dataesr/scientific-paragraphs-categorization huggingface dataset:
A novel perspective on relations of short peptides: embedding space analysis Every year, algorithms improve natural language processing (NLP) tasks such as automated translation or question answering, in particular by feeding large text corpora into Deep Learning (DL)-based Language Models (LMs)  [121]. These advances have been transferred to protein sequences by learning to predict masked or missing amino acids using large databases of raw protein sequences as input  [122, 123]. Such methods leverage the wealth of information present in exponentially growing unlabelled protein sequence databases by solely relying on sequential patterns found in the input. Processing the information learned by such protein LMs (pLMs), e. g. , by feeding a protein sequence as input to the network and constructing vectors thereof from the activation in the network's last layers, yields a representation of protein sequences referred to as embeddings  [122].
1  in  Elnaggar et al. [122] ). The protein Language Model (pLM) ProtT5 was trained solely on unlabelled protein sequences from BFD (Big Fantastic Database; 2. 5 billion sequences including metagenomic sequences) [126] and UniRef50. ProtT5 has been built in analogy to the NLP (Natural Language Processing) T5 [121] ultimately learning some of the constraints of protein sequence. As ProtT5 was only trained on unlabelled protein sequences and no supervised training or fine-tuning was performed, there is no risk of information leakage or overfitting to a certain class or label. As a result, every protein was represented as 1024-dimensional per-protein embeddings. Those high-dimensional representations were projected to 3 days using UMAP (n_neighbors = 25, min_dist = 0. 5, random_state = 42, n_ components = 3) or PCA and coloured according to their respective group to allow for visual analysis.
Embeddings and 3-day plots were created using the bio_embeddings package  [127]. All information on sequences used in the machine learning approach is specified in the additional material in a general table (Additional file 33). Unaligned mature and full sequences are given as fasta files (Additional files 43 and 44). However, the sequence data set for the ML approach differs from the sequence set used for phylogenetic analyses in that respect, that only unique mature sequences can be used. Sequence similarities for alignments (Additional file 45) were calculated by dividing the aligned sequence length by the number of sequences that align with a BLOSUM62 score of above 0, multiplied by 100. Global alignments were reconstructed with the Needleman-Wunsch algorithm (script attached, BLO-SUM62 matrix, gap_penality = -10, gap_extension_penality = -0.
Acknowledgements BMvR and IK thank  Frank Förster  for fruitful discussions on bioinformatics. BMvR is grateful to  Georg Petschenka  and  Hermann Falkenhahn  for helpful insights and discussion on solitary bees and localities in Giessen. BMvR thanks further  Ingo Ebersberger  for his support. We thank the  Genome Technology Center (RGTC) at Radboudumc  for the use of the  Sequencing Core Facility (Nijmegen, The Netherlands) , which provided the PacBio SMRT sequencing service on the Sequel IIe platform. We finally are grateful to anonymous reviewers from previous manuscript versions for helpful comments.
The final annotation contained models for 20,016 protein-coding genes and 168 tRNAs and was estimated to be 87. 1% complete (BUSCO4 v4. 1. 4). The resulting gene annotation files for C. gigas, E. dilemma, M. beecheii, T. carbonaria and Xylocopa violacea are made available as Additional files 37, 38, 39, 40 and 41 in the Zenodo archive accompanying this manuscript (10. 5281/zenodo. 7934577). evidence based on protein sequences from 11 related bee species: B. impatiens: GCF000188095. 2, B. ter- restris: GCF000214255. 1, A. mellifera: GCF003254395. 2, M. quadrifasciata: GCA001276565. 1, E. mexicana: GCF001483705. 1, F. varia GCA011392965. 1, M. rotun- data GCF000220905. 1, H. laboriosa GCF001263275. 1, D. novaeangliae GCF001272555. 1, M. genalis GCF011865705. 1, N. melanderi GCF003710045. 1. Briefly, funannotate used gene predictions from Gen- emark-ES, Snap v2006-07-28, glimmerHmm v3.
0. 4, Augustus v. 3. 3. 3 and CodingQuarry v2. 0 together with protein alignments in Evidence Modeler v. 1. 1. 1. Too short, gap-spanning or repeat-overlapping gene mod- els were removed (n = 5446) and tRNA genes were detected (n = 168) with tRNAscan-SE v2. 0. 6. Genes were functionally annotated using PFAM v33. 1, the UniProt database v2018_11, EggNog (eggnog_4. 5/hmmdb data- bases: Arthropoda, Insecta, Hymenoptera, Drosophila), MEROPS v12. 0, CAZYmes in dbCAN v7. 0, BUSCO Hymenoptera models v3. 0. 2, Hymenoptera odb9, Sig- nalP v4. 1, and InterProScan5 v81. 0. 1, ASM1312311v1). Repeats were soft-masked using RepeatMasker annotations (GCA013123115. 1_ ASM1312311v1_rm. out) with tabtk, bioawk and seqtk (https:// github. com/ lh3). We used Funannotate v1. 8. 1 [118] and Uniprot (sprot) for homology-based.
Nathan et al. , 2012). However, there is still far fewer datasets describing plant dispersal than plant demography because dispersal remains difficult to measure  (Bullock et al. , 2017). Here, we identified and resolved one of the key challenges that hinders progress in empirical characterization of dispersal: We incorporated the spatial extent of dispersal sources in the analysis of dispersal measurements. One approach to measure dispersal is to use spatially localized sources of dispersing individuals and record dispersal gradients produced from them. These gradients do contain relevant information about dispersal, but they are also influenced by the spatial extent of the source  (Cousens & Rawlinson, 2001; Ferrandino, 1996; Zadoks & Schein, 1979). How can we evaluate this influence to extract more general knowledge about dispersal from specific dispersal gradients? This can be achieved using a mathematical description of dispersal with the help of dispersal kernels.
| Experimental design and data analysis We re-analyzed published empirical data on dispersal gradients using the spatially explicit method that incorporates the spatial extent of the source and compared the outcomes with those based on the conventional point source approximation. We considered three datasets collected in field experiments investigating dispersal of major pathogens of crop plants: (i) the fungus Zymoseptoria tritici that causes septoria tritici blotch in wheat  (Karisto et al. , 2022); (ii)  the fungus Puccinia striiformis that causes stripe (yellow) rust in wheat  (Cowger et al. , 2005; Sackett & Mundt, 2005) ; and (iii) the oomycete Phytophthora infestans that causes late blight in potatoes  (Gregory, 1968)  In each experiment, pathogen spores were inoculated across inoculation areas within experimental plots to create area sources of dispersing populations (orange areas in Figure  2 ).
For each dataset, we chose an appropriate dispersal kernel function based on the original study, to allow for comparison with the results of the original analysis. Then we derived specific expressions for dispersal gradients, firstly, using the point source approximation (i. e. , assuming a point-like source and destinations in the middle of the inoculation area or measurement lines; "1D" in Figure  2 ) and, secondly, based on the spatially explicit Equation (1). Based on these expressions, we estimated dispersal kernel parameters.
| Data analysis The fitting was implemented in Python 3. 7 using packages numpy  (v. 1. 17. 3, Harris et al. , 2020 ), scipy (v. 1. 3. 1, Virtanen et al. , 2020 ) and lmfit (v. 1. 0. 1, Newville et al. , 2014). We used least squares optimization via the function 'Model. fit' of the package lmfit (Levenberg-Marquardt or Trust Region Reflective algorithm, function scipy. optimize. least_squares). The same method was used in all analyses of the experimental data and in numerical simulations. Brute force search was used with experimental data to achieve reasonable starting values for the least squares optimization. We used the estimates of the kernel parameters for septoria tritici blotch and stripe rust to quantify the characteristic scales of dispersal by computing medians (r 50 ) and 90th percentiles (r 90 ) of dispersal distance kernels  (Nathan et al.
For septoria tritici blotch, a moderate reduction in the -estimate in 2D-estimation compared to 1D-estimation translated into a similarly moderate decrease in r 50 and r 90 (Table  2 ). In contrast, for stripe rust, a modest increase in the -estimate in 2D-versus 1D-estimation translated into a dramatic decrease in the spatial scales of dispersal (Table  2 ). In particular, for Madras dataset, 2D-estimation resulted in a nearly two-fold reduction of r 50 and a massive, almost seven-fold reduction of r 90. Although we could not conduct this analysis with the powerlaw function defined by Equation (  11 ) since it cannot be normalized, (  11 ) p2 (r) = r - (12) I 1 (x) = I 0 x - (13) I 1 x d = I 0 w d ∫ w d y d =0 ∫ w y y s =0 ∫ 0 x s =-w x √ x d -x s 2 + y d -y s 2 -.
We demonstrated how to use this theory to extract more knowledge from existing dispersal datasets. The improved estimates can potentially enhance our understanding of ecological dynamics. For example, yellow rust pathogen P. striiformis has recently expanded its geographic range by adapting to higher temperatures  (Milus et al. , 2009). Here, we acquired more accurate estimates of P. striiformis dispersal kernels using the spatially explicit approach, whereby the characteristic dispersal distance r 90 is about seven times shorter compared to conventional estimates (Table  1 ). Thus, our results (combined with knowledge about other relevant biophysical processes) could enable a more accurate prediction of further range expansion of P. striiformis populations, which is likely to be slower than expected based on conventional estimates.
We assumed isotropic dispersal in data analysis and simulations. However, anisotropic dispersal is common in nature  (Soubeyrand et al. , 2007)  and the model can be extended to incorporate it. In this extended model, the probability of dispersal from a source point to a destination point will depend not only on the distance between the points, as in our case, but also on the direction from the source to the destination. Parameters of anisotropic dispersal kernels can then be estimated from measurements of dispersal gradients with the spatially explicit consideration of the source. Empirical data we analyzed characterized populations of passively dispersing plant pathogens. The methodology is applicable to plant and plant pathogen systems that have passive dispersal, but may not be applicable to characterize active dispersal, e.
DATA AVA I L A B I L I T Y S TAT E M E N T Data and source code are provided in public repositories. Data: https://doi. org/10. 5281/zenodo. 7794187, source code: https://doi. org/10. 5281/zenodo. 6976556. [Correction added on 21 April 2023, after first online publication: The data availability statement is updated in this version. ].
Statistical analysis. All statistical analyses were performed using R 4. 1. 2  41 , and figures were generated using the ggplot2 package  42. The significance threshold was set at α ≤ 0. 05 and tendencies were considered for p values ≤ 0. 1. To test whether the human participants experienced different emotions during the two videos, we compared the rating scores for 'fearful' and 'joyful' during the fear condition to those during the joy condition, using twotailed paired permutation tests  28  (symmetry. test function from the package coin  43  with an exact distribution). Of the 30 horses that participated in the test, 5 did not sniff any of the samples during the habituation phase (neither A 1 nor A 2 ) and were therefore excluded from further analysis. The duration sniffing the odors and the number of nostril dilations were explored with generalized linear mixed models (GLMM) from the package glmmTMB  44  , using Poisson distributions.
Distributions, within-group variance and homoscedasticity of the residuals were checked using the package DHARMa 45 for each selected model, showing that the model assumptions were satisfied. When necessary, a post hoc test based on Tukey's methods was performed with the package emmeans  46. The selected models are presented in Table  2  (see Table  S1  for the detailed results of each ANOVA). To further analyze the first reaction of horses according to the type of odor presented, we focused on odor A 1. We calculated a left-nostril bias index measuring the propensity to use the left nostril more than the right. This index was defined as L/(L + R), where L is the number of dilations of the left nostril, and R the number of dilations of the right nostril. This left-nostril bias could vary from 0 (indicating exclusive use of the right nostril) to 1 (indicating exclusive use of the left nostril); a score of 0.
While very low levels of TRPV2 mRNAs were present in normal human epithelial melanocytes (NHEM), a gradual increase in TRPV2 transcripts correlated with the rise of BRN2 expression in melanoma cells (ranging from the noninvasive 501mel, to the superficial spreading melanoma WM793, then to the metastatic melanoma WM266. 4 and 451Lu;  Herlyn et al, 1990; Juhasz et al, 1993; Arozarena et al, 2011a; Arozarena et al, 2011b; Tichet et al, 2015; Arozarena & Wellbrock, 2017) (Fig 1H and Appendix Fig S1D). In the broader CCLE melanoma cell lines dataset, the overall expressions of TRPV2 and POU3F2(BRN2) were also correlated  (Appendix Fig S1E). In addition to TRPV2 mRNA expression, both TRPV2 protein levels (Fig 2A ).
TRPV2 activity controls the calpain-dependent mechanical maturation of focal adhesions. A Quantification of vinculin-stained focal adhesion sites per cell. Confocal images taken on low confluency melanoma cells seeded on fibronectin-coated coverslips were analyzed using the imageJ software to count the number of vinculin clusters per cell. The total number of cells counted for each cell line is indicated in the boxes. Data are presented as box and whiskers plots (Boxes extend from the 25 th to 75 th percentiles, whiskers from min to max, the horizontal line in each box is plotted at the median). **P = 0. 0066, the Mann-Whitney test (501mel); ****P < 0. 0001 the Kruskal-Wallis test followed by Dunn's multiple comparisons tests (WM266. 4 and 451Lu). B Comparison of calpain activity in control (GFP) or overexpressing TRPV2 (GFP-TRPV2) 501mel cells, and in control (shRNA Ctrl) or TRPV2-silenced (shRNA V2-1, -2) WM266.
In metastatic melanoma cells, we investigated whether the mechanistic basis for TRPV2mediated aggressive potential could rely on such reprogramming which, in this specific case, is referred as pseudo-EMT since melanocytes are not epithelial cells and their invasive state may not be exclusively mesenchymal. Nevertheless, modulating TRPV2 expression-either way-had no impact on the expression of EMT markers. We, however, noted that the noninvasive 501mel versus the highly invasive WM266. 4 cells exhibited antagonistic markers ◀ Figure  7. TRPV2 expression is associated with malignancy and poor prognosis in human melanoma. A TRPV2 RNA expression levels (microarray probe: 219282_s_at) measured in normal skin (n = 7 biological replicates), nevi (n = 9 biological replicates), primary (n = 31 biological replicates) and metastatic (n = 73 biological replicates) melanomas (GEO: GSE46517; Data ref:  Kwong et al, 2013).
0028, the Mann-Whitney test). G Kaplan-Meier plot showing the association of TRPV2 expression (10% highest vs. 10% lowest expressers in the skin cutaneous melanoma cohort TCGA dataset) with melanoma patient survival (*P = 0. 0144, the Log-rank (Mantel-Cox) test). Source data are available online for this figure. profiles, corresponding to either a pseudo-epithelial or a mesenchymal-associated signature, respectively, and suggesting that the WM266. 4 have completed a transition process to acquire their metastatic potential. These observations were consistent with their morphology and migrative features, as well as with the expression of the BRN2 invasiveness marker. Regarding the 451Lu cells, the expression profiles of pseudo-EMT and BRN2 markers were representative of a previously described "intermediate phenotype"  (Rambow et al, 2019).
6 mM NaCl, 5. 6 mM KCl, 2 mM CaCl 2 , 1 mM MgCl 2 , 10 mM HEPES, 5 mM Glucose, pH 7. 4) followed by deesterification at 37°C for 15 min. Changes in [Ca 2+ ] i were monitored in cells bathed in HBSS using a DMIRB (Leica) inverted microscopebased imaging system equipped with a 40×/1. 35 UApo N340 high UV light transmittance oil immersion objective (Olympus), a CoolS-napHQ fast-cooled monochromatic digital camera (Princeton instrument), a DG-4 Ultra High Speed Wavelength Switcher (Sutter Instruments) for fluorophore excitation and METAFLUOR software (Universal Imaging) for image acquisition and analysis. Data were acquired every 10 s (emission at 510 nm) at 340 and 380 nm excitation wavelengths and all images were background subtracted. Cells with spontaneous or aberrant Ca 2+ activity were identified by imaging and eliminated from the analysis.
Proximity ligation assay Proximity-ligation assays (PLA) experiments were performed using the Red Mouse/Rabbit Duolink Starter Kit (Sigma-Aldrich) according to the manufacturer's instructions. Plating, fixation, permeabilization and blocking were done as described above. F-actin was detected using Acti-stain-488-Phalloidin. Fluorescence was analyzed using the confocal microscope IX81-based Olympus FV1000 with UPLSAPO-NA 1. 35 60× oil objective and the IQ3 software (Andor). Maximum projection intensities (MIP) of images were created from z stacks with a step interval of 0. 2 lm. Quantification of PLA fluorescent spots was carried out using the particle analyzer application of ImageJ, RRID:SCR_003070 software.
Louis, MO, USA) per well prior seeding. Cell migration toward the lower chamber was continuously monitored every 15 min, and data were collected and analyzed by the xCELLigence 1. 2. 1 software. For wound-healing assays, cells were seeded at high density (4 × 10 4 cells per well of a 96-well plate) in complete medium, enabling the attainment of confluence overnight. A BioTek AutoScratch wound making tool (Agilent, Santa Clara, CA, USA) was used to automatically create reproducible scratch wounds in cell monolayers and nonattached cells were removed by washing. Cells were then placed in complete media containing either pharmacological inhibitors or the DMSO vehicle. Refilling of the scratched area was followed by a time-lapse bright field imaging system (JuLI TM Stage Real-Time Cell History Recorder, NanoEnTek Inc, Seoul, Korea) taking images every 20 min during 24-72 h.
2D cell migration and cell tracking Serum-starved WM266. 4 cells expressing shRNA control or TRPV2 were seeded in chambers coated with fibronectin (Ibidi) filled with serum-free medium. Migration was induced with a 5% FCS gradient and carried out for 12 h. Pictures were taken using an inverted Olympus IX71 microscope equipped with a Cool SnapHQ camera installed on a Delta Vision system. Images were analyzed using the SofWorX and ImageJ softwares. Three-dimensional spheroid growth and invasion assay Melanoma spheroids were prepared using the liquid overlay method. Briefly, 24-well culture plates (Corning) were coated with 1. 5% agarose (Life Technologies) in sterile water. Cells from a single-cell suspension were added at 10,000 per well. The plates were incubated at 37°C in a 5% CO2 atmosphere until spheroids were formed (72 h).
Mice were given 150 mg/kg body-weight of D-luciferin potassium salt (Interchim). Images were acquired 3-5 min after injection and collected in real time until plate saturation was reached. Photons count per second per steradian per square centimeter was recorded by a photon imager system. For BLI plots, photon flux was calculated by using a rectangular ROI encompassing the thorax using the software Photo-vision+ (version 1. 3; Biospace Lab). This value was normalized to the value obtained immediately after injection, so that all mice had an arbitrary starting signal of 100. At necropsy, ex vivo BLI measurement was performed within 15 min after D-luciferin injection.
Immunohistochemistry The melanoma tissue microarray ME1004c (US Biomax Inc. ) came with the clinical stage, gender, age, organ, TNM classification, and HMB45 profiles. Other melanoma samples were obtained from the Rennes University Hospital (CHU) tumor biobank (Centre de ressources biologiques humaines -CRB Sant e). Staining was performed on a Discovery Automated IHC stainer (Roche) using a rabbit polyclonal anti-TRPV2 antibody (1:100, HPA044993, RRID:AB_10960889), or the same concentration of a control isotype, or the melanoma triple cocktail (HMB45+A103+T311, Ventana, Roche). Signal enhancement was performed using the Ventana ChromoMap Kit Slides (biotin free system). Sections were counterstained with hematoxylin and mounted with DPX. A detailed assessment was done by anatomopathologists.
For the TMA, individual tumor regions were analyzed by color deconvolution using the Fast Red, Fast Blue, DAB filter  (ImageJ, Appendix Fig S10D)  based on  (Stanisz et al, 2014). Specific TRPV2 stained regions were then subjected to densitometry analysis. Optical density was obtained using the formula: OD = Log(255/Mean). For immunostaining of metastasis from xenograft experiments, lungs were perfused with PBS, fixed in 4% paraformaldehyde, paraffin-embedded and sectioned. Sections of lungs (4 lm) were treated on a Discovery Automated IHC stainer (Roche) to remove paraffin, unmask epitopes and sequentially stain with rabbit polyclonal antibodies against TRPV2 (1:50; HPA044993 Sigma) or pmel17/HMB45 (1:50) and rat monoclonal antibody anti-mouse CD31 (1:200; clone SZ31) before detection with Alexa Fluor-conjugated secondary antibodies.
2+ channels); TRP (transient receptor potential). B Heatmap comparing TRP channels RNA expression levels (mean of the normalized probe intensities) in nevi (n = 9) and melanomas (n = 104) biopsies (GEO: GSE46517; Data ref: Kwong et al, 2013). Probe set ID: TRPA1 217590_s_at; TRPC1 205802_at;TRPC3 206425_s_at; TRPC4 220817_at; TRPC6 206528_at; TRPC7 208589_at; TRPM1 206479_at; TRPM2 205708_s_at; TRPM3 211422_at; TRPM4 219360_s_at; TRPM6 221102_s_at; TRPM8 220226_at; TRPV1 219632_s_at; TRPV2 219282_s_at; TRPV4 219516_at; TRPV5 208267_at; TRPV6 206827_s_at; no probe for TRPM5/TRPM7/TRPV3 in the used [HG-U133A] Affymetrix Human Genome U133A Array. The red arrow points to TRPV2. C Expression profile of the indicated ion channel analyzed by RT-PCR in the WM266. 4 and 451Lu metastatic melanoma cell lines (middle and bottom panels, respectively).
Acknowledgements We are grateful to  BIOalternatives (FX Bernard)  for providing normal human epidermal melanocytes, to  Dr P-Y Rescan  ( LPGP-INRA, Rennes, France ) for providing access to the zebrafish husbandry and technical guidance, to the  IRSET Inserm U1085 SMS  and DR@TE teams for housing and facility access, as well as to the  Abbelight company (N. Bourg)  and both the MRic and ImageUP microscopy platforms for critical assistance. We thank the  TCGA Research Network. This work would not have been possible without  Dr. FA Rassendren ,  Dr L Larue ,  Dr MD Galibert  and  Dr O Destaing  help and critical discussions, and without  Dr B J egou 's precious support. This work was funded by  Roche/ Groupe Canc erologie Cutan ee de la Soci et e Française de Dermatologie , the  University of Rennes-1 ,  Fondation ARC pour la Recherche sur le Cancer  ( PJA20151203419 ),  La Ligue  contre le cancer (Comit es  35 & 86 ), the  R egion Nouvelle Aquitaine  ( Chaire Universitaire Canaux Calciques  et M elanome, AP;  16/RALPC-P-R-23 ), and the  French Government (ANR, program  # ANR-12-JSV2-0004-001 ).