dataesr/scientific-paragraphs-categorization huggingface dataset:
We followed Thuiller et al. 69  to account for the overall effect of single predictors (i. e. considering their joint contribution to both additive and interactive terms). Each predictor was randomized 1,000 times, and the predictions obtained using original and randomized datasets were compared via the Pearson's correlation coe cient (r). Strong correlations indicate that randomizations had little effect on model performances; for each permutation, variable importance was nally expressed as 1-r. To evaluate model transferability, we followed a leave-one-out approach. We iteratively run the full model, retaining all glaciers except one as the training set; the estimated xed coe cients were then used to predict the expected temperature for the excluded glacier. Differences in sample size between glaciers, as well as those in the monthly frequency of snow-free days within each glacier may in ate agreement scores.
To account for these differences, we thus conservatively downweighted each observation, so that observations of each glacier received a weight = 1/M, where M is the number of recordings in any given sfd category available for that glacier. The agreement between observed and predicted temperatures was measured using i) the coe cient of determination form a weighted linear regression (wR 2 ), and ii) the weighted mean absolute error (wMAE). To con rm that temperatures estimated by model approximate the actual temperature better than other already available products, we also compared observed temperatures to: the ones predicted by the model, the downscaled macroclimate (mT), the time-series of TerraClimate  42  (resolution: 150 arcsec) and Chelsa  43  (resolution: 30 arcsec). For each observation, we extracted climate data for the corresponding year and month, after excluding the observations from 2020 (Chelsa dataset being limited to 2019), and calculated wR 2 and wMAE.
Global projection of soil temperature Obtaining high-resolution estimates of soil temperature in glacier forelands, at the global scale and in several periods, allows estimating soil microclimate variability and temporal variation, measuring the impacts of climate change on microclimate and the potential for microclimate buffering. The aim of this analysis was to assess the variation of microclimate during the last decades, thus we compared microclimate between the periods 2001-2005 and 2016-2020. We used the mean coe cients obtained from the leave-one-out analysis to generate predictions of soil temperature at the global scale, using Google Earth Engine and the rgee R package  70. Due to data availability, the analysis was spatially constrained between 60° S and 72° N. We focused on proglacial landscapes, thus we limited projections to within 3 km from glacier outlines.
The monthly frequency of snow-free days was calculated using the NDSI-derived daily fractional snowcover as detailed in the previous section. For each month, we estimated the percent snow occurrence using monthly values averaged over each ve-year period and bilinearly-interpolated at the 90 m resolution. All cells with sfd values ≤ 30% were masked (i. e. excluded from the analysis). To account for distance from the glacier, we used glacier outlines of the GLIMS dataset  54  (available at https://developers. google. com/earthengine/datasets/catalog/GLIMS_current). Glaciers may have been retreating between 2001-2005 and 2016-2020; consequently, for each period and glacier ("glac_id" eld), we selected the outline with the temporally closer source image ("src_date" eld), and calculated distances according to those positions.
The most recent glacier outline was the one used for calculating the distances for the 2016-2020 projection. Within each cell, we randomly sampled 10 points, two for each distance class. The resulting dataset was composed of 26,040 points, each associated with 12 × 2 (2001-2005 and 2016-2020) measures of monthly soil temperature. After removing points with missing temperature estimates in one or both periods, and cells with < 9 points, the nal dataset was composed of 22,415 records from 2,274 cells. For this set of points, we extracted the monthly average temperature and annual duration of the snow-free season for the two periods. Based on temperature data, we calculated both annual and seasonal (dec-feb; mar-may; jun-aug and sep-nov) microclimate variation (DT) between the two periods (DT = T 2016-2020 -T 2001-2005 ).
Acknowledgements S. M. , M. C. , M. G. , R. A. , R. S. A. , F. G. , F. P. , W. T. and G. F. F. are funded by the  European Research Council  under the European Community's  Horizon 2020 Programme , grant agreement no. 772284  (' IceCommunities-Reconstructing community dynamics and ecosystem functioning after glacial retreat ').
There has been considerable recent progress in designing new proteins using deeplearning methods  [1] [2] [3] [4] [5] [6] [7] [8] [9]. Despite this progress, a general deep-learning framework for protein design that enables solution of a wide range of design challenges, including de novo binder design and design of higher-order symmetric architectures, has yet to be described. Diffusion models  10, 11  have had considerable success in image and language generative modelling but limited success when applied to protein modelling, probably due to the complexity of protein backbone geometry and sequence-structure relationships. Here we show that by fine-tuning the RoseTTAFold structure prediction network on protein structure denoising tasks, we obtain a generative model of protein backbones that achieves outstanding performance on unconditional and topologyconstrained protein monomer design, protein binder design, symmetric oligomer design, enzyme active site scaffolding and symmetric motif scaffolding for therapeutic and metal-binding protein design.
We generate training inputs by noising structures sampled from the Protein Data Bank (PDB) for up to 200 steps  22. For translations, we perturb Cα coordinates with 3D Gaussian noise. For residue orientations, we use Brownian motion on the manifold of rotation matrices (building on refs. 23,24). To enable RFdiffusion to learn to reverse each step of the noising process, we train the model by minimizing a mean-squared error (m. s. e. ) loss between frame predictions and the true protein structure (without alignment), averaged across all residues (Supplementary Methods). This loss drives denoising trajectories to match the data distribution at each timestep and hence to converge on structures of designable protein backbones (Extended Data Fig. 2a ). The m. s. e. contrasts to the loss used in RF structure prediction training (frame aligned point error or FAPE) in that, unlike FAPE, m.
r. m. s. d. between design and AF2 is shown, for the unconditional generation of 300 amino acid proteins (Supplementary Methods). f, Two example 300 amino acid proteins that expressed as soluble monomers. Designs (grey) overlaid with AF2 predictions (colours) are shown on the left, alongside circular dichroism (CD) spectra (top) and melt curves (bottom) on the right. The designs are highly thermostable. g, RFdiffusion can condition on fold information. An example TIM barrel is shown (bottom left), conditioned on the secondary structure and block adjacency of a previously designed TIM barrel, PDB 6WVS (top left). Designs have very similar circular dichroism spectra to PDB 6WVS (top right) and are highly thermostable (bottom right). See also Extended Data Fig. 3  for further traces. Boxplots represent median ± interquartile range; tails are minimum and maximum excluding outliers (±1.
4  and 5 ). We took advantage of the increased size of these oligomers (compared to the smaller unconditional and fold-conditioned monomers described above) and collected negative stain electron microscopy (nsEM) data on a subset of these designs across different symmetry groups. For most, distinct particles were evident with shapes resembling the design models in both the raw micrographs and subsequent two-dimensional (2D) classifications (Fig. 3  and Extended Data Fig. 5f ). nsEM characterization of a C3 design (HE0822) with 350 residue subunits (1,050 residues in total) suggests that the actual structure is very close to the design, both over the 350 residue subunits and the overall C3 architecture. 2D class averages are clearly consistent with both top and side views of the design model, and a 3D reconstruction of the density has key features consistent with the design, including the distinctive pinwheel shape (Fig.
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41586-023-06415-8. Extended Data Fig. 1  | Training ablations reveal determinants of RFdiffusion success. A-C) RFdiffusion can generate high quality large unconditional monomers. Designs are routinely accurately recapitulated by AF2 (see also Fig. 2c ), with high confidence (A) for proteins up to approximately 400 amino acids in length. B) Further orthogonal validation of designs by ESMFold. C) Recapitulation of the design structure is often better with ESMFold compared with AF2.
In many cases, trajectories can be stopped at timestep 50-75 with little effect on the final in silico success rate of designs (left), and when normalized by compute budget (center), success rates per unit time are typically higher generating more designs with early-stopping. Again, this can be done without a significant loss in diversity (right). Extended Data Fig. 3  | Unconditionally-generated designs are folded and thermostable. A) Four 200 amino acid and fourteen 300 amino acid proteins were tested for expression and stability. 9/18 designs expressed, with a major peak at the expected elution volume. Blue: 300 amino acid proteins; Purple: 200 amino acid proteins. B) Colored AF2 predictions overlaid on gray design models (left), circular dichroism spectra at 25 °C (blue) and 95 °C (pink) (middle) and circular dichroism melt curves (right) for all 9 designs passing expression thresholds.
In all cases, proteins remain well folded even at 95 °C. Note that data on 300aa_3 and 300aa_8 are duplicated from Fig. 2f , reproduced here for clarity. Extended Data Fig. 4  | RFdiffusion can condition on fold information to generate specific, thermostable folds. A) 6WVS is a previously-described de novo designed TIM barrel (left). A fine-tuned RFdiffusion model can condition on 1D and 2D inputs representing this protein fold, specifically secondary structure (middle, bottom) and block-adjacency information (middle, top) (see Supplementary Methods 4. 3. 2). RFdiffusion then generates proteins that closely recapitulate this course-grained fold information (right). B) Outputs are diverse with respect to each other. With this coarse-grained fold specification, in silico successful designs are much more diverse (as quantified by pairwise TM-scores) compared to diversity generated through simply sampling many sequences for the original PDB backbone (6WVS).
C) NTF2 folds are useful scaffolds for de novo enzyme design  56  , and can also be readily generated with fold-conditioning in RFdiffusion. Designs are diverse and closely recapitulated by AF2. D) In silico success rates are high with foldconditioned diffusion. TIM barrels are generated with an AF2 in silico success rate of 42. 5% (left bar, pink) with in silico success incorporating both AF2 metrics and a TM-score vs 6WVS > 0. 5. NTF2 folds are generated with an AF2 in silico success rate of 54. 1% (right bar, pink), with in silico success incorporating both AF2 metrics and a TM-score vs PDB: 1GY6 > 0. 5. In silico success was further validated with ESMFold (blue bars), where a pLDDT > 80 was used as the confidence metric for success. Gray: RFdiffusion design, colors: AF2 prediction. E) 11 TIM barrel designs were purified alongside the 6WVS positive control.
Using these potentials dramatically improves in silico success (Two-proportion z-test of in silico success rate: n = 100 designs per condition, z = 4. 3, p = 1. 9e-5). D) Designs are diverse with respect to the training dataset (the PDB). While the monomers (typically 60-100 AA) show reasonable alignment to the PDB (median 0. 72), the whole oligomeric assemblies showed little resemblance to the PDB (median 0. 50). E) Additional examples of design models (left) against AF2 predictions (right) for C3, C5, C12, and D4 symmetric designs (the symmetries not displayed in Fig. 3 ) with backbone r. m. s. d. s (Å) against their AF2 predictions of 0. 82, 0. 63, 0. 79, and 0. 78 with total amino acids 750, 900, 960, 640. F) Additional nsEM data for symmetric designs. The model is shown on the left and the 2D class averages on the right for each design.
G) Two orthogonal side views of HE0537 by cryo-EM. Representative 2D class averages from the cryo-EM data are shown to the right of 2D projection images of the computational design model (lowpass filtered to 8 Å), which appear nearly identical to the experimental data. Scale bars shown (white) are 60 Å. Boxplot represents median ± IQR; tails: min/max excluding outliers (±1. 5xIQR). Extended Data Fig. 6  | External potentials for generating pockets around substrate molecules. A-D) Example in silico successful designs for enzyme classes 2-5 (ref. 57, see also Fig. 4 ). Native enzyme (PDB: 1CWY, 1DE3, 1P1X, 1SNZ); catalytic site (teal); RFdiffusion output (gray: model, colors: AF2 prediction). Metrics (AF2 vs design backbone r. m. s. d. , AF2 vs design motif backbone r. m. s. d. , AF2 vs design motif full-atom r.
m. s. d. , AF2 pAE): EC2: 0. 93 Å, 0. 50 Å, 1. 29 Å, 3. 51; EC3: 0. 92 Å, 0. 60 Å, 1. 07 Å, 4. 59; EC4: 0. 93 Å, 0. 80 Å, 1. 03 Å, 4. 41; EC5: 0. 78 Å, 0. 44 Å, 1. 14 Å, 3. 32. E-H) Implicit modeling of a substrate while scaffolding a retroaldolase active site triad [TYR1051-LYS1083-TYR1180] from PDB: 5AN7. E) The potential used to implicitly model the substrate, which has both a repulsive and attractive field (see Supplementary Methods 4. 4). F) Left: Kernel densities demonstrate that without using the external potential (pink), designs often fall into two failure modes: (1) no pocket, and (2) clashes with the substrate. Right: clashes (substrate < 3 Å of the backbone) & pockets (no clash and > 16 Cα within 3-8 Å of substrate) with and without the potential. Twoproportion z-test: n = 71/51 +/-potential; clashes z = -2.
Note that only 18 of these 20 had wild-type sequences that definitively bound nickel. Note also that for ITC plots, points represent single measurements. Extended Data Fig. 8  | Targeted unconditional and fold-conditioned protein binder design. A-B) The ability to specify where on a target a designed binder should bind is crucial. Specific "hotspot" residues can be input to a fine-tuned RFdiffusion model, and with these inputs, binders almost universally target the correct site. A) IL-7Rα (PDB: 3DI3) has two patches that are optimal for binding, denoted Site 1 and Site 2 here. For each site, 100 designs were generated (without fold-specification). B) Without guidance, designs typically target Site 1 (left bar, gray), with contact defined as Cα-Cα distance between binder and hotspot reside < 10 Å.
Acknowledgements We thank  N. Anand  and  D. Tischer  for helpful discussions, and  I. Kalvet  and  Y. Kipnis  for providing helpful Rosetta scripts. We thank  A. Dosey  for the provision of purified influenza HA protein. We thank  R. Wu ,  J. Mou ,  K. Choi ,  L. Wu  and  D. Blei  for valuable feedback during writing. We thank  I. Haydon  for help with graphics. We also thank  L. Goldschmidt  and  K. VanWormer , respectively, for maintaining the computational and wet laboratory resources at the  Institute for Protein Design. This work was supported by gifts from  Microsoft  (D. J. , M. B. and D. B. ),  Amgen  (J. L. W. ), the  Audacious  Project at the  Institute for Protein Design  (B. L. T. , I. S. , J. Y. , H. E. and D. B. ), the  Washington State General Operating Fund  supporting the  Institute for Protein Design  (P.
Data availability Design structures, AF2 models and experimental measurements are available at https://figshare. com/s/439fdd59488215753bc3. Cryo-EM maps and corresponding atomic models for the Influenza HA binder in Fig. 6d -h have been deposited in the PDB and the Electron Microscopy Data Bank under accession codes 8SK7 and EMDB-40557, respectively. Electron microscopy data collected for the HE0537 oligomer are available at EMDB-40602.
Study design and patients The patients included in this study were participants in a multicenter, prospective clinical trial, ALCAPONE (NCT02281214) (genomic analysis of lung cancer by next-generation sequencing for personalization of treatment). This trial was initiated in November 2014, and aimed to investigate possible associations between NGSidentified genomic biomarkers and the effectiveness of some treatments in patients with advanced lung cancer. The participants were recruited from among patients treated at the Dijon Cancer Center (Centre Georges-Francois Leclerc), the Dijon University Hospital, or the Besançon University Hospital (France). A total of 165 patients were recruited between November 2014 and August 2018. Patients were considered as eligible if they presented a locally advanced inoperable or metastatic lung cancer.
We only included patients with incurable disease. Detailed inclusion and exclusion criteria are available on ClinicalTrials. gov. The clinical data of the population was presented as Supplementary Results. All patients signed an informed consent to participate in the trial. The trial protocol was approved by an institutional review committee and by the relevant French regional ethical committee (Comité de protection des personnes Est). Of note, the information on race/ethnicity of the study participants was not collected due to legal constraints (collecting such data is forbidden in France). However, based on the location of participating hospitals and the patients' family names, we can presume that a vast majority of the patients were of Caucasian origins. Therefore, we used the Non-Finnish European (NFE) population (corresponding to Caucasian population) as a reference.
Exome analysis workflow Raw DNA sequencing data were aligned to the hg19 genome built using the Burrows-Wheeler Aligner (BWA), version 0. 7. 15. Duplicates were marked with Picard version 2. 5. 0. Base quality scores recalibration and variant calling were performed using GATK tools, version 3. 6. Single Nucleotide Variations were annotated using the Variant Studio Illumina software. Filters of candidate variants included the coverage depth of 10X or higher and a variant nucleotide allelic fraction in tumor DNA above 5%. All genomic analyses were performed at the Dijon Cancer Center.
The third factor is perceived behavioral control (PBC), which represents individuals' perception of their ability to perform a given behavior in terms of their capabilities and resources, such as information, time, or money  (Untaru & Ispas, 2014). Using a database of 185 published studies,  Armitage and Conner (2001)  have supported the efficacy of TPB in predicting BI, which in turn enables predicting the actual behavior (AB) driving a person's response in a specific context  (Ajzen & Fishbein, 1980). The TPB has been implemented in tourism research to explore hotel choice  (Han & Kim, 2010) , the intention to adopt environmentally responsible behavior at tourism destinations  (Hu et al. , 2019; Pearce et al. , 2022)  and to visit environmentally friendly tourism destinations  (Ashraf, Hou, Kim, Ahmad, & Ashraf, 2020).
Measurement model Modeling was performed on the proposed variables using SPSS AMOS v26. 0. The model fit to the sample data was deemed acceptable  (Hu & Bentler, 1999). The χ2/df = 1. 666 was less than 5. The values for RMSEA (0. 036) and RMR (0. 052) were both less than 0. 08. The GFI (0. 920), AGFI (0. 904), NFI (0. 947), RFI (0. 940), IFI (0. 978), TLI (0. 975), and CFI (0. 978) values were all larger than 0. 900. Reliability and validity analyses were then performed. Reliability was evaluated using Cronbach's alpha coefficients ranging between 0. 889 and 0. 930, higher than 0. 70  (Fornell & Larker, 1981) , and indicating good internal consistencies among the measured items. Thereafter, all measures were tested for validity by confirmatory factor analysis  (Anderson & Gerbing, 1992). As indicated in Table  3 , the factor loadings estimated for all items are higher than 0.
The eIF4E-eIF4G spots were identified by using Cellprofiler module 'IdentifySpots. m′ followed by correlation with each cell. The localization pattern of the eIF4E-eIF4G spots was calculated with Cellprofiler module 'MeasureLocalizationOfSpots. except one cell line (SKMEL-28). It included A375, Malme3M-S, A2058, Malme3M-R and Mel624. In addition, the two cell lines (A375 and Malme3M-S) sensitive to BRAF inhibition clustered closely together (Fig. 3F ). In the other cluster, we found all the NRAS Q61R mutated melanoma cell lines except one BRAF mutated cell line, SKMEL-28 (BRAF V600E /CDK4 R24C /EGFR P753S /PTEN T167A ). SKMEL-28 is the only BRAF V600E mutated cell line harboring both EGFR and PTEN mutations, which could potentially bypass BRAF signaling to activate the downstream MAPK pathway.
(A) The pipeline of the supervised machine learning. A SVM-based data clean-up process was used to eliminate in particular those cells in mitosis. We constructed a supervised SVM model by integrating the CellClassifier software developed in MATLAB. This cell-cycle related SVM data clean up resulted in a matrix of 1. 2 × 104 cells. The matrix was then subjected to non-supervised clustering in order to determine the number clusters related to the different patterns of eIF4E-eIF4G complex intracellular localization. We integrated different packages of hierarchical classification methods (i. e. NbCluster, hclust, PvClust and WeightedCluster) from R into the SPSS modeler as a node of working flux, which allowed us to calculate the evolution of different indicators for the quality of the classification, and in turn determine the number of final clusters.
The RNAi were used from the same siRNAs as previously reported  [37]. EIF4E-eIF4G proximity ligation assay single cell image analyses. All the images were analyzed with the image analysis software CellProfiler  [29] , based on MATLAB_R2011a version (7. 12. 0. 635 version, 64-bit). Nuclei were segmented using images from Hoechst 33342 staining. The cell outlines were then identified using the propagation and watershed algorithm in Cellprofiler modules. EIF4E-eIF4G PLA spot detection was performed by following steps: all the images were firstly corrected by Cellprofiler module 'illumination correction' and thus rescaled intensities were calculated automatically based on all the analyzed images. For spot detection, we performed a Laplacian-of-Gaussian (LoG) filter to the rescaled images and defined all objects above a certain threshold as spots.
We used one global threshold for all images after ensuring that corrected intensities of individual images were comparable. The specific threshold value was chosen from 40 random images. To avoid dim images and false-positive spots, at least one pixel within a spot was required to have an intensity which would be above the permitted range of the minimal intensity of an image. To further segment the highly crowded spots, intensity-based deblending was done to separate local peaks in each image. This was adapted by a deblending function originated from the astrophysics software SourceExtractor  [3] , with a minor modification in which a linear scale for deblending was used. Analyses of cellular patterns of eIF4E-eIF4G PLA spot localization. Primary spot features were calculated by the 'MasureLocalizationOfSpots.