dataesr/scientific-paragraphs-categorization huggingface dataset:
Measurement of Lysosomal Activity Besides the phagocytic activity, we tested whether the pigments altered the lysosomal activity. The neutral red uptake assay allows a crude evaluation of the endosomal and lysosomal activities as it requires an active proton pump and undamaged lysosomes. The results in Figures  8A, B  showed no significant differences regarding the uptake of neutral red between the conditions with and without pigment, under both acute and recovery exposure. Thus, no damage to the endosome-lysosome pathway was observed, thereby confirming that the decrease in phagocytic ability in the presence of PB28 (500 µg/ml) and corundum (500 µg/ml) impacted solely the phagocytosis process without modifying lysosomal integrity. In order to validate that the phagocytosis results obtained by flow cytometry are not due to an adsorption phenomenon, observations (Supplementary Data 2) via confocal microscopy validated the presence of the beads (used in classical phagocytosis assays) inside the cells and not only on their surface membrane.
FUNDING This project received funding from  GRAL , a program from the  Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ), and from the  Agence Nationale de la Recherche  ( ANR-21-CE34-0025 ). This project received help from MuLife imaging facility, which is funded by GRAL, a programme from the  Chemistry Biology Health Graduate School of University Grenoble Alpes  ( ANR-17-EURE-0003 ). This work used the EM facility at the  Grenoble Instruct-ERIC Center (ISBG ;  UAR 3518 CNRS CEA-UGA-EMBL ) with support from the  French Infrastructure for Integrated Structur-al Biology (FRISBI ;  ANR-10-INSB-05-02 ) and GRAL, a project of the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). The  IBS Electron Microscope facility  is supported by the  Auvergne Rhone-Alpes Reǵion , the  Fonds Feder , the  Fondation pour la Recherche Medicale and GIS-IBiSA.
3. The fragments are concatenated by making use of evolutionary information in the form of predicted inter-residue contacts. We used the Raptorx ContactMap server  [25]  to predict the contacts. The direction of each fragment is determined to maximize the agreement with the predicted contacts. During this stage, the map is segmented automatically, as a consequence of the fragment concatenation procedure. No prior segmentation of the map to individual components is necessary. 4. In the final stage, a full-atom model is constructed and refined against the map by standard approaches, Modeller  [23]  and the real_space_refine tool  [24]  from the Phenix software suite, respectively.
Application to the F420-Reducing Hydrogenase Heterotrimer As a test case, we applied our method to the cryo-EM map of the F420-reducing hydrogenase (EMD-2513) determined at 3. 36 Å resolution  [26]. The map has regions of high and low resolution, correspondingly 3. 3 Å and 6. 6 Å according to ResMap  [5]. The deposited model (PDB ID 4CI0), composed of 3 chains of 386, 275, and 281 amino acids, respectively, was obtained by a real-space refinement of a former model derived from a previous lower resolution map  [27] , which in turn had been built by the manual fitting of homologous protein structures and subsequent refinement. Regions for which no homolog was available had been manually built by using Coot  [9]. In stark contrast, the overall process of our method is independent of any prior structural knowledge of a homologous protein.
Design This was a single blind, randomized controlled, single-centre (university hospital, France) trial comparing Wii and conventional upper-limb therapy in individuals with chronic stroke-related hemiparesis (ClinicalTrials. gov: NCT01806883). Recruitment began in March 2013 and closed in June 2016. Ethical approval was received (CPP Ile de France XI # 12071) and all participants provided written informed consent. The study is reported according to the CONSORT guidelines. Blocked, stratified randomization was carried out by the physiotherapist using a web server with a random list, pre-generated by the software. Patients were stratified according to Fugl-Meyer score < or ≥ 50 and the side of the hemispheric lesions (similar to Michaelsen et al. [8] ). Evaluating therapists were blinded to group allocation.
All participants were out-patients. A reference group of age-and sex-matched healthy control participants were recruited among hospital staff and family (spouses) and friends of the participants with stroke. The purpose of the healthy control group was to record reference kinematic data for the pointing tasks and to be able to compare arm/trunk patterns during Wii therapy between healthy and stroke participants because "normal" patterns for such games were unknown. The inclusion criteria for the healthy controls were age 18 to 75 years, right-handed, not regular users of the Nintendo Wii and with no pathology affecting upper limb movements. Fourteen individuals were recruited (6 females, mean age 51. 6 years, 95%CI 45. 3; 58. 7): 10 underwent the same kinematic evaluation as the participants with stroke, and 10 underwent the same inertial evaluation during a Wii session (see below for evaluations) (i.
Kinematic evaluation Set-up (see supplemental file for more details) An electromagnetic, 6 degrees-of-freedom motion tracking device, the Polhemus Fastrak system (SPACE FASTRAK, Colchester, VT, USA) with 4 electromagnetic sensors was used to record kinematic data (sampling rate: 33 Hz). The sensors were fixed on the manubrium, deltoid insertion, forearm and dorsum of the hand. The 3D position of the radial and ulnar styloids, medial and lateral epicondyles was digitized as described  [19]. The position of the glenohumeral rotation centre was calibrated from passive circumduction movements of the upper arm  [20]. Then the position of those anatomical points was calculated in the local reference frame of the corresponding sensor, and the 3D positions of the anatomical points were computed by using a local-to-global transformation  [19].
Data analysis A custom-made program was developed using Labview software (National Instruments, Austin, TX, USA) to process the data. Only the movement toward the target was analysed (not the return). The tangential velocity of the hand sensor was calculated by derivation of the displacement data. Then the beginning and end (i. e. , when the participant touched the target) of the movements were automatically detected (with a threshold of 0. 05 m/s), then visually checked and validated by using an interactive display. The following variables were calculated: Elbow angle (in degrees): calculated as the angle between a vector from the radial styloid to the lateral epicondyle of the elbow and a vector from the lateral epicondyle of the elbow to the centre of glenohumeral joint. Larger angles denote greater extension.
Results We included 43 individuals with stroke (mean age 56 years, 95%CI 52. 1; 59. 9; 42% females) and 14 healthy control individuals (mean age 52 years, 95%CI 45. 3; 58. 7; 43% females). Of those with stroke, 21 were allocated to Wii therapy and 22 to conventional therapy (Fig. 2 ). Three individuals did not complete the study (2 in the Wii-therapy group and 1 in the conventional therapy group) for reasons unrelated to the study. Therefore, data for 19 participants were analysed in the Wii-therapy group and 21 in the conventional-therapy group. The characteristics of participants with stroke are in Table  1. The groups did not differ in age, sex, time since stroke onset, disability, apraxia, hemispatial neglect, FMA-UE, sensation, spasticity or strength, or any of the outcome measures, except for the MAL quantity score, which was significantly higher in the conventional-than Wii-therapy group.
Set-up An electromagnetic, 6 degrees-of-freedom motion tracking device, the Polhemus Fastrak system (SPACE FASTRAK, Colchester, VT, USA) with 4 electromagnetic sensors was used to record kinematic data (sampling rate: 33 Hz). The sensors were fixed to the participant with tape: one on the manubrium of the sternum; one at the insertion of the deltoid (using a Velcro strap wrapped around the participant's arm); one on the dorsum of the forearm, 2 fingers' width proximal to the wrist joint line; and one on the dorsum of the hand, over the middle of the third metacarpal bone. The 3D position of the following bony landmarks was digitized (radial and ulnar styloids, medial and lateral epicondyles) as described  [12]. The position of the glenohumeral rotation centre was calibrated from passive circumduction movements of the upper arm  [13].
Acknowledgements. We sincerely thank all the individuals with stroke and control participants who participated in this study. We are also very grateful to  Dominique Rouat ,  Elisabeth LeBomin ,  Erika Imandt ,  Marie-Anne Le Bris  and  Magalie Carcaille  for their help with patient recruitment and organization, and to  Julien Boudarham ,  Yves-Marie Vasse  and all the therapists who participated in the patient evaluations. Many thanks to  Gilbert Pradel  and  Tong Li  for supplying the accelerometers and performing the recordings. Thanks to  Nabila-Yasmine Domingo  for study coordination,  Idir Ghout  for the statistical analysis and all the clinical research assistants involved.
Study population and treatment regimen The signed informed consent was obtained from all patients prior to study start (clinicaltrials. gov: NCT01367665). Institutional review boards and independent ethics committees of participating study centers approved the study protocol, which was executed in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. This retrospective study is based on 148 laBCC patients recruited to the STEVIE study between 2011 and 2013 for whom the response was evaluated (Supplementary Table  S1 ). laBCCs included tumors that recurred at the same location after two or more surgical procedures and for which curative resection was deemed unlikely, as well as tumors where substantial morbidity or deformity from surgery was anticipated. Both patients with measurable and nonmeasurable disease (RECIST; version1.
Statistical analysis of the patient cohort Patients' characteristics were described as median and interquartile range for quantitative data and counts and percentage for qualitative data. The cumulative incidence of discontinuation following IR and AR was assessed within a competing risk framework, with vismodegib discontinuation for other reasons than IR or AR considered as a competing event. Association between baseline covariates and the occurrence of discontinuation for IR or AR were estimated using the direct model for the cumulative incidence model proposed by Fine and Gray and quantified by subdistribution hazard ratio  (11). If no event was observed in a subgroup, Gray test for comparing cumulative incidences curves was used instead of the Fine and Gray model  (12). All analyses were performed using R version 3.
Whole-exome sequencing and data processing For the genomic analysis, we used fresh-frozen tumor biopsies taken before the treatment with HPI for four of five cases (NB020, NB021, NB022, and NB023), and sample SG005 was biopsied 6 months after vismodegib treatment initiation. FFPE samples for genomic analysis were taken 1 month, 1 year, and 8 years before the HPI treatment initiation for SG005, NB020, and NB021 patients, respectively. Cases NB023 and SG005 were treated with radiotherapy 18 and 27 years.
We performed whole-exome sequencing of all 5 IR-BCC and compared them with 103 BCC na€ ve of treatment (N-BCC), 9 after treatment BCC with acquired resistance to vismodegib (AR-BCC) and 5 vismodegibsensitive BCC (S-BCC) from previous studies  (8, 14). The exomes were sequenced using Illumina HiSeq 2500 or BGISEQ-500 (sample SG005_T01) sequencers according to the manufacturer's protocols to the mean coverage after deduplication equal to 42Â for tumor and 54Â for normal DNA (100 bp paired-end reads). Reads were mapped using BWA-MEM (v0. 7. 12; RRID:SCR_010910) software  (15)  to the GRCh37 human reference genome and then we used the standard GATK (RRID:SCR_001876) best practice pipeline  (16)  to process the samples and call somatic genetic variants. PCR duplicates were removed, and base quality score was recalibrated using GATK  (17)  Somatic mutations with PASS flag from GATK Mutect2 were additionally filtered to have at least one supporting read from each strand and three reads in total.
Oncogenic mutations were annotated using the OncoKB database (RRID:SCR_014782) MAF annotator  (24). Mutational profiles of the samples were inferred using SigProfilerMa-trixGenerator software  (25). Samples from the previous work (  14 ) used for comparison were processed and analyzed in the similar way. To identify temporal order of mutations for samples with available archival FFPE slides, we first identified somatic mutations in freshfrozen tumors and then searched for reads supporting given alleles in the BAM files of archival FFPE samples using snp-pileup script from the FACETS package  (19). This analysis was restricted to only singlenucleotide variants and regions with coverage higher than 30Â in FFPE samples.
RNA-seq and data processing We sequenced five transcriptomes of IR-BCC and compared them with the previously published N-BCC [n ¼ 51; AR-BCC, n ¼ 7; actinic keratosis/squamous cell carcinoma (AK/SCC), n ¼ 19] and normal skin samples (n ¼ 18). Samples of AK and SCC were retrieved from Hoang and colleagues  (26). IR-BCC samples were sequenced on the BGISEQ 500 sequencer (20 million reads). Reads were mapped using STAR (2. 7. 2b; RRID: SCR_004463; ref. 27)  software to the GRCh37 human reference genome and then read count per transcript was done with HTSeq (v0. 11. 2; RRID:SCR_005514; ref. 28)  on GENCODE annotation (v28lift37; ref. 29). Mapping quality controls were done with Samtools  (21)  and RSeQC (RRID:SCR_005275; ref. 30). All processing steps were combined in a pipeline built with snakemake (v5.
4. 0; RRID: SCR_003475; ref. 23). RNA fusion analysis was performed with the nf-core rnafusion pipeline (v1; ref. 31) , and fusions of interest were validated with PCR. The NF-CORE rnafusion pipeline runs five software for RNA fusion calling [STAR-Fusion v1. 5. 0 (32), Fusioncatcher v1. 00  (33) , Ericscript v0. 5. 5  (34) , Pizzly v0. 37. 3 (35) , and Squid v1. 5  (36) ] and summarizes results. We focused our analysis on known oncogenic pathways using information relative to the activity of their core transcription factors (TF), which is inferred from the expression level of their direct targets  (37, 38). To assess the activity of oncogenic TFs through expression level of their direct target genes we used Dorothea footprinting framework  (37). First, we removed genes with fewer than 50 counts in average across all samples to reduce technical noise, then the read counts were converted into counts per million (CPM) with TMM approach of EdgeR (39) package (RRID:SCR_012802).
Image analysis Quantitative morphometry was performed on all stained slides using the QuPath v0. 2. 3 software platform for whole-slide image analysis (RRID:SCR_018257; ref. 40). Whole-slide scans acquired with a Panoramic 250 Flash III digital slide scanner (3D Histech) were individually loaded into the software. For each staining vector (i. e. , color), estimates of the background staining were applied to maximize differences in expression in QuPath by selecting a region of interest containing an area of background along with examples of strong nuclear counterstaining and chromogen staining and applying QuPath's "Estimate stain vectors" command to identify stain vectors within this region. The "Positive Cell Detection" command was then used to identify cells across the entire tissue section based upon nuclear identification.
YAP1 lentiviral transduction To investigate the role of YAP1 in BCC, we transduced the ASZ001 cell line (RRID:CVCL_6251) with the mouse Yap1 expression lentivirus vector and assessed proliferation rate between ASZ001 and ASZ001 (YAP1 þ ) cells at 5 and 24 hours. We used the mouse Yap1 expression lentivirus vector (mCherry:T2A: Puro) from VectorBuilder (pLV[Exp]-mCherry:T2A:Puro-EFS>mYap1[NM_001171147. 1]; vector ID VB900000-0080jmw). Plasmids were isolated by incubating the bacterial stock for up to 24 hours in 50 mL of LB media supplemented with ampicillin (MCE, HY-B0522; 100 mg/mL). The isolation was performed with Machrey-Nagel NucleoBond Xtra Midi kit for transfection-grade plasmid DNA (Machrey-Nagel, ref. : 740410. 50) according to the manufacturer's protocol. The virus was prepared using CaCl2 transfection  (41).
Upregulated activity of HIPPO-YAP and WNT but not Hh pathway in IR-BCC The activity of the Hh pathway was measured as relative expression of direct target genes of one of its main TFs, GLI1. This analysis revealed activation of the Hh pathway in IR-BCC as compared with skin; however, it was significantly less activated when compared with N-BCC (Fig. 4A ). In line with this, we detected significantly lower expression of Hh pathway target genes such as HHIP and PTCH2 in IR-BCC as compared with N-BCC (Fig. 4B ). We then analyzed the RNA-seq data from the IR-BCC samples for activation of other cancer pathways. A strong increase in the signature of the HIPPO-YAP pathway TF TEAD1 (Fig. 4C ) was observed. IHC demonstrated nuclear YAP1 positivity in a significantly higher fraction of IR-BCC tumor cells than in N-BCC (96.
Acknowledgments  S. I. Nikolaev  was supported by grant  Foundation ARC 2017 ,  Foundation Gustave Roussy and Swiss Cancer Research Foundation   KFC-3985-08-2016. N. Basset-Seguin  was supported by a  Roche Genentech Grant. L. Flatz  was supported by the  Swiss National Science Foundation  grant  PP00P3_157448  and  Research Fund of the Kantonsspital St. Gallen  ( 11/19 ). The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U. S. C. Section 1734 solely to indicate this fact. Received October 19, 2021; revised December 3, 2021; accepted January 20, 2022; published first January 24, 2022.
Fungal Species Aspergillus flavus (GenBank accession number OL907105) and Aspergillus parasiticus (GenBank accession number OL907106) from the culture collection of the University of Western Brittany (Brest, France) were used in the present study. The cultures were maintained on Potato Dextrose Agar (PDA) at 25 ± 2 • C. The well-grown colonies were sub-cultured for a week before the experiments.
jrc. ec. europa. eu/) and Data Collection Reference Framework (DCRF) (https://www. fao. org /gfcm/data/dcrf/ru/). Due to reported ontogenetic changes in hake otoliths  (Morales-Nin et al. , 2014)  and the possible influence of maturity stage on both shape and chemistry, only juvenile hake were studied (females <28 cm TL and males <16 cm TL). The use of immature fish is also supported by previous works  (Lombarte and Castellón, 1991; Lombarte et al. , 2003)  where immature otoliths of different species of Merluccius showed higher influence of external factors (especially temperature) than in mature specimens. Moreover,  Recasens et al. (1998)  reported a shorter depth range (less environmental variability) in juvenile than in adult specimens of Merluccius merluccius. A subsample of the females of the large sample was used for microchemistry analysis.
Otolith microchemistry Otoliths were prepared under clean conditions and analyzed using a CETAC Teledyne Technologies Laser Ablation System LSX-213 G2 + coupled to a Thermo-Finnigan Element XR Inductively Coupled Plasma-Mass Spectrometer (LA-ICP-MS). We analyzed the otolith edge area only, because it is assumed that this area summarizes the environmental conditions experienced by a fish just before it was captured. A suite of 27 different isotopes was determined, but after a complex data pre-processing  (Martí-Puig et al. , 2019)  and a rigorous quality control (fully detailed in Supplementary material S2), the small sample matrix used for classification and multivariate analyses of otolith shape and microchemistry (see Section 2. 4) was composed of 154 otoliths (rows) and seven elements ( 23 Na, 24 Mg, 43 Ca, 44 Ca, 55 Mn, 88 Sr and 138 Ba) from 10 GSA subunits.
Classification and multivariate analyses The resulting matrices (i. e. , the large sample matrix of shape and the small sample matrix of shape+microchemistry) were analyzed following two complementary statistical strategies (classification and MANOVA), which were each completed at two spatial scales (GSAs versus 3-areas). Linear discriminant analysis (mass library from the R package) was used as a classification method because preliminary trials demonstrated that it performs better than alternative classification methods (as in  Jones et al. , 2017 ; details provided in Supplementary materials). Classification success was assessed using leave-one-out cross-validation. In addition to success rate, predicted versus observed unit membership were compared using the kappa coefficient  (Artetxe-Arrate et al.
, 2019). Moreover, actual classification rates were then compared with the baseline success after randomly shuffling the unit membership of fish (30 iterations), which allowed comparison of success rates obtained with different sample sizes and numbers of units. Finally, between-unit differences were assessed using MANOVA as implemented in the rda library from the vegan package  (Oksanen et al. , 2020). Variables were standardized (i. e. , mean-subtracted and divided by standard deviation) thus the same weight is given to any variable. Significance of between-unit differences was assessed after 1000 permutations. In addition to the anayses above, unsupervised clustering methods were used as an alternative to the two a priori defined spatial scales above (GSAs versus 3-areas). These are data-driven methods because the number and limits of the units are not a priori defined.
Large sample (shape only) For the large sample (1656 otoliths), the classification success at the GSA level was 17 %, while this figure reached 54 % after pooling GSAs into three larger units (Western, Central and Eastern Mediterranean). Note, however, that the crude value of classification success heavily depends on the number of units considered. In our case, the classification success at the GSA level was five times greater than expected by chance after accounting for this effect, while it was very close to that expected by chance at the 3-units spatial level. The kappa coefficient displayed the same pattern, and the value at the GSA level was better (z = 34. 2) than at the 3-units level (z = 9. 0). MANOVA results strongly supported the existence of shape differences at the two spatial levels (Table  1 ).
Acknowledgments We are very grateful to all the people and organisations that helped in the collection of samples, especially:  Hichem Kara  ( Annaba University, Algeria );  Adel Gaamour  ( Institut National Des Sciences et Technologies de la Mer de Salammbô, Tunisia );  Sabrine Mekni  ( University of Tunis El Manar, Tunisia );  Hatem Hanafy  and  Reda M. Fahim  ( Arab Academy for Science, Technology & Maritime Transport, Egypt );  Abdelnasser Madi (Palestine) ;  Sharif Jemaa  and  Myriam Lteif  ( CNRS-L National Council for Scientific Research, Lebanon ); Meltem ( METU Institute of Marine Sciences, Turkey );  Sébastien Couette  and  Camille Lutet-Toti  (  École Pratique des Hautes Études, France );  Christoph Stransky  ( Thünen Institute, Germany );  Julia Olivia Davies  ( DTU Aqua, Denmark );  Christelle Paillon  and  Eric Durieux  ( Corsica University, France );  François Garren ,  Romain Elleboode ,  Angelique Jadaud ,  Gregoire Certain  and  Pascal Laffargue  ( IFREMER Institute, France ).