dataesr/scientific-paragraphs-categorization huggingface dataset:
2015; Yates et al. 2014). All measurements were conducted on point-mounted specimens under stereo microscopy. Because body size in diverse species can drive the overwhelming majority of variation in a dataset and potentially mask other important contributors, we created two datasets: one comprising raw measurements and one comprising only sizecorrected ratios. A list of all traits measured with associated definitions may be found in Supplementary Tables  S1  and S2. All specimens were assigned a binning from each of three ecological niche aspect categories: functional role (six binnings), nesting niche (five binnings), and foraging niche (four binnings). Specimen binnings were assigned based on literature surveys. When any particular aspect of a species' ecological niche was uncertain, the species was assigned an "unknown" binning and excluded from further model development.
Model development We implemented Random Forest analysis, a supervised machine learning algorithm, to delimit species into ecological niches by morphology using our extant ant morphometric dataset. Random Forest models are classification algorithms that partition morphospace according to predefined ecological niche binnings through an assemblage of decision trees  (Breiman 2001). The algorithm builds a series of decision trees which each provide a "vote" on a given specimen's predicted category; the vote consensus from the decision trees is then used as the final predicted category for the specimen. At each split in each decision tree, a predetermined number of variables are randomly selected and tested to evaluate their contribution to the accuracy of the model. During each iteration of the model, one-third of the data is randomly removed and used to estimate the error rate of that particular iteration; the converged test error rate across the iterations is considered the out-of-bag error rate for the model.
This removal of testing data during bootstrapping eliminates the need for a priori separation of a testing and training dataset, incorporating all collected data. While other supervised machine learning or dimension reduction techniques have been used in morphology-based paleoecological prediction, Random Forest has recently been shown to outperform linear predictive approaches with respect to morphology  (Pigot et al. 2020; Sosiak & Barden 2021). This is the first application of Random Forest in predicting extinct ecology to our knowledge. Random Forest models were trained on our morphometric data, using both raw measurement datasets and size-corrected ratio measurement datasets separately for each ecological niche aspect. We constructed Random Forest models for functional role, nesting niche, foraging niche, and ecomorph syndrome separately, allowing for both granular classification of ecological niche and more synthesized classification.
Model parameters were selected based on initial sensitivity tests: mtry=4 (number of variables tested at each split) and ntree=5000 (number of trees iterated). Out-of-bag error rate estimates ranged from 15-22%, reflecting accuracies of 78-85%; OOB error rates for each model are shown in Table  S5. All Random Forest analyses were implemented in the R package "randomForest"  (Liaw and Weiner 2018). Random Forest models were then trained on subsets of the original dataset. Homologous traits were measured on fossil ant specimens; when available, traits were measured from CT reconstructions, and otherwise were measured under light microscopy. Finally, the pre-trained Random Forest models were used to predict extinct ecology from fossil morphometric datasets.
Fossil data We measured fossil hell ant specimens using a combination of stereo microscopy and reconstructions of X-ray micro-computed tomography (micro-CT) scans. Twenty specimens from 16 species and morphospecies were measured under stereo microscopy. We submerged the amber specimens in water to reduce light distortion; some measurements were not possible due to specimen positioning. Three specimens were micro-CT scanned and reconstructed for subsequent measurements; two species of hell ant and one species of Pseudomyrmex macrops from Dominican amber to assess the reliability of CT-scan based data. Congeners of the Dominican Pseudomyrmex fossil are extant today and their ecology is well consistent across the genus and well-characterized. Two specimens, Haidomyrmex scimitarus (specimen AMNH Bu-FB80) and Linguamyrmex vladi (specimen AMNH BuPH-1) were scanned at the American Museum of Natural History Microscopy and Imaging Facility using a GE phoenix v|tome|x s240 60kV CT-scanner.
Specimen AMNH Bu-FB80 was imaged at 180 µA for 5 second exposures and a voxel size of ~8µm; specimen AMNH BuPH-1 at 250 µA for 1 second exposures and a voxel size of ~3µm. The Pseudomyrmex macrops specimen (AMNH DR-14-1021) was imaged at the New Jersey Institute of Technology York Center using a Bruker SkyScan 1275 at 60kV and 150µA for 1 second exposures with a subsequent voxel size of ~3. 5µm. Volume reconstruction of the x-ray images was conducted in 3D Slicer v4. 11  (Fedorov et al. 2012 ) using the Segmentation modules; still images of the reconstructed specimens were subsequently imported into ImageJ  (Abràmoff et al. 2004 ) for linear measurements to retain consistency with measurements taken under stereo microscopy. Most hell ant traits were distinguished as homologous to extant species; however, because haidomyrmecine cranial morphology is highly modified, eye position is difficult to assess in the context of modern ant variation.
Functionally, however, the hell ant would have been moving with its head held hypognathously, as evidenced by fossilized posture as well as preserved predation  (Perrichot et al 2008; Barden & Grimaldi 2012; Perrichot et al. 2016; Barden et al. 2020)  thus the functional anteroposterior axis of the hell ant head would be from its clypeal protrusion or horn to the occipital foramen, rather than the homologous anteroposterior axis of the head running perpendicular to the ground from vertex to oral opening. Considering that our question is a question of functional ecology -what is the ecological occupation of hell ants? -we measured traits related to eye position both functionally and homologously, to incorporate this possible variation. Due to limitations measuring specimens directly from amber fossils, we produced two fossil morphometric datasets: one 'incomplete dataset' which excluded a subset of traits for all specimens; and one with all measurements included.
The incomplete dataset lacked the frontal head length, head width, frontal mandible length, and pronotal width measurements. These measurements are often difficult to accurately capture because amber fossils are typically prepared to expose a clear lateral profile of any inclusions, leaving the dorsal and frontal margins of the amber curved and distorted. Twenty hell ant specimens were included in this incomplete dataset. The complete dataset comprised the proof-of-concept fossil Pseudomyrmex macrops specimen and three hell ant specimens: Dhagnathos autokrator, Haidomyrmex scimitarus, and Linguamyrmex vladi. While the majority of specimens were workers, two of the specimens -the complete Dhagnathos autokrator and Haidomyrmex scimitarus were represented by alate and dealate queens, respectively.
We include these here for two reasons: one, hell ant queens are hypothesized to have actively foraged and hunted in early colony foundation, and so likely occupied a similar ecological niche to the workers of the species; and two, worker specimens are limited in many hell ant species and entirely absent in the genus Dhagnathos. While we have no comparison to Dhagnathos workers, we include a Haidomyrmex scimitarus worker in the incomplete morphometric dataset, allowing us to compare the accuracy of the model in predicting alate and worker ecology. A full list of all specimens included with information pertaining to their sampling methods and castes can be found in Table  S6 ; all morphometric data for fossil specimens may be found in Supplementary Data_Fossil Morphometrics. To ensure that morphological diversity for traits measured from fossil species are within the bounds of extant morphological diversity, we conducted principal component analysis (PCA) to compare morphospace occupation of hell ants relative to extant lineages.
Random Forest model implementation We implemented different sets of Random Forest models, given the missing traits in our incomplete fossil dataset; once with the missing traits eliminated from the extant ant morphometric training dataset, and once including all traits for the complete fossil dataset. For each dataset, we implemented two models using the raw measurements and the size-corrected ratio measurements. Using these two models, we predicted ecological niche aspects of hell ants twice; once using functional morphology, and once using homologous morphology. Thus, each specimen's ecological niche was predicted four times: using raw measurements with functional morphology, raw measurements with homologous morphology, size-corrected ratio measurements with functional morphology, and size-corrected ratio measurements with homologous morphology.
Our myrmicine sampling therefore included Strumigenys as well as the five "dacetine" trap-jaw genera: Acanthognathus, Daceton, Epopostruma, Microdaceton, and Orectognathus. Although it is not yet clear whether all dacetine trap jaws are the product of a single origin, we grouped these taxa in analyses as they are more closely related to each other than any are to Strumigenys  (Ward et al. 2015). To estimate the total number of unique ecomorphological combinations for each specialized predatory lineage, we gathered niche occupation and size data for a total of 982 species, including 15 hell ant species with ecologies estimated under random forest. Our extant sampling represents a minimum of 50% species sampling for each genus. Each species was assigned one of three foraging and three nesting niche aspects according to our modeling results for haidomyrmecines or published natural history observations for extant taxa (Supplementary Data_Trap Jaw Ecomorphospace).
Because observational data do not exist for all species, we applied published generalizations in some cases (e. g. Strumigenys are noted as almost always leaf litter nesting and foraging, we assumed this occupation by default except when otherwise noted in the literature). To estimate body size, we gathered minimum and maximum Weber's length (a measurement of mesosoma length and traditional metric of ant size) measurements from taxonomic descriptions and revisions. To include taxa without published morphometric data, we collected Weber's length measurements from publicly available images on AntWeb (AntWeb 2021) using ImageJ. We discretized species sizes by delimiting Weber's length ranges for each species into at least one of twelve equal size binnings. Size binning ranges were defined as one half of the standard deviation of Weber's length measurements across all species.
For example, the ecological disparity between unique occupation 1 (uo1) and unique occupation 2 (uo2) would be: | Nesting Nicheuo1 -Nesting Nicheuo2 | + | Foraging Nicheuo1 -Foraging Nicheuo2 | + | Body Sizeuo1 -Body Sizeuo2 |. We summarized mean and standard deviations for each lineage using ggplot. Visual representations of lineage specific ecomorphological occupations (ecospaces) were generated using the R package "rgl"  (Adler et al. 2021)  and redrawn in Adobe Illustrator.
There were rarely scenarios where more than two niche aspect binnings were predicted for a single specimen. More frequently in situations of evenly split predictions, the size-corrected ratio models would predict the same aspect binning while the raw measurement models would predict another; this suggests that the difference between functional morphology and homologous morphology with respect to eye positioning did not matter as much to niche prediction. This may be because the model ranked eye positioning traits relatively low on its scale of variable importance, thus, the difference between functional and homologous morphology was negligible (Supplemental Figure  S4, S5 ). There was a limited drop in accuracy from the models trained on the full morphometric dataset to the models trained on the limited morphometrics dataset, with missing traits removed (Table  S5 ).
Hell ants as epigaeic or leaf litter predators We recover broad consensus across models for hell ant ecological niche occupations: models consistently predicted haidomyrmecine taxa as leaf litter foraging or epigaeic ground-nesting predators, with few outliers (Figure  4 ). Variations within the testing datasets, such as homologous vs functional morphology or missing traits, did not greatly affect predictions. Our results are in contrast to previous hypotheses suggesting a primarily arboreal lifestyle among hell ants. Initial hypotheses were based on qualitative assessments of morphology  (Barden & Grimaldi 2012 ) and an assertion that hell ants' vertically aligned mandibles might have precluded the fine manipulation of soil required to create ground nests  (Dlussky 1996). However, extant and fossilized behavioral evidence provide support for ground and leaf litter leaf litter habits among in haidomyrmecines.
Acknowledgements We thank  Christine Johnson  and  Christine Lebeau  for facilitating access to specimens at the  American Museum of Natural History , in addition to  Morgan Hill  and  Andrew Smith  for facilitating access to imaging equipment at the AMNH;  Stefan Cover  and  David Lubertazzi  for facilitating specimen access and hospitality at the  Museum of Comparative Zoology ;  Eugenia Okonski  and  Ted Schultz  for facilitating specimen access and hospitality at the  Smithsonian National Museum of Natural History ; all of whom without which we would not have been able to develop the initial model.
Acknowledgments This work is supported by  Campus France/DAAD , project  PROCOPE   46704WF , by  Investissements d'avenir, Labex ENIGMASS , contrat  ANR-11-LABX-0012 , and by  Chile ANID FONDECYT  project  1200641. We are grateful to the  Centre de Physique Théorique Grenoble Alpes (CPTGA)  for the hospitality extended to  M. Maniatis  during his stay in Grenoble in fall 2021.
23  While some metals, such as silver, have long been known to possess antimicrobial properties, there have been few systematic studies of anti-infective metal complexes until recently. [24] [25] [26] [27]  In the last few years, several reports have described promising antibacterial properties of metal complexes,  [28] [29] [30] [31]  including our systematic study on the antimicrobial properties of ∼1000 metal-containing compounds contained within a screening collection of >300,000 molecules. 32  These data were collected via the crowd-sourced Community for Open Antimicrobial Drug Discovery initiative (CO-ADD, co-add. org) funded by The Wellcome Trust and the University of Queensland. CO-ADD provides free antimicrobial screening to chemists around the world. 33  Our analysis found that metalcontaining complexes displayed significantly superior hit rates (9.
71  Of note, compound Pt5 is identical to Pd3 in all aspects except the platinum metal center. Indeed, there are a total of nine complexes in the CO-ADD database with very similar structures, as well as the free ligand. The latter showed no activity at all in the CO-ADD primary screening and was not evaluated further. The biological evaluation of these analogue compounds has been reported separately. 72, 73  The fact that both Pd3 and Pt5 have similar activity profiles against the fungal panel but the free ligand alone shows no activity suggests that a metal is essential to obtain the observed antifungal activity. The cytotoxic and hemolytic properties of the two compounds are slightly different, with Pd3 displaying low cytotoxicity and significant hemolysis. On the other hand, no hemolysis up to 200 μM could be detected for Pt5, and the cytotoxicity was similar to Pd3, resulting in a promising therapeutic index of >2500 (Table  S7  in SI).
The larvae were then monitored for 6 days for survival and health using the G. mellonella Health Index Scoring System. 111  Of the 11 tested compounds, only Au2 showed signs of toxicity at the highest tested concentration (1 mM). For the other compounds, no detrimental effects could be observed in the larvae over the monitoring period. These data contribute to our understanding that metal complexes need not be considered as generally toxic. Nevertheless, factors such as the metabolism, excretion, and/or accumulation of the various metal complexes and their metabolites will still need to be studied more rigorously in rodent models if they are to be advanced into the preclinical pipeline. Of note, the maximum concentration reached in the larvae in these assays is anticipated to be significantly lower than the concentrations measured in the in vitro toxicity/ hemolysis assays.
Briefly, G. mellonella larvae were inoculated with C. albicans (ATCC 90028) and incubated for 2 h at 37 °C. The larvae were then injected with 10 μL of the corresponding compound solution (1 mM) or control (same concentration and injection volume) and incubated for 24 h at 37 °C. The larvae were then anesthetized, macerated, and serially diluted onto SDA (Sabouraud dextrose agar) plates. After 48 h of incubation, the CFUs were counted. Pt2-treated larvae showed CFUs comparable to the DMSO control, indicating that no significant biological effect was achieved by this compound at the tested concentration (Figure  5 ). On the other hand, a significant reduction in CFUs was detected for the control compound fluconazole (1. 1 log10) and for compound Pt1 (0. 7 log10). These data indicate that Pt1 is able to reduce the fungal burden in a living organism.
Recent literature reflects the substantial progress in combining spatial, temporal and spectral capacities for remote sensing applications. As a result, new issues are arising, such as the need for methodologies that can process simultaneously the different dimensions of satellite information. This paper presents PLS regression extended to three-way data in order to integrate multiwavelengths as variables measured at several dates (time-series) and locations with Sentinel-2 at a regional scale. Considering that the multi-collinearity problem is present in remote sensing time-series to estimate one response variable and that the dataset is multidimensional, a multiway partial least squares (N-PLS) regression approach may be relevant to relate image information to ground variables of interest.
PLS methods have been extensively used  [12]  as they are well-suited to deal with multi-variate data structures with high covariance and redundancy  [13] , such as are found in remote sensing datasets. However, challenges remain with respect to addressing these increased data volumes and increased data variability  [14]. Exploiting multispectral image time-series is a promising but still relatively underexplored research direction due to the complexity of jointly analysing spatial, spectral and temporal information  [15]. The increasing complexity of information leads to a natural generalization of the concept of the 'data set' from conventional tables to higher-dimensional arrays  [16]. As  Bro [17]  specified, when including time as an additional dimension, the dataset becomes three-way, which cannot be handled by commonly used conventional prediction models.
Among the alternatives proposed in the literature for modelling multi-way data, deep learning has the potential to outperform traditional classification and regression techniques  [18]. However, deep-learning approaches often requires a large dataset to perform the learning and do not provide immediate access to intermediate steps to analyse and understand the agronomic processes that impact production. An interesting alternative to modelling multiway data has been proposed in the chemometrics literature, the multiway partial least squares regression (N-PLS). This is a modelling strategy introduced by  Bro [17]  and further advanced by Smilde  [19]. N-PLS aims at building a model between a high-order array X of independent variables and a response array y  [20]. In this paper, PLS regression is extended to multiway data (N-PLS), with the main emphasis on three-way data, by including time as a dimension.
It is important to note that N-PLS imposes a trilinear structure on the data  [21]  and when introducing time into the analysis, this implies that the emphasis is exclusively on the development of a correlational structure over time  [22] , i. e. , time will determine both the variability between individuals and the variability between variables (wavelengths). The N-PLS methodology allows a joint evaluation of time and variables, in order to determine the interaction between them, to know if and how the variables modify their "behaviour" at different occasions. To the authors' knowledge, such an approach has not been proposed in the analysis of remotely sensed satellite imagery. Therefore, the objectives of this study are (i) to propose a formalism to apply the N-PLS approach to a time-series of images at the regional scale in order to predict a variable of interest with a small ground truth dataset, (ii) to show the value of the approach for its potential to generate knowledge in an agricultural case study, and (iii) to identify the possible limitations of the approach when dealing with image time-series on large scales with series that may be incomplete for certain areas.
N-Way Partial Least Squares In general, the PLS approach is particularly useful when one or a set of dependent variables needs to be predicted by a (very) large set of predictor variables (or time-series) that are strongly cross-correlated  [25]. The strength of N-PLS is that it summarizes all latent information from a large 3-way dataset of object variables (X) and relates it to a dependent variable (y) using a relatively low number of parameters, which makes the prediction more robust  [26]. This situation leads to an analysis that is able to extract the maximum information possible from samples measured at different times based on cubic structure. For these remote sensing data, the 3-PLS1 method  [17]  was considered here to generate a three-way array X = ||X i,j,k ||. The first of the three dimensions of cube X corresponds to the objects (I), the second to time (J) and the third to spectral bands (K).
N-Way Partial Least Squares In general, the PLS approach is particularly useful when one or a set of dependent variables needs to be predicted by a (very) large set of predictor variables (or time-series) that are strongly cross-correlated  [25]. The strength of N-PLS is that it summarizes all latent information from a large 3-way dataset of object variables (X) and relates it to a dependent variable (y) using a relatively low number of parameters, which makes the prediction more robust  [26]. This situation leads to an analysis that is able to extract the maximum information possible from samples measured at different times based on cubic structure. For these remote sensing data, the 3-PLS1 method  [17]  was considered here to generate a three-way array X = || Xi,j,k ||. The first of the three dimensions of cube X corresponds to the objects (I), the second to time (J) and the third to spectral bands (K).
Moreover, Hird et al. [33]  have demonstrated that there is an impact of varying atmospheric conditions and sun-sensor-surface viewing geometries on satellite-derived time-series. A preliminary sensitivity analysis with a noise reduction approach and given time steps provided information on the sensitivity of the variation of the P and N parameters to interpolation. The parameters P and N involved in the interpolation setting, were optimised by cross validation of 2 blocks repeated 5 times of a N-PLS between cube X and vector y using the calibration dataset (see Section 2. 3. 2). For this analysis, values of P and N ranged from 10 to 50 and 5 to 30, respectively.