dataesr/scientific-paragraphs-categorization huggingface dataset:
31% of spectra acquired at the various stages of growth were correctly identified as T. indotineae with the MSI-2 database as published by Jabet et al. [23] , using the improved database, the percentage of correct identifications of T. indotineae spectra was 96. 02% overall (91. 25% for extractions performed before day 5 of growth, 98. 03% for extractions performed between day 6 and day 10, and 97. 37% for extractions performed after day 11), while distinction between T. interdigitale and T. mentagrophytes remains imperfect with 81% of T. interdigitale isolates and 68% of T. mentagrophytes, respectively, identified as such.
Detection of Specific Peaks We highlighted the importance of two sets of peaks that allow the distinction between T. indotineae and the tested genotypes of the T. mentagrophytes species complex. Boxplots showing the distribution of intensities for these more discriminant peaks, allowing for T. indotineae identification among the T. mentagrophytes complex, are presented in Figure  3. Using the peak list obtained with Flex Analysis software, we calculated the percentage of spectra from Panel 1 for each genotype that contained these four peaks. The results are found in Table  3. The presence/absence of the peaks at 6834/6845 daltons determined between 7 and 10 days of growth seem to be the best discriminating peaks for the distinction between T. indotineae and T. interdigitale/mentagrophytes, as 100% of the tested T.
This study shows that differentiating T. indotineae from T. interdigitale or T. mentagrophytes is possible using MALDI-TOF MS tools by using an appropriate spectral database and taking into account the evolution of spectra according to the stage of the fungal colonies. Even though T. indotineae is known to more often be resistant to treatment with terbinafine than other Trichophyton species, not all T. indotineae isolates are resistant to treatment. In the literature, resistance to terbinafine was observed for 16  [32]  to 76%  [8]  of the isolates studied. In our study, we did not try to achieve resistance identification through MALDI-TOF mass spectrometry. This resistance is linked to several mutations in the squalene epoxidase (SQLE) gene  [33] [34] [35] , and the SQLE protein is a relatively large one (55 kDa for Saccharomyces cerevisiae; https://www.
uniprot. org/uniprotkb/P32476/entry, accessed on 28 September 2022) that cannot be detected using the universal MALDI-TOF identification protocol for microorganisms  (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20). Detecting the SQLE protein and its mutations through MALDI-TOF MS requires different Microflex parameters and a different methodology than the one applied in microbiology laboratories. This is why we decided here to prove that we could identify this new species using MALDI-TOF MS and an appropriate MS database as a first identification step in the process of treating a patient with tinea corporis or tinea cruris. The MSI-2 database published by Jabet et al. in 2021  [23]  was not initially tested with T. indotineae isolates, but was reported to yield poor performance regarding the identification of T.
indotineae. As in the article by Jabet et al. , we decided to work with SDA-CG and IDFP media to both improve the MSI-2 database and test the identification process. SDA-CG is the culture medium that is most often used in microbiology laboratories to grow filamentous fungi and dermatophytes. It is sometimes supplemented with cycloheximide to favor the growth of dermatophytes, compared to fast growing fungi such as Aspergillus spp, but to the best of our knowledge, cycloheximide does not interfere with the peaks present in the mass spectrum of a dermatophyte  [27]. Recently, the IDFP culture medium has been introduced in the mycology landscape as a medium of choice to use as a sub-culture medium when identifying poorly sporulating fungi that are difficult to sample without collecting culture medium.
Indeed, this medium presents a porous membrane on its surface on which the fungus can grow, allowing for easy sampling without danger of collecting agar, whatever the sampling tool chosen. Its use in mycology laboratories is rapidly increasing, especially for dermatophytes, as a spectrum can be obtained as soon as 48 h after sub-culturing. Other culture media such as Potatoes Dextrose Agar or Malt agar can be used in the search for dermatophytoses agents, but the use of those culture media remain occasional, and we focused on the most commonly used media to improve the reference database. In fine, sensitivities for T. indotineae (95. 6%), T. interdigitale (81. 2%), and T. mentagrophytes (67. 9%) are high with the improved MSI-2 database. Improvement of the MSI-2 database also led to a high specificity for the three species, with only eight spectra corresponding to two strains of T.
Conclusions In conclusion, the high sensitivity and specificity obtained for the identification of T. indotineae using the online MSI-2 application will allow MALDI-TOF users to rapidly identify T. indotineae from a positive culture of dermatophytes with this database. This will also improve diagnosis and help to monitor the spread of this species around the world, as laboratories proceeding to their identification through MSI-2 use the same database and are connected through the network of users. By more easily identifying T. indotineae isolates, biologists will more successfully target isolates that are potentially resistant to terbinafine and will trigger the susceptibility testing of those isolates more efficiently through the results obtained by MALDI-TOF MS.
Retrievals Vertical profiles of the volume mixing ratio (VMR) of target gases are made using the JPL Gas Fitting Software  (GFIT or GGG; Irion et al. , 2002; Wunch et al. , 2011). At each tangent height (defined relative to the aeroid) and over a specified spectral fitting window, a forward model is computed using volume absorption coefficients derived from the 2020 edition of the HITRAN database  (Gordon et al. , 2021) , a priori VMR profiles for various gases, vertical profiles of temperature and pressure, and computed optical path through the atmosphere. Where available, line shape parameters for broadening in a CO2-rich atmosphere are used  (Devi et al. , 2017; Gamache et al. , 2016; Tudorie et al. , 2012; Wilzewski et al. , 2016). Spectral fitting is performed using non-linear Levenberg-Marquardt minimisation in which target gas VMR profiles are varied to obtain the best-fit.
This approach was previously used to study CO, O3 and HCl with ACS MIR  (Korablev et al. , 2021; Olsen et al. , 2020; Olsen, Lefèvre, et al. , 2021). Several studies have been performed using ACS MIR data where a single spectrum close to the edge of the observational slit nearest the centre of the Sun was analysed at each tangent height (e. g. Alday et al. , 2019; Korablev et al. , 2019; Olsen et al. , 2020; Trokhimovskiy et al. , 2020) , which is very computationally efficient. To improve the sensitivity of ACS MIR when absorption feature depths approach the noise level of a spectrum, such as when studying gases present in only trace amounts, or at higher altitudes, more data from the detector frame can be used. This method was developed to improve the retrievals of the trace gas hydrogen chloride (HCl)  (Olsen, Trokhimovskiy, Montabone, et al.
1-0. 2 km), line depths, noise level, and ILS. Examples of 11 spectra extracted from one frame are shown in Figure  1. Sets of occultation spectra are made by grouping the spectra by row (a set of all the top-most spectra, a set of the second row, etc. ), since these should have similar noise levels and ILSs, and VMR vertical profile retrievals are performed on each set. The weighted average of the profiles is taken to be the best estimate of the vertical profile of the target gas VMR, and it is assigned the standard deviation of the weighted mean as an uncertainty. This method has been shown to significantly improve the accuracy of a retrieval  (Olsen, Trokhimovskiy, Montabone, et al. , 2021) , since the impact of any data quality issues for a single spectrum are reduced (spurious features, inaccuracies when measuring quantities such as the solar spectrum, corrupted data, interpolation errors, etc.
Pressure levels are used rather than tangent heights to mitigate any errors between the estimated heights of ACS MIR and ACS NIR since tangent height is used to define the pressure levels. The comparison is done using number densities rather than VMRs since differences in retrieved VMR may result from differences in temperature and pressure (total number density), rather than measured absorption line depths. Number densities facilitate comparison with  Lefèvre et al. (2021) , and since they are monotonic functions of altitude the axes of Figure  10  reveal higher and lower heights. The three panels in Figure  10  show a comparison between the GCM results and water vapour retrieved with ACS MIR data, ozone VMR retrieved with ACS MIR data, and temperature retrieved using ACS NIR data  (Fedorova et al.
However, the LMD GCM predicts larger VMRs above 30 km (> 5 ppmv compared to < 2 ppmv with ACS MIR) and a more prominent surface layer of water vapour, causing the VMR to increase again below 10 km. Due to the relationship between number densities and altitude, large differences in number densities for vertical profiles with similar shapes, as shown in Figures  8  and 9 , imply a height mismatch between profile features in the model and observations. Such differences in altitudes where water vapour and ozone are abundant are visible in Figures  8  and 9. Figure  10b  shows the number density of ozone retrieved from ACS MIR data and compared to the GCM. ACS MIR generally observes higher ozone concentrations than predicted by the GCM. This tends to occur in each latitude and Ls grouping (but notably so in the equinoctial periods, related to the difference in water vapour described above).
In the GCM, the ozone abundance peaks at 25 ppbv, but at a higher altitude, 35 km. At lower altitudes, the difference grows, and there is only ~10 ppbv in the model compared to >50 ppbv in the observations. This is the period in which we may be irregularly and indirectly observing the polar vortex, which will not be reflected in the model. Previous studies have found similar disagreement between models and observations, with a factor of 2 times identified in  Olsen et al. (2020)  and  Lefèvre et al. (2021) , and up to 10 times in  Patel et al. (2021). These results depend on the amount of water vapour present in the model, which was well constrained when examining SPICAM data  (Lefèvre et al. , 2021). A more important characteristic to compare with the model than ozone abundance, is the anti-correlation with water vapour, which it is now possible to study at discrete altitude levels for the first time with ACS MIR (section 6).
Note that while the LMD GCM was run using MY 35 and MY 36 climate data in the form of solar UV flux and dust opacities, it does not incorporate measured temperatures and pressures, which will further constrain ozone photochemistry. Work is ongoing on assimilating ACS, NOMAD and Mars Climate Sounder Data into the LMD and GEM-Mars GCMs which will allow the reassessment of odd-hydrogen production and ozone reaction rates.
The nadir SPICAM observations used to derive column abundances are most sensitive to the lowest layers of the atmosphere and the vertical profiles produced by ACS MIR are able to provide more information about the behaviour of O3 and H2O at all altitudes. The accuracy, vertical resolution, and sensitivity to temperature, and multiple trace gases of ACS provide a unique opportunity to probe the relationship between different chemical species. The anti-correlation of ozone and water vapour was noted qualitatively in early observations and repeatedly confirmed (e. g. ,  Clancy, Wolff, et al. , 1996; Lefèvre & Krasnopolsky, 2017; Perrier et al. , 2006 and references therein)  and quantified using SPICAM nadir data  (Lefèvre et al. , 2021). Figure  11  shows the anti-correlation between retrieved water vapour VMRs and retrieved ozone VMRs for the coincident measurements made with ACS MIR at each altitude where ozone was observed.
The anti-correlation is not always verified. There is a group of data in the lower middle of Figure  12a  that lies just below the main trend, with <50 ppbv O3 and ~1 ppmv H2O. These were collected in the south during aphelion, at the edge of the dry winter polar vortex. There, observations that were made near 20 km exhibit strong gradients in both the O3 and H2O abundances, where ozone is declining and water vapour rapidly increases as altitude grows (Figure  4 ). Ozone in this period is only observed at low altitudes, around 10 km, and the water vapour is observed to fall rapidly between 20 and 10 km. Another unique subset of observations is that of the equatorial aphelion ozone layer. As water vapour abundance falls and the ozone VMR increases, an apparent minimum water vapour VMR is reached (around 1 ppmv), while the ozone concentration continues to increase above 200 ppbv.
Figures  11c  and f  show the same thing, but using the vertical profiles of H2O and O3 VMR extracted from the LMD GCM solar occultation run at the latitude and Ls of the ACS MIR occultations. The anti-correlation between water vapour and ozone reveals a similar inverse trend, but with less ozone being present (and thus a lower slope in panel c), by a factor of >2 when considering the maximum values along the trend being ~100 ppbv for the GCM and ~200 ppbv for ACS MIR measurements. A difference of this magnitude was also found between SPICAM and the LMD GCM with only gas-phase chemistry  (Lefèvre et al. , 2021) , and is consistent with the differences between model and observation shown in Figure  10. Of note is that there remains a cluster of data below the main trend. These come from the southern aphelion, with VMRs of H2O ~1 ppmv and O3 < 100 ppbv, and are related to altitudes above 30 km where ongoing changes in the VMRs of H2O and O3 are seen, and water vapour is increasing.
In the GCM (Figure  9 ), descending from 50 km, the temperatures fall from ~180 K to ~130 K, while the H2O VMRs tend to be fairly constant. From around 30 km towards the surface (Figure  9 ), temperatures increase again, but the water vapour falls off very rapidly by 20 km due to the cold temperatures. Similar behaviour is observed in the retrievals, but to a lesser extent. A difference between the model data and the retrievals impacting this comparison is that the minimum atmospheric temperature occurs at a lower altitude in the ACS NIR data than in the model computations. The behaviour of water vapour at cold temperatures indicates differences in the model regarding condensation and saturation conditions than in our observations.
Conclusions We present here three years of data collected by ACS MIR on the ExoMars TGO, covering the second half of MY 34, all of MY 35, and the start of MY 36. ACS MIR operating in solar occultation mode is sensitive enough to measure vertical profiles of the VMR of O3 using the infrared 003←000 absorption band (3000-3060 cm -1 ). By comparing the vertical structure of simultaneously measured ozone, water vapour, and temperature, we have observed trends in how these important species vary seasonally and exchange hydrogen and oxygen between them. Photochemistry in the Martian atmosphere and the stability of CO and CO2 depend on oddoxygen and odd-hydrogen chemistry, and photolysis of H2O is the primary source of members of the odd-hydrogen family, while O3 is an important tracer of odd-oxygen.
Acknowledgments The authors declare no conflicts of interest. The ACS investigation was developed by the  Space Research Institute (IKI) in Moscow , and the  Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS/CNRS ) in Paris. The investigation was funded by  Roscosmos , the  National Centre for Space Studies of France (CNES)  and the  Ministry of Science and Education of Russia. The GGG software suite is maintained at JPL (tccon-wiki. caltech. edu). This work was funded by the  UK Space Agency  ( ST/T002069/1 ), the  Natural Sciences and Engineering Research Council of Canada (NSERC)  ( PDF-516895-2018 ), and the  National Centre for Space Studies of France (CNES). IKI affiliates acknowledge funding from the  Russian Science Foundation  ( RSF-ANR 20-42-09035 ) (data analysis and interpretation).
Accepted Article This article is protected by copyright. All rights reserved. Forget, F. , Hourdin, F. , Fournier,  R. , Hourdin , C. , Talagrand, O. , Collins, M. , et al. (1999). Improved general circulation models of the Martian atmosphere from the surface to above 80 km. J. Geophys. Res. , 104,  24155-24176. https://doi. org/10. 1029/1999JE001025Gamache, R. R. , Farese, M. , & Renaud, C. L. (2016). A spectral line list for water isotopologues in the 1100-4100 cm -1$ region for application to CO2-rich planetary atmospheres. J. Mol. Spectrosc. , 326,[ 144][145][146][147][148][149][150 ] https://doi. org/10. 1016/j. jms. 2015. 09. 001Gordon, I. E. , Rothman, L. S. , Hargreaves,  R. J. , Hashemi , R. , Karlovets, E. V. , Skinner, F. M. , et al. (2021). The HITRAN2020 molecular spectroscopic database.
Open Data The VMR vertical profiles generated in this study are available on the Oxford Research Archive at dx. doi. org/10. 5287/bodleian:2adPoRMdz. The data sets generated by the ExoMars Trace Gas Orbiter instruments analyzed in this study are made available in the ESA Planetary Science Archive (PSA) repository, https://archives. esac. esa. int/psa/#!Table%20View/ACS=instrument, following a six months prior access period, and the ESA Rules on Information, Data and Intellectual Property. Temperature and pressure data used here are from ACS NIR and were generated in other studies. Data from MY 34 are made available in Fedorova et al. (2020)  and an updated data version containing MYs 34-36 are published in  Fedorova et al. (2022). Data generated with the LMD GCM for TGO solar occultations, along with its user guide, are hosted.
This disintegration of the co-occurrence networks can be the first sign of disappearing interaction links which, in turn, may jeopardise ecosystem function and can lead to regime shifts. In this work, we used a unique long-term dataset collected across the islands of the Azorean archipelago with standardised methodology. We built on the deep knowledge gathered over two decades on the ecology of species, as well as on the ongoing processes shaping the islands' arthropod fauna, yet took a novel methodological approach and disentangled hidden ecological processes of great ecological and conservation concern.
2010 ) and a high number of exotic species introduced  (Borges et al. 2010). Taxonomic, functional and phylogenetic diversity patterns and community structures of Azorean arthropods have been widely studied  (Borges et al. 2005 , 2016 , Rigal et al. 2018) , but little attention focused on ecological networks of interspecific associations  (Rego et al. 2019, Valido and Olesen 2022 ). Yet, the availability of this unique dataset on the arthropods of the Azores provides an opportunity to map detailed co-occurrence networks and compare them among islands and relate them to biotic and abiotic environmental factors. We hypothesize that although species pools among the Azorean islands are highly similar, due to the presence of single island endemics the co-occurrence networks differ between islands (H1).
Network estimation and statistical analysis A species-site matrix was used as a baseline dataset to generate a large meta-network, containing all species abundance data collected from all islands and sampling sites with the two sampling periods separated. When the ecological functions of adults and juvenile stages were substantially different, adults and juveniles of the same species were recorded separately (e. g. Lepidoptera). Based on  Borges et al (2016) , each species was categorised as either endemic, non-endemic but native (termed as native henceforth), or exotic species, and higher taxonomic levels, such as family, order, and class, were also assigned to them. Natives and endemics were sometimes merged and referred together as 'indigenous' species. Species whose nativity status was unknown remained included in the overall species numbers but they were not categorised into either indigenous or exotics and thus they did not inflate the number of any of those groups.
Species with less than ten individuals in overall abundance and those occurring at less than three sampling sites were removed and excluded from the further analysis. Due to methodological constrains, abundances were converted to binary (i. e. presence-absence) data and were used in the co-occurrence analysis. Two co-occurrence networks, one for the years of BALA I and one for those of BALA II, were generated using the cooccur package  (Griffith et al. 2016 ) in an R programming environment (R Core Team 2012). The union of the two networks (i. e. merging all nodes and edges from both networks), a meta-network, consisting of species (represented as nodes) and their predicted associations (represented as links or edges) served as a base of our further analysis. The association detecting method provided by the R package uses a probabilistic model based on hypergeometric distribution to assess if species co-occur more or less frequently than expected by mathematical chance.
Commonly used measures to characterise network topological properties, such as the number of nodes and edges, connectance, proportion of negative links, the proportion of isolated nodes, mean closeness and betweenness centralities, and modularity (based on the 'fast and greedy' community detection algorithm) were calculated. Similarly, node characteristics, such as the number of edges connecting other nodes (degree), the proportion of degree to the number of all nodes (relative degree); the number of negative edges (vulnerability) and the proportion of those to the relative degree (relative vulnerability); betweenness, closeness centralities were also computed, with the help of the igraph package  (Csárdi and Nepusz 2006). Since most of these measures strongly depend on the number of nodes (i.
In this latter case though the ratio of the negative links to the number of links within each combination pair was calculated and compared to the randomised distribution, using onesample t-tests. To investigate if there are differences in the frequency of endemic, native, and introduced species having association links with each other we compared the number of links between each combination using Kruskal-Wallis tests, and pairwise Wilcox-tests with p-values adjusted according to the FDR method. The relationship between major network topology measures and the number and the proportion of exotics in the communities was investigated using linear mixed models with the island identity set as the random term using the lmer() function in the lme4 package  (Bates et al. 2015 p. 4). All proportion variables were square-root transformed prior to regression to approximate normality.
P-values were estimated according to Satterthwaite's method, as implemented in the lmerTest package  (Kuznetsova et al. 2017 ) in R. Marginal and the conditional R2 values were extracted using the r. squaredGLMM() function in the MuMIn package  (Barton 2020). Since indigenous and exotic species richness strongly correlated, to better disentangle the effects of exotics, similar models as above were fitted on networks consisting of native species only and residuals from these models were used to re-fit models between node properties and the number and proportion of exotics. Both network-related and node-related properties were compared between native and exotic species using Kruskal-Wallis tests and pairwise Wilcox tests with p-values adjusted for multiple testing according to the FDR method.