dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of Single-Cell RNA Sequencing The markers of temozolomide resistance were validated using the GLASS database as a source of clinically relevant recurrent glioblastoma  [53]. RNA sequencing transcript counts derived from the GASS database were downloaded from Synapse (https://www. synapse. org/glass, accessed on 5 May 2023)  [53, 54]. The samples were selected in order to retain the primary wildtype GBMs and their first recurrence. The preprocessing steps were carried out as described for the current dataset used in this study. Differential gene expression analysis was conducted using the R package EdgeR  [41]. The significantly upregulated and downregulated genes were used for the overlapping with the marker genes of temozolomide resistance derived from the single-cell analyses.
The coverslips were rinsed three times with 3% BSA in PBS and mounted on microscope slides using Vectashield hardset antifade mounting medium with DAPI (Vector Laboratories, Amsterdam, The Netherlands). Fluorescent images of immunolabeled cells were obtained using a CX7 confocal microscope (Thermo Fisher) with 20× objective, so that hundreds of cells per sample and per condition were available for analysis. Images acquired with the CX7 confocal microscope (Thermo Fisher) were analyzed using the ImageJ software v1. 53n. The DAPI signal and those from antibodies were used for segmentation of the nuclei and the cytoplasmic compartments, respectively, as well as the boundary of individual cells. After the segmentation, the area, the integrated density, and the mean gray value were measured. For each image, three background areas were used to correct against autofluorescence.
Acknowledgments : The authors gratefully acknowledge the patients who kindly donated tumor tissue and data, making this study possible. In addition, we thank  Iris Verploegh  for the helpful discussions. M-P. C. acknowledges support from the  Oncode Institute ,  Stichting Ammodo and NWO (Netherlands Organization for Scientific Research) Vidi  Grant. M-P. C. appreciates  Josephine Nefkens Stichting 's support with the UFO microscope.
(D) Venn diagrams displaying the amount of overlapping up-(left) and downregulated (right) genes between recurrent and temozolomide-resistant (T2-TMZ) GS359, and the percentage of the genes related to the two dominant clusters. (E) Volcano plot representing the differentially expressed genes between primary and recurrent glioblastomas derived from the GLASS dataset. The overlapping significant up-and downregulated genes with temozolomide-resistant (T2-TMZ) GS789 are displayed in the white boxes. (F) Venn diagrams displaying the amount of overlapping up-(left) and downregulated (right) genes between recurrent and temozolomide-resistant (T2-TMZ) GS789, and the percentage of the genes related to the dominant cluster. The thickness of the lines in the network graphs represents the percentage of the overlapping genes.
3D world and view rendering The virtual environment used in our model was generated by the software Habitat3D  84  , an open-source tool which provides photorealistic meshes of natural scenes from point clouds acquired with help of a LiDAR scanner (IMAGER 5006i). This environment is mapped on the habitat of Myrmecia ants from Canberra, Australia  37. The mesh spans a diameter of around 65 metres and features large eucalyptus trees and the distant panorama cues (Fig. 1g ). This dataset can be found on the Insect Vision webpage (https://insectvision. dlr. de/3d-reconstructiontools/habitat3d). For speed optimization purposes, we down-sampled the originally highresolution mesh with the open-source software Blender into a lower number of vertices; the rendering was then realized in OpenGL, with the Python libraries Plyfile and PyOpenGL.
Learning walks trajectories analysis We analysed learning walks trajectories of Myrmecia croslandi ants recorded at 100fps (courtesy of Jochen Zeil, from a data set used in  22  ) and Melophorus bagoti ants recorded at 300fps (from a data set used in  15  ). Trajectories were analysed using Matlab (R2016b Matworks). Code and data are available on request. b. Each view cell projects (via Projection Neuron, PN) to both hemispheres' Mushroom Bodies (MB), where it makes pseudo-random connections with KCs: we set each KC to connect to 4 randomly chosen PNs, roughly matching what is observed in insects. We chose 20,000 KCs per hemisphere, which underestimates the number of KCs in ants (> 100,000). At each time step, the 0. 1% KCs with the strongest input (i. e. , the sum of the 4 PNs activities connecting to the KC, which can be seen as the KC's dendritic excitatory postsynaptic potential) activity would be set to 1 (reflecting one action potential), the other KCs would be set to 0.
Our study used underwater visual census (UVC) survey data conducted by the MPS over two austral summers (2019/2020 and 2020/2021) to characterise the great hammerhead shark population in the Tiputa Pass (Rangiroa) (Figure  1 ) and Tuheiava Pass (Tikehau) (Figure  1 ). We used photo-identification (photo-ID) and laser-photogrammetry given their easy implementation, costeffectiveness, and non-invasiveness, reducing the impact on the shark's natural behaviour, compared to more invasive techniques (e. g. , use of bait to attract individuals,  Heim et al. , 2021). Specifically, we assessed the number of individuals, sex ratios, seasonal residency patterns at both sites, and investigated potential drivers of occurrence and habitat use in the Tiputa pass, the only site surveyed throughout the whole sampling period for logistic reasons.
A first round of identification and matching was conducted after each sampling period by a first observer. A second observer then went through the database again. Sightings with only one matching trait, with poorquality pictures that prevented identification, or for which both observers disagreed, were considered to have a low confidence index. Only observations with a high confidence index are considered here. Seasonal residency (i. e. , as a form of temporal attachment to a limited area,  Chapman et al. , 2015 ; in our case the study site and its surrounding lagoon or open ocean area) was determined when an individual was sighted within at least one lunar cycle (~one month) between the first and last sighting or with more than one sighting within a month of the austral summer (extending from November to May).
, 2015). Three sources of inaccuracies can occur when using laser photogrammetry  (Deakos, 2010) :  (1)  image distortiondue to the airspace between the GoPro® lens and the waterproof housing refracting the incoming light and distorting the captured image, (2) non-parallel alignment of the lasers and (3) the parallax error leading to shark size being underestimated due to the relative spacing between pointers increasing as shark body position veers away from being perpendicular to the lasers. To quantify distortion, an in-situ experiment was carried out and consisted of filming a grid underwater in a parallel layout. The distortions were adjusted using the Photoshop® software (CS5 version) (correction of -12 out of +100/-100 range) to restore the straight angles from the grid (Supplementary Figure  4 ).
Biotic and abiotic drivers To assess the influence of water temperature on the seasonal occurrence of great hammerhead sharks, we positioned a temperature logger (HOBO ® Pro v2 data logger; accuracy ± 0. 2°C) at the Hammerhead plateau (57 m depth). Water temperature was recorded every 30 min during the austral summer 2020 -2021 only (i. e. , 6 January to 28 February 2021). Daily lunar age was obtained from the equation derived from Stephen R. Schmitt's algorithm: age of the moon = integer ([(age of the moon on March 1 st ) + (number of days since March 1 st )].
Statistical analysis We analysed the impact of biotic and abiotic factors on the occurrence of great hammerhead sharks in location A using the statistical environment software R Studio (Version 4. 0. 4;  R Core Team, 2021). First, we tested the influence of biotic and abiotic drivers on the occurrence of great hammerhead sharks using a generalized linear model (GLM) following  Venables and Ripley (2002)  and  Dobson and Barnett (2018)  for each season (2019 -2020 and 2020 -2021) with a binomial family. Abiotic factors include site (i. e. , location A/B (to check the site effect); categorical variable, fixed), current (i. e. , inflow/outflow/slack water; categorical variable, fixed), water temperature at 57 m (continuous variable, fixed), moon age (continuous variable, fixed), day phase (i.
e. , morning and afternoon; categorical variable, fixed), and hour of dives (continuous variable, fixed). Sex (male/female; categorical variable fixed) was the only biotic factor included in the models. The presence of potential prey, which is also a biotic factor (e. g. , A. ocellatus), was not included in the model because it was recorded only from 19 December 2020 to 4 March 2021. Instead of a GLM, the Kruskal-Wallis Chi-squared test was employed to assess differences in occurrence between the two datasets (S. mokarran vs. A. ocellatus) during the overlapping period (19 December 2020 to 4 March 2021), with a limit of significance of <0. 01. Also, for the 2020 -2021 season, the water temperature was not included in the model because it was not recorded throughout the Austral summer period.
Instead of a GLM, the water temperature was analysed by univariate analysis with the Wilcoxon test with the stats R package (R core team). All other variables were analysed through the GLM for each dataset, and then, univariate analysis was performed to decipher the observed variations for each significant variable in the previous GLM with Bonferroni p adjusted < 0. 01. For the moon age analysis, the distribution of presence/absence sightings was tested with a Wilcoxon test and Chi-squared test (with a limit of significance of <0. 01) with the stats R package (R core team). All graphics were produced with the ggplot2 R package  (Wickham, 2011). All scripts and data used for these analyses are freely available at https://github. com/PLStenger/Mokarran_II allowing the automatization, tracing, and reproduction of the analyses.
In location B, five great hammerhead sharks, all females (sex ratio of 0/5), were identified across 23 sightings, on 11 out of 23 dives (47. 8%) over 15 days. Between one to four solitary individuals were sighted randomly per dives across the pass. All three datasets (MPS data, additional data from dive instructors and historic data) record a total of 1075 sightings of great hammerhead sharks in locations A,  A. bis and B combined, between February 2006  and May 2021. Among these 1075 sightings, a total of 166 (89 from the MPS data, 27 from instructors' data and 49 from historic data) match the 55 identified great hammerhead sharks (Supplementary Table  1 ). In locations A, the PCL ranged between 147 and 297 cm (209. 1 ± 29. 7 cm, mean ± S. D cm; n = 105 sightings) over the two seasons combined, indicating that all measured individuals might be mature (Table  3 ).
In location B, size ranged between 225 and 264 cm PCL (248. 3 ± 15. 9 cm; n = 4 sightings) over one season (Table  4 ), also indicating likely maturity of the f emale great hammerhead sharks. The analysis of the historic database enabled us to identify 30 additional individuals among 54 sightings (48 in location A, two in location A. bis, and four in location B) through February 2006 and December 2019, with a sex ratio of 14/16 (n = 13 males in location A; n = 1 male in location B) (Table  2 ; Supplementary Table  1 ). Males were primarily sighted from August to October and females during the austral summer months from November to May, which is consistent with what we observed during the main survey and suggests distinct temporal habitat use and segregation between males and females on the two atolls.
bis combined, four in location B) out of the 55 individuals identified demonstrated residency behaviour around the passes, where diving activities and sightings occur. Great hammerhead sharks were sighted for a period of up to five months (e. g. , shark N23; between December 2020 and April 2021), although two months was the mean time of observation during the austral summer. Re-sighting ranged between one to six occurrences over one to five months and up to six days/per month (Table  2 ). One seasonal resident (I19) re-sighted on six occasions during a 37-day timeframe, demonstrated the fast-healing capabilities of a 17 cm horizontal open wound (Supplementary Figure  6 ). Out of the 30 individuals identified from the historic database, seven (n = 23. 3%; four females, three males) showcased residency behaviour around the sites, with one (A15) sighted on both location A.
bis and location A (10 km apart) one month apart. Males (n = 3; 21. 4%) were only sighted over one month, up to twice per month. Among the 70 females identified from the three datasets (MPS, instructors and historic images), 29 of them (41. 4%; 25 in location A and A. bis combined, four in location B) demonstrated seasonal residency (Table  2 ). In the location A, sightings occurred in 72. 8% of the time on the "Hammerhead plateau" (outer part of the pass), with sharks roving around in the area, not providing evidence of origin nor destination (lagoon or open ocean/outer reef). From the 55 great hammerheads sharks identified during the main surveys on both atolls, 25 (45. 5%; 21 sharks in locations A and A. bis combined and four in location B) were re-sighted across two to five different austral summer seasons, not necessarily consecutive.
Overall, out of the 70 females identified from the three datasets, 29 of them (n = 41. 4%; 24 in location A and  A. bis combined, five in location B) demonstrated site fidelity (Table  2 ), of which 17 also showed seasonal residency. No great hammerhead sharks identified in 2019 -2021 were sighted at both sites (locations A and B). However, one femaleidentified from the historic dataset (A06) demonstrated geographical connectivity between location A and B, during the austral summers of 2014 -2015 and 2017 -2018. The first sighting was reported on January 2015 in location B and a second observation was made over the same month (unknown exact day) in location A. This female was re-sighted two seasons later in location B (on December 2017 and April 2018) with the last observation on May 2018 in location A.
Influence of the biotic and abiotic drivers on the presence of S. mokarran in location A Temperature data was only recorded during the 2020 -2021 season, and not included in the GLM analysis. Great hammerhead sharks were sighted between 26. 6°C -28. 9°C (median value at 27. 7°C ), which is 74. 2% of the recorded temperature range during deployment (26. 4°-29. 5°C). The Wilcoxon test showed differences in presence/absence (1/0) of great hammerhead sharks in 57-meters depth with P value = 0. 032 (Figure  3 ). Across both seasons (2019 -2020 and 2020 -2021) moon age was the only factor that significantly affected shark sightings (adjusted P value < 0. 001 for both seasons). Sightings increased during days close to the full moon, with a peak two days before the full moon (13 th day of the lunar cycle) (Figure  4 ).
The distribution of presence/ absence observations were significantly different (Wilcoxon test P value < 0. 001), and only present observations were significantly affected by moon age (Chi-squared test P value < 0. 001). During the period from 19 December 2020 to 4 March 2021, ocellated eagle rays were sighted 73. 1% of times in location A (95 out of the 130 dives) and 81. 5% of the observations took place around the "Hammerhead plateau" (E1 area, see Supplementary Figure  1 ). Although the Kruskal-Wallis Chi-squared demonstrated differences in occurrence between the two datasets (S. mokarran vs A. ocellatus; P-value <0. 001), the two distributions were similar, with a peak, with a maximum peak at the end of January and beginning of February, and a second smaller peak at the end of February (Figure  5 ).
Discussion Our study shows that the Western Tuamotu archipelago and Rangiroa atoll are seasonally important for great hammerhead sharks, with a total of 55 individuals identified among 167 sightings obtained across three locations. Results provided evidence of philopatric behaviour for great hammerhead sharks, with more than half of the sharks demonstrating seasonal residency, long-term site fidelity or both. All individuals measured were likely mature and a strong female bias in the sex ratio stands out during the austral summer. Thirty additional great hammerhead sharks were identified from the historic dataset, with further evidence of sexual segregation between males and females and insights on the philopatric behaviours and geographical connectivity between the two sites. In location A, the moon age was the only significant factor affecting shark sightings during both seasons, with observation rate increasing shortly before and after the full moon.
The results from the historic dataset suggest that males are essentially observed between August and October, which supports our lack of males sighted during the austral summers of 2019 -2020 and 2020 -2021. Sexual segregation in great hammerhead sharks has not been reported in fishery-based studies  (Stevens and Lyle, 1989; Cliff, 1995; Piercy et al. , 2010; O'Connell and Leurs, 2015; Raoult et al. , 2019; Hsu et al. , 2020) , unlike other hammerhead species, such as the scalloped hammerhead sharks (Sphyrna lewini, Griffith and Smith, 1834)  (Klimley, 1987; Stevens and Lyle, 1989; Harry et al. , 2011; Noriega et al. , 2011) ; or other marine species  (Sims, 2005; Wearmouth and Sims, 2008 ). Yet, all three studies conducted on the great hammerhead sharks in Bimini, Bahamas, show a female-dominated population  (Guttridge et al.
7% (n=18) of all the females identified (percentage likely underestimated by the method used). The philopatric behaviour of male great hammerhead shark is beyond the scope of this study as the main surveys focused on the austral summer season (e. g. , female biased), however, results from the historic database show preliminary evidence of site fidelity to location A. 4. 4 Impact of biotic and abiotic drivers on the great hammerhead shark presence in location A.
, 2013). Looking at dive instructors' observation data from  Seǵuigne et al. (2023) , grey reef sharks' abundance in the pass tends to decrease between January/May. However, based on anecdotal observations, the overall abundance of grey reefs does not vary during the year, only the location of the groups within the pass during this period, with grey reef sharks moving to another side of the pass, less frequented by divers and closer to the Hammerhead plateau. The continuous annual presence of grey reef sharks in location A does not, therefore, seem to explain the seasonal peak in great hammerhead sharks' presence on its own, but could be an additional driver in the seasonal presence of A. ocellatus. Stable isotope analyses should help better understand the relative importance of prey abundance and predatory opportunities to the occurrence of great hammerhead sharks in location A.
Acknowledgments This study could not have been successfully completed without the  Direction de l'Environnement (DIREN) of French Polynesia  and our financial partners  Blancpain  and " Banque de Tahiti ". We thank the municipality of  Rangiroa  and Tikehau;  Andromède Oceánologie ; The  Polynesian Shark Observatory (ORP)  with  Nicolas Buray ;  Thomas Perceval  and  Christophe Requillart ;  Christian Lumbreras ; our dedicated volunteers  Jean-Marc Belin  and  Sterenn Gallou ; our network of contributing photographers (alphabetical order):  Francis Abbott ,  Sauveur Adamo ,  Vincent Albertini ,  Christian Allanic ,  Nicolas Amiel ,  Arnaud Avare ,  Sophie Babel ,  Bernard Beaussier ,  Michel Bègue ,  Xavier Beźiers ,  Christiane Borg Barthet ,  Jimmy Caire ,  Mathias Castello ,  David Chandeleur ,  Damien Cortet ,  Angeĺique De Steṕhano ,  Bertrand Feuillet ,  Kori Garza ,  Jean-Marie Gayard ,  Etienne Guitton ,  Volker Heitmann ,  Christian Heylen ,  Alain Hurpeau ,  Anne Hurpeau ,  Christophe Kraether ,  Mathieu Junker ,  Baptiste Le Bouil ,  Eva Lefebvre ,  Manuel Lefevre ,  Erick Maillet ,  Charles Marque ,  Gerard Martigny ,  Ugo Mazzavillani ,  Reḿi Millot ,  Marc Mouton ,  Jaime Paumier ,  Francisque Protche ,  ChloéRauchs ,  Serge Renault ,  Marine Pacchiano ,  Peter Schneider ,  Albert Sholz ,  Didier Theŕon ,  Yoshino Katuhilo  and  Sophie Vanruymbeke ; our network of contributing divers (alphabetical order):  Laurent Dureau ,  Raphael Ferreira ,  Beĺen Ferro ,  Jean-Louis Jacquelle ,  Sebastien Hubert ,  Yves Lefèvre ,  Mao Manuarii ,  Martin Marquez ,  Alizeé Meme ,  Claudio Pena ,  Gaël Rossi ,  Yukari Sugimoto , and  Yannick Wagner ; the local dive operators " Rangiroa Plongeé" , " YAKA Plongeé", "CCR Rangiroa", "Top Dive Rangiroa", "Rangiroa Diving Center" , " Raie Manta Club Rangiroa" "Raie Manta Club Tikehau", "Tikehau Plongeé" , " Coco Dive" , " Hititemanava Tikehau Excursion".
The discovery of excess alkalinity above the saturation horizon in the North Pacific has sparked debate about the role of different pelagic calcifiers and their contribution to the alkalinity budget above the thermodynamic saturation horizon  23, 32, 33. We conducted a research cruise from subtropical to subpolar North Pacific waters in which we assessed the pelagic living CaCO 3 standing stock. We targeted the main planktonic calcifiers at five survey stations, from Honolulu, Hawaii, to Seward, Alaska, (Fig. 1 , Tables  S1  and S2 ). We deployed plankton nets to sample calcifying zooplankton and rosettes of Niskin bottles to target calcifying phytoplankton. In addition, four intermediate planktonic towing stations were sampled and integrated into the overall data set (Fig. 1 , Tables  S1, S2 ).
m -3 ) (Table  S3 ) are similar to previous estimates for this region and are higher than values observed across the Atlantic Ocean  46. Our pteropod CaCO 3 concentrations range from 0. 2-8. 6 mg m -3 (Fig. 3a ). We find good agreement between our estimates of pteropod CaCO 3 standing and North Pacific sites in the MAREDAT database  17  (Methods; Fig. 3 ), which show a typical concentration of 0. 5 (0. 2-1. 1, 68% CI) mg m -3. Despite our pteropod CaCO 3 standing stock results being similar to/higher than previous estimates from the North Pacific/ North Atlantic, our values are lower than the proposed 23. 17 mg CaCO 3 m -3 global-mean value of shelled pteropod CaCO 3 standing stock concentration  18. However, our analysis shows that this global dataset is heavily skewed (Skewness = 13. 3, where a value above one indicates a skewed dataset), with the median pteropod biomass value reported  18  being around three orders of magnitude smaller than the reported mean.
As such the global-mean value is not a useful descriptor of this data compilation  18. Our analysis shows typical pteropod CaCO 3 biomass globally is 0. 3 (0. 08-0. 9, 68% CI) mg m -3 in the upper 250 m and 0. 2 (0. 07-0. 8, 68% CI) mg m -3 in the upper 1000 m (Methods; Supplemental Fig. S6 ), two orders of magnitude lower than the globalmean value reported by ref. 18  , and in line with our results from the North Pacific. Our vertically integrated pteropod CaCO 3 standing stocks range between ~64-111 mg m -2 in subtropical gyre and between ~215-1306 mg CaCO 3 m -2 in the subpolar gyre (Fig. 3 ; Table  S3 ; Fig. S5 ). We find good agreement between our estimates and the vertically integrated pteropod CaCO 3 standing stock calculated from North Pacific sites in the MAREDAT database  17  (Methods; Fig.