dataesr/scientific-paragraphs-categorization huggingface dataset:
This can be done using the Images module. Replace the rule criterion "Extension Is the extension of an image file" with "File Does Contain r01", for example. • Illumination correction images produced by an illumination correction pipeline applied to the U2OS image data. See download information at https://github. com/carpenterlab/2022_Cimini_NatureProtocols/blob/main/README. md. • A listing of per-cell image features generated by CellProfiler using the analysis pipeline, available at https://github. com/carpenterlab/2022_Cimini_NatureProtocols/blob/main/CellProfiler_features. csv. • • PhenoVue Dye Diluent A (5x) The product from PerkinElmer (cat no. PING22) contains 80mL of a 5x stock solution. Dilute 5 times in dH2O to create a 1x HBSS solution with 1% BSA.
Handling of QC results, once a visualization tool like the open-source KNIME is set up, takes 20 s per plate (seeBox 2). A substantial time saving can be achieved if you run the feature extraction, segmentation, and QC pipelines on a distributed computing cluster, which massively parallelizes the processing as compared with running it on a single local computer, as well as removing slowdowns in CellProfiler that can accumulate after several hundreds of images are processed in a single run. Steps 45-50, Creation, normalization, and feature reduction of per-well profiles: variable; collation of CSVs (if CellProfiler is run in parallel with ExportToSpreadsheet rather than on a single machine with ExportToDatabase) and aggregation into per-well mean profiles can take 8-16 unattended hours per 384-well plate; plates may be run in parallel.
This was then extended to AlphaFold-Multimer to accurately predict protein complexes  22. Many methods have followed from AlphaFold2 and AlphaFold-Multimer but these remain the de facto gold standard for single domains and complexes  [23] [24] [25]. The AlphaFold2 model can be divided into two main steps: In the first step, the Evoformer module is used to extract evolutionary couplings from alignments of many protein sequences into information-rich embeddings. It then uses these embeddings in the structure module to predict the 3D structure of a given protein sequence. Structure prediction methods specific to a certain class of protein tend to outperform more general methods  26, 27. By using knowledge specific to a type of protein, they can easily predict the conserved regions in that protein allowing greater focus on harder details.
EquiFold does not use an energybased method to refine its predicted structures and hence all of the structures it generates are unphysical. This shows that a refinement step is still necessary to ensure structures generated by deep learningbased methods are realistic (Table  6 ). The results for TCRs and nanobodies are shown in Supplementary Tables  1  and 2 , respectively. Computational cost. The original version of AlphaFold-Multimer is by far the most computationally expensive of the benchmarked methods. It requires over one terabyte of sequence data and takes around three hours to generate one structure when run on five CPUs. Large speed-ups can be obtained by reducing the size of the sequence database, using faster sequence alignment algorithms, or using GPUs  43, 44. Even with these modifications, it takes around thirty minutes on a GPU to generate a single structure.
Discussion We present ImmuneBuilder, a set of three open-source and freely available tools for modelling immune proteins capable of rapidly generating accurate antibody, TCR, and nanobody structures. ImmuneBuilder can produce structures of antibodies and TCRs with accuracy comparable to AlphaFold-Multimer while being over a hundred times faster and without the need for large sequence databases or multiple sequence alignments. ABody-Builder2 is shown to be the most accurate of the antibody-specific tools and the only one to consistently predict structures with correct stereochemistry. The comparison with homology modelling methods, such as ABodyBuilder, shows the benefits that deep learning has brought to the field of antibody structure prediction. However, all methods still struggle to accurately predict the conformation of CDR-H3, suggesting that models capable of predicting multiple conformations may be required to accurately capture this loop.
Methods In the methods section we describe in detail the data and models for creating ABodyBuilder2. An overview of the steps used to predict an antibody structure with ABodyBuilder2 is shown in Fig. 4. Details for NanoBodyBuilder2 and TCRBuilder2 are given in Supplementary Notes 1 and 2. Data. The data used to train, test, and validate ABodyBuilder2 was extracted from SAbDab  11  , a database containing all antibody structures in the PDB  45. The training data was extracted on the 31st of July 2021 resulting in a total of 7084 structures. Filters were used to ensure structures in the training data had both the VH and VL chains, were not missing residues other than at the start and end of the chain, and had a resolution of 3. 5 Å or better. Structures with the same amino acid sequence were kept in the training data to expose the model to antibodies with multiple conformations.
A comparison of the maximum sequence identity to the training set against CDR-H3 RMSD for each Fv in the test set is shown in Supplementary Fig. 3. A full list of PDB codes for structures used in the training, validation and test set is given at https://github. com/oxpig/ImmuneBuilder. Deep learning architecture. The architecture of the deep learning model behind ABodyBuilder2 is an antibody-specific version of the structure module in AlphaFold-Multimer with several modifications. Residues are treated as rigid bodies, each one defined by a 3D point in space and a matrix representing its orientation. The input node features are a one-hot encoded representation of the sequence and the input edge features are relative positional encodings. At the start, all residues are set at the origin with the same orientation.
48  is used. First, the chirality at the carbon alpha centre of each D-stereoisomeric residue is fixed by flipping the hydrogen atom. The structure is then relaxed keeping the flipped hydrogen atoms in place before a final minimisation. Benchmarked methods. We compared ABodyBuilder2 to five other methods: AlphaFold-Multimer, EquiFold, IgFold, the original version of ABodyBuilder and ABlooper. AlphaFold-Multimer was run using the freely available version of code  22. It was run using the weights from version 2. 2 and without the use of templates. The effect of templates on antibody structure prediction is shown in Supplementary Table  3. This generated 25 structures per antibody out of which the top-ranked was selected for the benchmark. The public version of their respective code bases (as of December 15th) was used to generate EquiFold  31  and IgFold 25 models.
As in their paper, Rosetta 42 is used to minimise IgFold models. The original version of ABodyBuilder 32 was run by using the SAbBox Singularity container (https://process. innovation. ox. ac. uk/software/p/20120-a/sabboxsingularity-platform-academic-use/1) from July 2022 excluding all templates with a sequence identity of 99% or higher. ABlooper 33 (version 1. 1. 2) was run to remodel the CDR loops from the ABodyBuilder predictions. Structures generated by all methods were numbered using ANARCI  49.
Background In recent years, genome-wide association studies (GWAS) have identified genetic variants associated to multiple traits. The phenomenon where one genetic loci affects multiple different phenotypes is called pleiotropy  [1]. A comprehensive overview of the genetic architecture in complex traits from GWAS datasets reported that 31% of single nucleotide polymorphisms (SNPs) and 63% of genes were pleiotropic  [2]. Identification of these pleiotropic effects may help to understand the shared etiology among complex diseases by highlighting common biological pathway. As a consequence of the active interest in pleiotropy, there are now a number of statistical tools which identify pleiotropic signal  [3] [4] [5] [6] [7]. See  [8]  for a survey. Amongst these methods one of the most popular for practitioners is a subset based meta-analysis ASSET  [9].
These methods were later extended by Liu et al. [14]  who made use of extended mixture models to allow for the incorporation of gene set analysis. In this article, we propose novel methods which model pleiotropy for genomics data in the case of independent datasets. Our methods are developed to model pleiotropic correlation amongst jointly analysed traits and account for the gene structure information contained in the data. Integrating additional information such as gene pathway knowledge offers the potential to improve statistical efficiency. Our statistical approach exploits both gene (or pathway) and pleiotropy knowledge to increase the statistical power of identifying risk variants shared by multiple diseases. We conduct simulation studies to evaluate the performance of our method. Our method can be motivated in a multi-task framework  [15].
In our context, each genetic dataset would correspond to a different learning task. A common approach to this setting is to assume that only a few common important genes are shared across the K studies. In particular, we make note of Lounici et al. [16]  who consider using the Group Lasso as a candidate estimation method for multitask estimation in linear regression models. These ideas have since been extended by Wang et al. [17]  who propose a penalised likelihood approach for multi-task regression which can incorporate group structure. Their method is proposed for the case when the response is a set of continuous responses. We extend their penalisation methods to account for a binary response variable. We are motivated by an application of our methods to the analysis of pleiotropy between thyroid and breast cancers.
Modelling sparse and grouped associations in many independent datasets Suppose we have data from K independent datasets, D = D 1 ∪D 2 ∪• • •∪D K , where D k = ({y 1k , x 1k },. , {y n k k , x n k k }) and dataset contain n 1 ,. , n K samples respectively. The response variable y ik ∈ {0, 1} is the binary phenotype of the ith individual of the kth study and x ik ∈ R p is the vector with corresponding p variables of the ith individual of the kth study. These data are assumed to come from a logistic regression model where p (Y ik = y ik |X ik = x ik ) = exp y ik x T ik β •k 1 + exp x T ik β •k for k = 1,. , K, where β •k ∈ R p denotes the regression coefficients for the kth study. To simplify further notation, let β j• ∈ R K , j = 1,. , p denote the vector of the K regression coefficients corresponding to the jth SNP over the K datasets.
We let β jk denote the regression coefficient for the jth SNP of the kth study. We assume that the set of SNPs can be partitioned into G groups where each SNP belongs to a single unique group. Let π g , g = 1,. , G denote the set of SNPs contained in the gth group and n g be the number of SNPs in group g. Finally, we let the matrix of all regression coefficients be denoted by B = (β •1 ,. , β •K ). Since the K multiple studies were observed independently, the negative log likelihood for the combined datasets has the generic expression: (B; D) = - K k=1 n k i=1 y i x T ik β •k -log(1 + e x T ik β •k ) (1) where (B; D) denotes the negative log likelihood for the observed data D.
Sparse group multi-Task method Our Sparse Group Multi-Task (SGMT) approach is based on penalised likelihood maximisation. Using the likelihood form for independent datasets (1), we propose the penalised likelihood estimate B = argmin B∈R p×K (B; D) + λ(1 -α) B G2,1 + λα B l2,1 where B G2,1 = G g=1 √ n g i∈πg K k=1 β 2 ik and B l2,1 = p i=1 β i• 2 = p i=1 K k=1 β 2 ik ( 2 ) where λ ≥ 0 and α ∈[ 0, 1] are regularisation parameters weighting a G 2,1 -norm penalty B G 2,1 and l 2,1norm penalty B l 2,1. The parameter λ controls an overall amount of penalisation, while α determines how much penalisation is used for each penalty. The G 2,1 -norm  [19]  fixes the group structure across studies and encourage sparsity at group-level. As important groups may contain irrelevant SNPs we desire a method which is able to select variables within a group.
Commonly in GWAS data we are concerned with controlling the false discovery rate (FDR). One way to approach controlling this would be to specify a cut-off for the minimum probability of inclusion for each variable. In our simulation study we consider a strict control, only selecting variables that are included on every bootstrap. This conservative strategy would often be too drastic and for or application section we adopt another approach. This is to report the variables that had selection probabilities at least as high as the selection probabilities of the variables included on the full data fit. We state both the variables and their bootstrapped selection rates. More details on the bootstrapping procedures are given in the Results and Applications sections. An example demonstrating the Bootstrapping approaches is given in the supplementary material and can be reproduced from github.
Simulation design To assess the correctness and efficiency of our methods, we run simulations and compare the results with the well known frequentist approaches ASSET and GPA for detecting pleiotropic signal  [9, 13]. In order to show the contribution of leveraging pleiotropy, we also run and compare the results of our novel approaches with the single-task group lasso (SGST), in which each trait is treated individually. The simulations and implementations of all methods have been carried out in R. At each simulation, K = 2 datasets are generated. We look at monitoring the efficiency of our methods for detecting effects across the multiple studies. In particular, we considered the effect of grouping information on the efficiency of the methods. The simulations were set to range from a simulation setting in which the grouping information was not as useful, the groups were almost entirely set to zero, to one where grouping information entirely determined the selected variables, i.
Study population We used individual-level data from CECILE study  [32] , a French population-based case-control study on breast cancer (1,125 cases and 1,172 controls) and from the french case-control studies included in the EPITHYR consortium on thyroid cancer (CATHY, Young-thyr, and E3N studies totalling 1,129 women cases and 1,174 women controls)  [33]. Only women of European ancestry were kept for the analyses. Participants from CECILE study were genotyped using a customized microarray including variants from 28 candidate pathways (648 genes) selected from KEGG database and from a literature review. A total of 8,716 SNPs were selected to capture SNPs within 5 kb of each gene (pairwise approach with r2 > 0. 8) with a minimum minor allele frequency (MAF) of 0. 05 in the Caucasian population (CEU) genotyped by the HapMap Project (Data Release 21/Phase II, NCBI Build 36.
1, assembly dbSNPb126)  [32]. In EPITHYR, all subjects were genotyped using the Infinium OncoArray-500K BeadChip (Illumina). This array includes a genome-wide backbone of about 250,000 tag SNPs designed such that the large majority of common variants could be accurately imputed. Additional SNPs included dense coverage across known loci associated with common cancers, including breast cancer but not thyroid cancer. We added 13,759 custom markers of possible interest for thyroid cancer to the design of the chip  [33]. Imputation of data from EPITHYR were then performed using the 1000 Genomes Project dataset as the reference panel (release of October 2014, version 3). Rare SNPs (MAF < 0. 01) and palindromic SNPs were excluded. After quality controls (QC), we retained 6,677 SNPs available for both cancers.
As our approaches do not deal with overlapping groups, 10 non-overlapping candidate pathway were selected and only the SNPs related to those pathways were kept in the final datasets. Within each genes, SNPs were pruned for high pairwise correlation (r 2 > 0. 98). Then, only SNPs belonging to non overlapping groups (genes and pathways) were selected. At the end of the QC, the two datasets included the same panel of 3,766 SNPs within 331 genes and 10 pathways (see Table  1 ).
Statistical analysis We applied the three proposed methods to the investigation of pleiotropy between breast and thyroid cancers. The GMT and SGMT methods were both applied twice in order to consider both gene and pathway as different group structures. First, the tuning parameters has been calibrated using 5-fold cross-validation procedures. For SMT and GMT, we then performed the analysis using the values of λ parameter minimising the mean of the binomial deviance over 5 repetitions. For SGMT, the best couple of tuning parameter (α, λ) has been calibrated using 5 repetitions of 5-fold cross-validation. Once the methods were fit to the data, we explored the stability of the penalised methods using a bootstrap sampling strategy. We evaluated the methods on 10,000 bootstrap samples of the data using the tuning parameters from the original fit to the full dataset.
We evaluated the frequency of selected SNPs (or groups for GMT) on the 10,000 bootstrap samples. Finally, we selected only the variables with a higher bootstrap selection rate than the non-selected variables from the original fit to the full dataset. For details see the supplementary material. We also analysed these datasets using ASSET for an empirical comparison of the methods. We first performed GWAS analyses for breast and thyroid cancers separately in order to get summary statistics. As ASSET is based on p-values, we applied a FDR to correct for multiple testing. As we only have interest in identification of pleiotropic effects, we only considered SNPs detected in both datasets. Furthermore, we compared the results of our proposed methods with previously published results on the same data using Bayesian meta-analysis models called GCP-Bayes at gene-level  [34]  which are based on summary statistics.
Simulation results Table  2  shows the estimated variable selection performance of the regression coefficients from the different penalised multi-task methods and the competitor ASSET. Selection performance was measured by the number of correctly selected variables, the true positive rate (TPR), the number of correctly non-selected zero variables, true negative rate (TNR) and the Mathew's correlation coefficient (MCC). For ASSET, variables were said to be selected if they had a false discovery adjusted p-value lower than 0. 05 at a variable-level. Groups were selected if the minimum adjusted p-value at a variable-level within the group was significant. We applied a strict bootstrapping approach for detecting effects using the penalised approaches. Specifically, for each simulated dataset we re-sampled the data 200 times and re-ran each of the penalised methods (SMT, GMT, SGMT and SGST) with their one-standard-error cross-validated λ and α values.
A consequence of using this strict FDR and bootstrapping procedure is that the true negative rate is almost consistently at 100% with low standard error. The MCC and TPR in comparison have higher variability and differ more amongst the methods. We note also that the variability of the methods appears to decrease for the larger simulations. This is because the smaller simulation settings have a small number of true variables and consequently there is more variance in the estimate of the true positive ratio. For example Sim 1 has only 2 active variables so there are 3 possible TPR values for any dataset. The simulations results in Table  2  show clearly that single-task method is not efficient at detecting pleiotropic effects. For every scenario the method struggles to find signal in the data.
We have applied our methods to the analysis of two datasets on breast and thyroid cancers. Future work could consider extending these methods to allow for groups with overlap with extensions to the ADMM optimisation or alternative efficient methods  [38]. Other extensions could include generalising the approach for the joint analysis of multiple generalised likelihoods (e. g. logistic, linear, Poisson, etc). Further investigation of the choice of weights in the adaptive component of the penalisation could also be of theoretical and practical interest. Another future avenue of research would be development of p-value calculations and more technical FDR control measures in line with the theoretical development of Lockhart et al. [39]  or Candés et al. [40]. In conclusion, the proposed multi-task regression methods were seen to be more powerful than methods based on summary statistics to detect new pleiotropic effects in complex diseases, and are computationally feasible.
These methods allow us to take into prior knowledge in the analysis of the genetic data as the biological structures of genes or pathways, and hence it allow to select important risk variants or group structures with more biological meaning. These methods are likely to be of interest for other application to detect non-zero effects of possible different directions in structured data. The methods have been implemented in a user-friendly R statistical package called "SGMT", available at https://github. com/mattsutton/SGMT.
Acknowledgements The authors acknowledge Pascal Guénel (PI of CECILE and CATHY studies) and  Florent de Vathaire  ( PI of Young-thyr study ) for providing the datasets for the application, and  Alexandre Lefranc  for his help on the package. The authors acknowledge also the calculus center  MCIA  ( Mésocentre de Calcul Intensif Aquitain ) for providing its facilities.
Greenland ice sheet mass loss continues to accelerate as global temperatures increase. The surface albedo of the ice sheet determines the amount of absorbed solar energy, which is a key factor in driving surface snow and ice melting. Satellite retrieved albedo allows us to compare and optimise modelled albedo over the entirety of the ice sheet. We optimise the parameters of the albedo scheme in the ORCHIDEE land surface model for three random years taken over the 2000-2017 period and validate over the remaining years. In particular, we want to improve the albedo at the edges of the ice sheet since they correspond to ablation areas and show the greatest variations in runoff and surface mass balance. By giving a larger weight to points at the ice sheet's edge, we improve the model-data fit by reducing the RMSD by over 25% for the whole ice sheet for the summer months.
Other datasets have also been assimilated to improve snow estimation, including snow cover fraction estimates from optical sensors (e. g. ,  Toure et al. , 2018; Xue et al. , 2019)  and measured ice surface temperatures (e. g. ,  Navari et al. , 2018). There have also been several studies assimilating joint datasets. For example, MODIS-based snow cover fraction and albedo have been assimilated in the Common Land Model LSM  (Xu and Shu, 2014)  and the Noah LSM  Kumar et al. (2020). All these studies use DA for state estimation, i. e. , updating the model state whilst keeping the model parameters fixed. The techniques used range from relatively simple methods like direct insertion to more advanced statistical techniques like the ensemble Kalman filter and particle filters. Examples of DA used for parameter estimation, i.
MODIS In this study, we used satellite-derived albedo from the NASA (National Aeronautics and Space Administration) MODIS (Moderate-Resolution Imaging Spectroradiometer) MOD10A1 product  (Hall et al. , 1995). The latest collection, collection 6, is used  (Riggs et al. , 2015; Hall and Riggs, 2016). These data have been de-noised, gap-filled and calibrated into a daily 5km grid covering Greenland after  Box et al. (2017)  for the years 2000-2017. When MODIS retrievals are inaccurate due to inadequate solar illumination during the winter months (January, February, November, and December), April values are swapped in. In this study, we further aggregated these data to the resolution of the ORCHIDEE outputs, imposed by the meteorological forcing files (20 km).