this enormous task, however, is essential both to improve ALCC models and to empower archaeologists and historians to play a larger role in the writing of critical narratives of human-environmental interaction. In this paper, we lay the groundwork for archaeological and historical synthesis of data on past land use by defining a land use classification scheme.
That is, not all people have, or had, identical impacts on the environment  [31]. Differential forms and rates of consumption are made possible by land use practices (e. g. , farming) and by other forms of production (e. g. , metal working). Land use, the mechanism by which resources such as food, fuel, and other goods (e. g. , prestige items, technological items) are produced, emerges as a critical mediator between 'raw' population human numbers and realized environmental impacts  [32]. A great deal of uncertainty still surrounds the Holocene CO 2 record, and this uncertainty is fostered by the lack of high-quality data-based syntheses of global land use and of anthropogenic land cover change for the Holocene. Syntheses of historical land use are less developed than those of land cover, in part because of higher data heterogeneity in the archaeological and historical record and the larger size and disciplinary diversity of the scholarly communities involved.