dataesr/scientific-paragraphs-categorization huggingface dataset:
A team of Japanese researchers developed the TPF (Two-Phase Flow) experiment in the KIBO module with the support of Japanese Space Agency JAXA. This experiment was operated onboard the International Space Station(ISS) during from July 2017 to March 2018, and from February 2019 to July 2019. Two series of flow boiling experiments with n-Perfluorohexane in a 4mm diameter tube were conducted in a metallic copper tube and in a transparent glass tube coating by a gold deposite. The data are under evaluation with a special care to the estimation of the heat losses for a precise evaluation of heat transfer  [24]. An American experiment Flow Boiling and Condensation Experiments (FBCE) was launched to the ISS in August 2021. Flow boiling of n-Perfluorohexane in saturated conditions is under investigation in a rectangular channel of dimensions 114.
Parabolic flights For the parabolic flight campaign, the experimental set-up was accommodated into 2 racks: one containing the fluid loop and electronics in a double containment (Rack 1) and one is the "control Rack" (Rack 2) containing the electrical supplies and 3 computers for data and image acquisitions. It is operated by three people (see Figure  12 ). The 76th ESA Parabolic Flight Campaign took place in Paderborn (Germany) from 25th to 30th June 2021, with 3 flights of 31 parabolas each. It was a partial gravity campaign with 31 parabolas in microgravity, 31 parabolas in Moon gravity (0. 16g) and 31 parabolas in Mars gravity (0. 38g). The periods of partial gravity were at least 20 s long. Experiments were performed with 3 mass fluxes G=50, 100 and 150 kg/m -2. s -  , corresponding to liquid Reynolds numbers Re l of about 650, 1300 and 2000, respectively and qualities x up to 0.
Bubble radial distribution in the tube In order to check the axisymmetry of the two-phase flow, some experiments were performed on ground in vertical upward bubbly flow and during the parabolic flight campaign. On ground experiments, nucleate boiling was generated in the last part of the preheater 2 PH2. 3 at 3 mass fluxes G=50, 100 and 150 kg/m -2. s -. As mentioned previously for parabolic flight experiments, only PH2. 1 was utilized. To analyse the bubble distributions in the tube, the power was limited in the preheater 2 and the quality x was lower than 0. 01, to have a limited number of bubbles to ease the bubble detection and to avoid overlapping of several bubbles. The images of the 2 cameras are processed using Matlab software following the different steps (see Figure  14 ): starting from the raw image (a), substraction of a background image without bubble (b), enlightening of the bubbles (c), binarization (d), bubble filling (e), morphological opening (f), contour detection (g).
Patients We performed a single-center, retrospective study on 64 patients undergoing AC with brain mapping through VR tasks. All the patients came from two clinical trials: ClinicalTrials. gov NCT03010943 and NCT04288505. These two trials were approved by the ANSM ("Agence nationale de sécurité du médicament et des produits de santé"), the ethics committee and the CNIL ("Commission Nationale de l'Informatique et des Libertés") (NCT03010943: ID-RCB: 2016-A01139-42, ethics committee: CPP OUEST II, date of approval: 18 October 2016; NCT04288505: ID-RCB: 2020-A00074-35, ethics committee: CPP Est II, date of approval: 17 April 2020). All patients signed a written informed consent form.
VR Tasks VR applications have progressed at a similar rate to VRHs. We initially used nondedicated VR applications developed as entertainment  [37] [38] [39] [40]  or to simulate social interactions, with the social platform vTime ®  [35, 41]. We then developed three types of dedicated applications. The first VR application was developed from the DO80 picture-naming test widely used to assess language function during AC (VR-DO80)  [36, 37]. The second VR application was developed from the Esterman test for intraoperative visual field assessment during AC (VR-Esterman)  [13, 15]. The Esterman test is the current gold standard for testing binocular visual fields and is used officially for driving license authorization in accordance with European recommendations. The third VR application developed was an interactive application called VR-TANGO (task for awake neurosurgery exploring gaze and TOM), which was developed for the simultaneous assessment of visuospatial exploration and detection of social cues, such as low-level TOM, including gaze processing and emotion recognition.
Eye tracking data showed that patients never looked at the sentence "this is. ". This observation suggests that the patient "said" the sentence to themselves automatically, focusing only on the naming task. We observed that some areas for which the result was unclear during DES with the computer tablet (hesitation or delay in denomination) were clearly not eloquent areas during mapping with the VRH  [36, 37]. Visual field assessment was performed using the VR-Esterman task in 10 ACs (14. 5%). This procedure was reserved for rare situations, such as lesions close to optical radiations, patients with a pre-existing scotome or blindness of the contralateral eye, and patients for whom a normal visual field was essential for their professional activity. The first VR-Esterman task used was performed using an Oculus VRH  [13].
For evaluations of social cognition, facial emotion recognition tasks are frequently performed during AC, based on photographs of individuals displaying one of the six primary facial emotions (anger, happiness, fear, surprise, disgust and sadness) extracted from several tests (Ekman's Faces, the Brief Affect Recognition Test, the Japanese and Caucasian Brief Affective Recognition Test, and the ATR facial expression database)  [18, 54, 55]. Published results suggest that, when presented for 10 sec at the patient's bedside, the emotion portrayed by each photograph is correctly identified by more than 70% of patients. In our experience, tests involving the recognition of facial emotions from photographs are difficult to perform during AC, with a high rate of error, even in the absence of DES.
Dynamic facial expressions are more accurately recognized than static expressions  [56, 57]. A simplified and adapted "reading the mind in the eyes test" (RMET) based on photographs centered on the eye region is also performed in patients undergoing AC for right-side low-grade gliomas, to explore TOM  [28, 58]. However, RMET appears to measure emotion recognition rather than TOM ability  [59]. Rather than immediately developing a specific application to test and map social cognition, we initially evaluated the potential of the available VR social networks, such as the social VR application vTime ®  [35]. This application simulates virtual social interactions with an avatar controlled by the neuropsychologist, who also wears a VRH. However, we experienced several limitations of the use of the social VR application vTime ® , precisely because of the lack of control of all potent non-verbal language cues, including facial expressions and eye gaze.
Limitations The main limitation of this study is that it does not provide clinical results for VR tasks during AC in terms of test performance, clinical preservation and extent of resection. Several established bedside tests are currently being performed to evaluate the performance of VR tasks before their use in clinical practice for brain tumor resection in awake patients (ClinicalTrials. gov NCT04288505). For example, the bells test  [66] , the Ekman test  [67]  and the RMET  [68]  are being used to assess the performance of the VR-TANGO task for detecting visuospatial and social cognition impairments. Once this validation is complete, it will be possible to perform prospective studies to assess the benefits of using VR tasks during AC to preserve language, visual field, visuospatial and social cognition or, more globally, the ability to perform daily life activities.
Acknowledgments: We thank patients for their contribution to the two clinical trials (ClinicalTrials. govNCT03010943 and  NCT04288505 ), the delegation for clinical research and innovation (DRCI,  CHU d'Angers, Angers ) for managing the legal and administrative aspects of this study,  Doctor Ghislaine Aubin  and  Nolwenn Madec  ( Département de Neurochirurgie, CHU d'Angers, Angers, France ) for bedside evaluations of patients,  Professor Philippe Allain  ( LPPL-EA4638 ,  Université d'Angers, Angers, France ) for assistance in the design of the clinical trial  NCT04288505 ,  Fanny Leluan-Higgins  for advice and  Alex Edelman  and Associates for correcting the English of the manuscript.
Raman spectra were acquired at room-temperature using a Bruker MultiRAM FT-Raman spectrometer for 1000 scans with a spectral resolution of 2 cm À 1 using a Nd:YAG laser (1064 nm, 500 mW) as the excitation source. Fitting and deconvolution of the spectra were performed in selected regions to address the TFSI and PC speciation separately using the PeakFit TM software and Voigt functions.
Acknowledgements The funding from theEuropean Union's  Horizon 2020 research and innovation programme   H2020 FETOPEN-1-2016-2017  ( CAR -BAT , grant agreement # 766617 ) is gratefully acknowledged. FÅ and PJ are also grateful for the financial support provided from the  Swedish Energy Agency  by the project " Highly Concentrated Electrolytes"  (# 39909-1 ) within " Batterifondsprogrammet ". The continuous support to PJ from several of  Chalmers Areas of Advance: Materials Science, Energy, and Transport , is highly appreciated. FÅ and PJ are in addition grateful for the computational resources provided by the  Swedish National Infrastructure for Computing (SNIC) at Chalmers Centre for Computational Science and Engineering  (C 3 SE)/e-commons.
Immunohistological Staining For histologic analysis, slides were prepared using Masson's Trichrome Stain, Mast Cell Tryptase antibody, and von Willebrand Factor/Factor VIII complex antibody (Dako) and counterstained with hematoxylin and eosin (H&E) using Dako Omnis automated platform. Slides were scanned at 400× magnification using a Pannoramic 250 Flash II scanner (3DHistech, Budapest, Hungary) and were evaluated using CaseCenter software with QuantCenter extension (3DHistech, Hungary). Up to five hotspots in the wound area per slide were analyzed as described by Weidner et al. [35]. Collagen evaluation was performed using the "Pattern Recognition" tool of the QuantCenter package. Each hotspot field was classified in terms of occupancy by structures automatically recognized as collagen. Mast cells were counted in the hotspot fields by the user.
Statistical Analysis All results were presented as the mean ± range. The data distribution was evaluated using the Shapiro-Wilk test. Statistical evaluation of the wound size was performed using a two-way ANOVA with Tukey's multiple comparisons test. Masson's Trichrome Stain, Mast Cell Tryptase, and von Willebrand Factor/Factor VIII complex staining results were evaluated using the Brown-Forsythe and Welch ANOVA test. Collagen III, CD45, CD11b, CD4, CD8a, TUNEL, and Ki67 staining results were analyzed using the Kruskal-Wallis test. GraphPad Prism software (version 9. 4. 1; GraphPad Software, Inc. , La Jolla, CA, USA) was used for all evaluations. p < 0. 05 was considered statistically significant.
Results Identification of Temnothorax longispinosus behavioral phenotypes. Task specialization in the ant T. longispinosus is neither genetically fixed nor rigid, but can change with age and in response to colony needs  39. We conducted behavioral observations of seven T. longispinosus laboratory colonies to identify individuals that specialize in brood care behavior (hereafter referred to as nurses), and others that specialize in foraging (hereafter referred to as foragers) (Fig. 1A ; see "Methods" section for more details). The grouping of workers into the two behavioral categories was based on the frequency of their location inside or outside the colony and on their behavior, especially whether they showed brood care or foraging behavior (Dataset S1). Individuals identified as nurses interacted with the brood in 54% ± 22% (mean ± sd) of the observations, and were recorded outside the nest in 1% ± 3% of the observations.
The influence of colony identity was controlled for by including it as an explanatory variable in both full and reduced models. A Benjamini-Hochberg adjusted p value of 0. 05 was set as a threshold to obtain genes whose variation was significantly explained by behavioral specialization in each tissue. We detected 339 differentially expressed genes (DEGs) in the brain (223 upregulated in nurses, 116 in foragers), and 2267 in the antennae (1241 upregulated in nurses, 1026 in foragers; Dataset S2 and Fig. 1B ). We found an overlap of 162 DEGs between the two tissues, including 143 DEGs that showed differences in the same direction across tissues. Principal component analyses (PCA) for both brain and antenna data reveal that most samples clustered by behavioral phenotype rather than colony of origin (Fig.
Foragers overexpressed 27, 19, 8, and 8 OR genes from the L, V, P, and H subfamilies, respectively, while no OR genes from these subfamilies were upregulated in nurses (Dataset S3). We found that 63% (27/43) of the genes from the L subfamily and 73% (8/11) from the P subfamily were overexpressed in foragers, which represents a significant overrepresentations (L subfamily: Fisher's test, odds ratio = 3. 67, p value < 0. 001; P subfamily: Fisher's test, odds ratio = 5. 25, p value < 0. 02). For the V and H subfamilies we did not find such an overrepresentation, likely due to lower gene numbers (V subfamily: Fisher's test, odds ratio = 1. 64, p value = 0. 17; H subfamily: Fisher's test, odds ratio = 1. 71, p value = 0. 30). On the other hand, we found that 80% (51/64) of the OR genes overexpressed in nurses belong to the 9-exon subfamily.
Methods Sample collection and behavioral determination. A total of seven colonies of the ant T. longispinosus were selected with an average colony size of 110 ± 31. 5 workers (mean ± sd, Dataset S4) at the moment when the workers were sampled. The ants were collected in the forests of the Edmund Niles Huyck Preserve, Renssellearville, NY, USA (42°31′41. 0′′N 74°09′38. 8′′W), in June of 2018 with permission. Upon collection, we housed each colony in a plaster-floored nesting box (43 cm × 28 cm × 10 cm) divided into three chambers containing a single slide nest, in which the colony relocated. A slide nest is an artificial nesting site comprised of a small Plexiglas cavity sandwiched between two glass microscope slides. Colonies were established at the Johannes Gutenberg University in Mainz, Germany, under a 14 h:10 h light:dark photoperiod at 18 °C to a 22 °C temperature.
Each dissection was completed in less than 5 min to prevent RNA degradation. We dissected brain and antennae tissues from 48 nurses and 49 foragers. We pooled the brains and antennae from seven workers from each behavioral state and colony. The only exception was the "GO" colony (NY18 E110), for which we pooled only 6 brains and 12 antennae from 6 nurses (Dataset S4) due to the loss of one sample during the dissection process. Immediately after dissection of each brain and antennae, the Eppendorf tubes were kept on dry ice while we dissected the remaining individuals. Brain and antennae tissues were homogenized with a pestle. Sample brains were transferred separately to a 1. 5 ml Eppendorf tube containing 50 μl of TRIzol. We added 50 μl chloroform to each brain and antenna samples, gently inverted for 30 s and then centrifuged samples at 12,000 × g for 15 min at 4 °C.
We collected the resulting supernatant and precipitated RNA with 25 μl 70% ethanol. We conducted the subsequent RNA extraction with the RNeasy Mini Kit (Qiagen), following the manufacturer's instruction. The resulting 28 samples (14 brains and 14 antennae) were stored at -80 °C until library preparation. RNA-seq libraries were prepared by Novogene Company Limited, Cambridge, UK, using the NEBNext Ultra RNA Library Prep Kit for Illumina according to the manufacturer's protocol. After amplification and purification, 28 libraries were sequenced on an Illumina NovaSeq 6000 S4 flow cell platform using a paired-end 150 bp. Approximately 43 million raw reads were generated from each library (Dataset S4). Gene expression analyses. Raw data obtained from Novogene were checked using FastQC v. 0. 11.
9 158 , and Illumina adapters were removed using Trimmomatic v. 0. 36 159. The protein-coding genes of T. longispinosus together with the manual OR annotations (GCA_004794745. 1) 160 ; and the congener T. curvispinosus (GCA_003070985. 1) were retrieved from the NCBI database and we used Liftoff v. 1. 6. 1 tool 161 to assign these annotations to the recently published T. longispinosus genome  72. In total, 10,029 of 13,061 (~77%) annotated protein-coding genes were assigned from the original T. longispinosus assembly (genes identified as "DBV15") and 4808 were assigned from T. curvispinosus (genes identified as "LOC"), for a total of 14,837. For gene expression analysis, reads were mapped to our T. longispinosus genome assembly, and the read counts table was generated using STAR 2. 7. 0 162 with default settings.
Detailed mapping statistics for each sample is available in Dataset S4. We used the deseq2 v1. 16. 1 package for R to identify differentially expressed genes 163. To avoid biased results due to low read counts, we removed from the counting matrix those genes for which less than 10 of the reads mapped to at least 6 of our 14 samples (n -1 of the smallest sample size). Then, we conducted a differential gene expression analysis with DESeq2 164. We began with comparisons between nurses and foragers using the ~Colony+Task model, followed by a likelihood ratio test (LRT) approach, with colony ID as a fixed factor. Genes were considered differentially expressed if the false discovery rate (FDR), using Benjamini-Hochberg procedure, had an adjusted p value of ≤0. 05. The resulting lists of DEGs refer to genes that are overexpressed and underexpressed in foragers compared to nurses.
We used the online tool Venny v. 2. 1 (https://bioinfogp. cnb. csic. es/ tools/venny) to generate a Venn diagram containing the DEGs associated with task and tissues. Separation of differentially expressed genes by task was visualized by performing principal component analysis (PCA) with a 95% confidence ellipse using the ggplot2 v3. 4. 2 package for R 165. For PCA, we used the transformed reads of filtered transcriptomes from all contigs using the plotPCA function provided by DESeq2. Samples "GO", "BG" and "YY" showed a divergent expression pattern. To ensure that our results were not influenced by these deviating samples, we re-run the DEseq2 analyses repeatedly removing sample after sample. This resulted show slight shifts in the number of DEGs in the antennae (2267 with all samples vs.
1960 without "GO"; 1861 without "BG"; and 1862 without "YY") and in the brain (339 with all samples vs. 227 without "GO"; 272 without "BG"; and 346 without "YY"). However, the main findings remained similar and a large number of differentially expressed ORs were always found in all analyses (209 with all samples vs. 195 without "GO"; 209 without "BG"; and 179 without "YY"). Finally, OR genes that were upregulated in each behavioral phenotype were visualized in a volcano plot using ggplot2. All statistical tests and graphical visualizations were performed in RStudio v. 1. 4. 1106 166. Identification of behavior candidate genes and ORs. We used gene annotations based on a BlastX search of the T. longispinosus transcriptome compared to a list of different invertebrate proteomes (i. e. , Acromyrmex echinatior, Apis mellifera, Camponotus floridanus, Drosophila melanogaster, Harpegnathos saltator, Odontomachus brunneus, Temnothorax curvispinosus) downloaded from the NCBI database with an E-value of 1e-5 and below.
Clusters containing more than one sequence match per species were reduced to a single specimen based on the highest blast score. We constructed orthogroups across all of the above species using OrthoFinder 167 , including amino acid sequences from the T. longispinosus proteome 160 , and retained orthogroups containing caste DEGs (Dataset S5) to again compare potential behavioral candidate genes previously identified as involved in regulating the division of labor in social insects  39, 42, 94, 100, 155, [168] [169] [170] [171]. GO enrichment analysis was performed with TopGO v. 2. 44. 0 for R using a Fisher's exact test for the different gene sets compared to the whole genome with the weight01 algorithm 172. Only annotated GO terms with a p value of ≤0. 05 were considered significantly enriched.
OR protein sets were clustered across multiple ant species using OrthoFinder to derive orthologous groups and identify subfamilies for each OR in T. longispinosus. To associate orthogroups with previously identified OR subfamilies, we used OR annotation in Atta cephalotes, Acromyrmex echinatior from Engsontia et al. 67  , and Camponotus floridanus, Harpegnathos saltator, and Solenopsis invicta from Zhou et al. 63, 66. Missing subfamily information was labeled as "unassigned" (Dataset S3).
Statistics and reproducibility. The experiments were performed in seven replicates. Each sample contains the pooled RNA from either the antennae or brain of seven ant workers of the respective behavioral phenotype belonging to the same colony, with the colony representing the level of replicates. Bar graphs show next to median and quartiles, the individual data points. Statistical analyses were performed in RStudio v. 1. 4. 1106, bioinformatics analyses in Bash, and scripts for both are available on Mendeley. Gene expression analyses were performed using DEseq2, and to exclude the influence of outliers, these were removed individually, and results presented without them. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Acknowledgements We thank  Diego Páez-Moscoso  for many helpful discussions and comments throughout the study,  Marah Stoldt  and  Carlotta Martelli  for providing helpful comments on the manuscript,  Marion Kever  and  Jenny Fuchs  for technical assistance, and  Mark Harrison  for providing the manual OR annotations. This project was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) to  SF -GRK2526/1 -Projectnr. 407023052. M. A. C. thanks the  E. N. Huyck Preserve  for support and funding.
Acknowledgements We thank  Stella Stefanova ,  Lorenzo Raeli  and  Emmanuel Traunecker  from the flow cytometry core facility of  Department of Biomedicine  for help in cell sorting,  Christian Beisel  from the  Genomics Facility Basel (ETH Zurich ,  D-BSSE  located in Basel operated jointly with the  University of Basel. ) for sequencing support, and members of the laboratory for helpful discussions and critical reading of our manuscript.
Indeed, assume that we could come up with a code family which is able to decode a linear number of errors in the code length n and which would remain secure when used in a McEliece scheme. In the Hamming metric, the best algorithms for solving the decoding problem for a generic linear code are exponential in this regime in n, whereas they are exponential in m • n in the case of the rank metric. This would give cryptosystems with much smaller keysize in the rank metric case, which somehow mitigates the main drawback of the original McEliece proposal that is its large keysize. This dependency of the complexity exponent in the two parameters m and n also allows for much finer tuning of the parameters of such schemes. A very significant step in this direction was made with the Low Rank Parity Check codes (LRPC) that were introduced in  [37].