dataesr/scientific-paragraphs-categorization huggingface dataset:
A set of simulated mass spectra containing 15 differences (variations in peak intensity, addition and removal of peaks) was presented to the panelin triplicate and not consecutivelywithout prior notice. The variability between the results of these triplicates gave an average standard deviation of 2. 3 observed differences per individual, with mean and median values of 9. 6 and 9. 7, respectively, and a range of 0-19. Considering the variability between panelists, a standard deviation of 20. 6 differences was determined over the whole dataset, with mean and median values of 85. 6 and 90, respectively, for a total 148 differences to be identified. The number of observed differences ranged from 31 to 122. The number of differences identified varied to the point that one operator would conclude that two spectra were almost identical while another would consider them significantly different!  26 e problem is substantially more complicated when comparing not only spectra but series of spectra corresponding, for example, to samples taken upstream and downstream of a treatment plant.
A large variation in an ion count between upstream and downstream spectra may not be significant if the magnitude of variation is equal within and between the downstream and upstream populations; changes in the abundance of this ion reflect only the intrinsic chemical variability of the environment and are not a relevant marker of the impact of the plant. On the other hand, a slight change in the abundance of an ion between "upstream and downstream spectra" may be significant if the abundance is almost constant within each population; it then reflects a real effect of the plant on water quality. The SPIX software was created to remedy the observed fact that it is impossible for an operator to determine what makes sense based on simple observation of complex datasets, especially since the data are subject to intrinsic variability.
| MATERIALS AND METHODS Prior to performing any statistical analysis of the data, pre-processing is required to identify and align significant peaks in the data. The method used for detection and alignment actually depends on the type of data available: • When the device provides data in xml format, these data have already been filtered and contain only the most significant peaks. These peaks are then aligned by using the mspalign function of the Bioinformatics Toolbox (MATLAB) with the "shortest-path" option. • When the data obtained are raw data (e. g. xy Bruker format in the present study), i. e. intensities measured on a fine and regular grid, the following algorithm is used: considering K series to analyze, the approximate positions of the significant peaks are first roughly determined by building a single series, consisting of the maximum intensities of the K series at all data points, and by thresholding this series.
| Chemicals, reagents, irradiation processes and analysis The ability of SPIX to extract relevant information from sets of complex high-resolution mass spectra is illustrated in two experiments. The first concerned peroxide-photocatalyzed degradation of maprotiline (an antidepressant drug) in a wastewater treatment pilot plant. In this case, the comparison aimed at revealing reagents, intermediates and products using kinetic models, from mass spectra recorded at different irradiation times. The second experiment concerned UV irradiation of acetamiprid (a neonicotinoid insecticide) in a complex mixture of aqueous fulvic acid to simulate river water; it aimed at revealing acetamiprid photoproducts at trace levels and evaluating the impact of UV treatment on dissolved organic matter. The comparison covers two datasets, for spectra recorded before and after irradiation.
| HRMS analysis The formulas were assigned using Bruker software based on accurate mass measurements (sub-ppm accuracy) and isotopic pattern-matching. All the extracted m/z values were related to maprotiline or its photoproducts (oxidized compounds); they corresponded to singly charged ions with  12  C and 13 C isotopic threshold was halved (6 × 10 7 ) and the same methodology was applied. Thus 197 peaks were selected by SPIX and the data were ordered according to statistical relevance. Then 23 peaks were selected on the criterion r 2 > 0. 9 (see Table  1 ). Here again, the m/z values were all related to maprotiline and its photoproducts; a second artifact (m/z 93. 06499) was found and attributed to the harmonics of m/z 279. 19386. Using the selection parameters referred to above, one protonated species was detected at m/z 336.
14927. Considering the "seven golden rules" of Kind and Fiehn and selecting atoms C, H, N, O, P, S, F, Cl, Br, Si, Na and K, the only matching formula was C 23 H 18 N 3 +. 28 is species is logically assumed not to be related to maprotiline; it could correspond to a contaminant in high concentration in wastewater, degrading under UV radiation. According to the kinetics revealed by SPIX, some photoproducts were present in detectable abundance after 2. 5 min irradiation. To estimate the relevance of the results provided by SPIX, one of the spectra recorded at this reaction time was selected. After blank subtraction (the blank consisting of wastewater matrix without maprotiline), the spectrum was exported in Ácsv format (Bruker's FTICR-MS file format). The spectra were recorded using 8 Mpt, and as the experiments were carried out using secondary treated wastewater, 4479 peaks were exported by the Bruker software which were above the signal-to-noise ratio threshold 294.
18552 and m/z 302. 17530 (protonated molecules); the fitting is presented in Figure  3  for the former. One of the maprotiline-related peaks that was not in the list of the highest intensities was not found in the spectrum recorded at 2. 5 min of irradiation: it was removed by the blank subtraction process, since the wastewater matrix was very complex. It is thus noteworthy that SPIX does not require blank subtraction to provide valuable information, allowing relevant peaks that coincidentally overlap with some of the matrix peaks not to be T A B L E 1 Ions extracted and associated kinetic models related to the photodegradation of maprotiline in wastewater with r 2 > 0. 9 (data ordered by decreasing intensity)  This example demonstrates the ability of the SPIX software to point out relevant changes between two conditions from changes in low-abundance ions within a complex matrix.
4% in the spectrum recorded before and 2. 4% after photolysis. These differences, not detectable looking at the whole spectrum, are obvious when zooming on the region from m/z 223. 00 to 223. 20 (Figure  4 ). Sets of spectra recorded before and after photolysis were compared using the SPIX software. Given the intensity of acetamiprid within the mixture, a peak detection limit was set at only three times the average intensity of spectral noise (average noise at 1 × 10 6 , detection threshold set at 3 × 10 6 ). This threshold was set as low as possible so as to identify acetamiprid degradation products in small amounts. Blank spectra were subtracted to eliminate any interference from solvents or instruments. Exported from SPIX, Table  2  lists the ions the abundance of which underwent significant change after irradiation.
Here, only ions with a probability of ≥95% (p≤ 0. 05) were retained, i. e. with significant difference in intensity between the two conditions. In this example, results are organized by increasing p value, but any parameter can be chosen for presentation of the results. A negative value in the "Difference" column indicates that the ion abundance increased after photolysis. Visual comparison (Figure  5 ) confirms the results displayed in ) shown here correspond to the protonated and cationized forms of a photoproduct previously described, the structure of which was elucidated in a study of the UV irradiation of acetamiprid in pure water. 23 This photoproduct was the major one. Thus, it is not possible to say whether others which were   F I G U R E 5 Visual result provided by the SPIX software after processing of mass spectra series recorded from samples taken before and after 30 min photolysis.
This is of great interest because it opens a way to investigate the global consequences of a depollution treatment, evaluating the treatmentapart from its ability to efficiently degrade pollutantsin terms of biotope preservation. 5 | CONCLUSIONS The SPIX software aims at extracting relevant data from mass spectra datasets. User-friendly and totally free, it is available for all at: http:// spix. webpopix. org. Two features of SPIX are presented in this article, based on examples taken from the field of environmental analysis. The first example showed how SPIX revealed photodegradation reactions by correlating significant changes in ion abundance over time with kinetic models. Thus, the software revealed the reagents, products and intermediate species in a very complex mixture (wastewater).
This functionality can be extended to monitor any kind of reactioneven unknownin all types of mixtures. Some features of the SPIX software are still under development. One aims at extending compatibility with many more file formats (wiff, Ád, Ápkl, Áqgd, etc. ), in order to be used with most marketed mass spectrometers. Another focus of developmentalready running but requiring some improvementsconcerns the extension of SPIX to three-dimensional datasets from hyphenated techniques such as GC/MS, LC/MS or IM-MS. Some commercial software applications allow comparison of chromatograms but, to our knowledge, an approach consisting of extracting relevant data from hyphenated techniques based on fitting with kinetic models has never been reported. The second example showed the ability of SPIX to detect photoproducts at trace amounts in an aqueous solution containing dissolved organic matter, through comparison of datasets for two conditions (before/ after photolysis in the present case).
ACKNOWLEDGEMENTS Financial support from the  National FT-ICR  network (FR 3624 CNRS) and from  Inria  (Institut national de recherche en informatique et en automatique) for conducting the research is gratefully acknowledged. This work is part of a project that has received funding from the  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie Grant  Agreement No. 765860  (AQUAlity).
Moreover, in both Pax6 -/-and O2e33 -/-mutants, neural progenitors with distinct identities, pMN and p3, producing MNs and V3 neurons, respectively, continue to be generated, but these different progenitor types intermix to a greater extent than normal. If the differential expression of cell adhesion molecules or different mechanical properties explained the sharpness of the boundary between pMN and p3 cells, this would lead to the sorting of pMN and p3 cells in the mutant embryos. Nevertheless, cell adhesion might play a role in the neural tube of teleosts  (Xiong et al. , 2013; Tsai et al. , 2020). Unlike the epithelial neural tube of amniotes, the zebrafish neural tube initially comprises unpolarised non-epithelial cells and sister cells disperse widely, including contralaterally, in the neural tube.
Pre-processing We performed a set of normalisation steps in order to compare embryos from different batches and across phenotypes. First, the datasets were realigned vertically with respect to the reference points, and the ventral-most point was set to (0,0) in axes coordinates. Second, cells with DAPI levels below two s. d. from the mean were removed to eliminate falsely identified nuclei. This value was decided individually for each sample to account for different background levels resulting from technical noise. Third, points that were very low in intensity (below two s. d. ) were set to a minimum threshold in each individual channel. Fourth, for Nkx2. 2 and Olig2, the intensity values were rescaled such that the minimum value was at 0 and the 40% quantile was at the arbitrary value of 0.
Defining boundary position and width Once the cell types had been classified, we assigned a quantitative measure of the width of gene expression boundaries. For this, we fitted to the cell position data, for each embryo, a smooth function indicating the probability of finding a cell of one type (the prevalent type on one side of the boundary) at each location of the image. We focused on the boundary between p3 and pMN domains. The classifier is then binary and gives the probability of finding a p3 cell at each image location. We used a Gaussian process approach to fit this classifier as detailed in  Rasmussen and Williams (2004) , using public MATLAB code (MATLAB version r2018b). The Gaussian process was chosen to have a constant mean function and a squared exponential co-variance function.
Fitting in vitro delay and resulting predictions We first identified parameter sets that could replicate the observed in vitro delay in the onset of Olig2 expression in the mutant, leading to a reduced parameter space (Fig. S12 ). In this step we do not set any constraints to the position or precision of boundaries between expression domains as this information cannot be extracted from the in vitro system. The delay in Olig2 activation was determined for networks positioned a fraction 0. 3 along the neural tube, and we retained those networks that took twice the amount of time to express Olig2 than in the WT. The same measurement was performed at other neural tube positions and resulted in similar distributions (data not shown). We next investigated what further phenotypical behaviour the retained parameter sets predict, focussing on the domain size and boundary precision generated in response to a graded Shh signal.
Geometrically, in the space of expression levels (phase space), the speed at which the steady state and saddle point separate as a function of neural tube position s is a plausible contributor to boundary sharpness because even if the fluctuations around the initial steady state favour a jump, such a jump will be inhibited by a large separation between steady state and transition point. High signal sensitivity should thus lead to rapidly increasing jump times and hence to sharp boundaries. To measure signal sensitivity we focussed on a fixed position (chosen as s = 0. 25) along the neural tube, beyond the saddle-node bifurcation, and calculated the Euclidean distance between steady state and transition point. We then used this as a simple quantitative indication of signal sensitivity. We checked the robustness of this measure by performing the measurement for different fixed positions along the neural tube, and also at variable locations chosen as the centre of the boundary region for each network; we found qualitatively similar results in every case (data not shown).
For interactions between nodes we took into account not only the parameters k ji but whether each parameter in conjunction with the actual states of the system would have a noticeable effect. We evaluated interactions by considering the contribution of an interaction given the highest level that the effector node can take. Accordingly, we consider an interaction with 0. 3 < |k ji | max(x i ) to be significant, otherwise we classify it as negligible. The maximum was taken over all steady states for all neural tube positions. The cutoff value of 0. 3 was chosen by systematic inspection of a representative number of networks, for which we compared the dynamics with and without individual interactions and assessed whether these were qualitatively identical or not. To assess the robustness of the cutoff value, we varied it within a range up to an order of magnitude larger and found that the results of our characterisation of network topologies remained qualitatively the same (data not shown).
2020b). That is despite management, especially when applied at an early invasion stage  (Leung et al. 2002; Ahmed et al. 2021) , being highly cost-effective in reducing longer-term management expenditure or damage to resources  (Aukema et al. 2011; Paini et al. 2016). Until recently, large-scale studies into the economic costs of invasive species have been limited to major geographic entities, such as the United States  (Pimentel et al. 2000 (Pimentel et al. , 2005)) , Europe  (Kettunen et al. 2009)  and Australia (Hoffman and Broadhurst 2016). Importantly, these studies have raised societal and policy-maker awareness of the massive economic costs of biological invasions, yet many nations lack assessment. In 2020, the United Kingdom (UK) was the 5 th largest economy in the world (World Economic Outlook Database 2020) and has experienced high levels of invasion success  (Roy et al.
As the rate of invasion across the UK increases over time, so too are the expected costs associated with these invasions  (Diagne et al. 2020a ). However, how economic costs relate to the length of time an alien species has been established remains unclear; species that invaded earlier might accrue greater costs or, on the contrary, these costs might diminish as species become naturalised. This needs to be assessed and temporal dynamics in total costs need to be characterised. Likewise, whether certain pathways of introduction are associated with higher costs than others at different times require consideration. To address these knowledge gaps, we use UK-specific data from 1976 to 2020 in the InvaCost database  (Diagne et al. 2020a) , a global compilation of the available literature on the economic costs of invasive species.
This database compiles detailed cost information suitable for large-scale syntheses of costs associated with invasive species at different spatial, taxonomic and temporal scales. Specifically, we ask: Question 1: What is the reported economic cost of invasive species in the UK and how is it distributed amongst taxonomic groups, habitat types and socioeconomic sectors? Given its economic importance, we expect costs to be higher from species impacting agriculturally-intensive terrestrial environments. Question 2: Are studies and recorded costs shared equally amongst all invasive species? We expect that most costs are caused by relatively few species and that these species are particularly well-known and studied, reflecting a positive feedback between documented costs and study effort. Question 3: How do costs of invasions vary over time and are species with early introductions costlier than more recent invaders considering their introduction pathways? We expect that costs per species will increase with residence time, given a longer time period over which to accrue costs and that common introduction pathways will be dominant (e.
Data collection and filtering To estimate the cost of invasive species on the UK economy, we used UK-relevant cost data from the latest available version of the InvaCost database (version 3. 0;  Diagne et al. 2020a ; https://doi. org/10. 6084/m9. figshare. 12668570) up to the year 2020. We note that InvaCost is a 'living' database that is subject to further additions and improvements. Following the InvaCost protocol  (Diagne et al 2020a) , all references were retrieved using standardised searches within selected repositories [Web of Science (https://webofknowledge. com/); Google Scholar (https://scholar. google. com/); Google search engine, (https://www. google. com/)] and targeted collection through gathering opportunistic literature and contacting experts and stakeholders. Collected materials were thoroughly assessed to identify relevance and extract cost information.
Specifically, titles, keywords, abstracts and full texts were checked hierarchically to ensure that (1) they were in English, as per the language competencies of the review team, (2) they contained at least one cost estimate and (3) each cost estimate was attributed exclusively to invasive species (see  Diagne et al. 2020a  for full details). InvaCost only includes invasive species for which there are documented economic impacts and our cost analysis reflects that scope. The database effectively defines invasive species as humanintroduced alien species that cause some economic cost. Duplicates that reported the same or overlapping costs were also removed from the data. We note that, for the most part, InvaCost includes species that are currently invasive in the UK. However, in some cases, costs pertaining to past successful eradications are included, such as for coypu Myocastor coypus.
Data processing The period of estimation across reported costs varied considerably, spanning periods of several months to several years. In order to obtain comparable costs, we considered all costs for a period of less than a year as annual costs and re-calculated costs covering several years on an annual basis (i. e. costs accumulated over multiple years were divided amongst those years, giving annual cost estimates). Therefore, costs that spanned multiple years were divided equally amongst those years (e. g. a cost totalling $10,000 over ten years would equal $1,000 per year). If there was no evidence for a cost occurring in more than one year (i. e. One-time cost), we conservatively counted it for one year only and likewise for costs that were Potentially-ongoing (Occurrence column in InvaCost).
In cases where the timespan for the costs was not described in the data publication, we used publication year as a surrogate for starting year and -if the cost was Potentiallyongoing -publication year as a surrogate for ending year. The conversion of all costs into an annual basis resulted in a total of 709 expanded entries (Suppl. material 1; 353 initial entries). This was accomplished using the expand-YearlyCosts function of the 'invacost' package version 0. 3-4  (Leroy et al. 2020 ) in R version 4. 0. 2 (R Core Team 2020); this function considers both the probable starting and ending years of each cost entry in the InvaCost database to annualise costs (see Suppl. material 2; https://doi. org/10. 6084/m9. figshare. 12668570). The first cost entry in our dataset was recorded in 1976, so all analyses were performed for the period 1976 to 2019, because that was the last year with robust reported costs.
We calculated full costs, which include potential and low reliability estimates, but excluded these more speculative estimates when examining the data in detail (as well as for the following subsections). The more detailed, conservative analysis, therefore, considered only the following descriptors: (iii) Country  (England, Scotland, Wales, Northern Ireland). Where costs were attributed to a particular country, we presented costs to that country; other costs were recorded as spanning multiple countries, i. e. Great Britain (i. e. excluding Northern Ireland) or the UK (i. e. including Northern Ireland); (iv) Environment of the invasive species (Terrestrial, Aquatic, Semi-aquatic or Diverse/ Unspecified): the habitat from which the species causing the cost originated. Here, we considered that Semi-aquatic corresponds to species that are closely associated with water for development, reproduction and/or foraging (e.
Question 2: Taxonomic biases in invasion costs To identify the proportions of invasive species in the UK for which cost data are available, the list of individual species in InvaCost was compared with comprehensive lists of invasive species in the UK. Lists of known invasive species were extracted and compiled for the UK from the following databases: (1) InvaCost version 3. 0; (2) the Global Invasive Species Database (GISD); (3) the sTwist database; and (4) the Great Britain Non-native Species Information Portal (GB-NNSIP) (Table  1 ). Only species listed within the UK were extracted from each database, with listed species checked to confirm their alien status and refined accordingly. We classify all of these species as "invasive", but note that the definitions of invasiveness differ slightly amongst these datasets (Table  1 ).