dataesr/scientific-paragraphs-categorization huggingface dataset:
fMRI data For our investigations, we use publicly available data from  [10]. This dataset consists of fMRI data collected on five healthy participants viewing images from a subset of categories available in ImageNet. Participants performed a one-back test in the scanner in which they had to press a button when the same image was repeated on two consecutive presentations. The data were collected on 1200 training images that were presented once, and 50 test images presented 35 times each. For our experiments, we restrict ourselves to the subset of test images since the higher number of repetitions provides a more robust estimate of the multi-voxel representation of each image. After downloading the raw fMRI data, we preprocessed them with a standard pipeline using SPM12  [12] : slice-time correction, realignment, and coregistration to the T1W (T1-Weighted) anatomical images.
Models We include a variety of models in our analysis to facilitate interpretation and discovery of underlying trends in different classes of models. All the included models are publicly available, and possess a ResNet50 backbone to minimize architectural differences. For CLIP, we used the visual CLIP-RN50 backbone (called CLIP hereafter), which was jointly trained along with a linguistic head (called CLIP-L hereafter) on a contrastive learning task on 400M image-caption pairs  [1]. Additionally we also considered visual features from TSM  [15] , another multimodal model that is trained with a contrastive objective on the HowTo100M dataset  [16] , in a task that comprises three modalities (video, text, and audio). The impact of contrastive learning objectives on the features of these models can be compared to VirTex and ICMLM, multimodal networks trained with different objectives.
For Virtex, the visual backbone is trained on an image captioning task  [17] , while for ICMLM, the visual features are trained on a text-unmasking task  [18]. Both VirTex and ICMLM are trained on MS-COCO  [19] , a much smaller dataset compared to those used for CLIP or TSM. To tease apart the effect of multimodal training, we also included visual-only models in our comparisons. Since dataset size has been suggested to affect the quality of representations learned by a network, we considered two visual-only models trained on different datasets. We used the standard ResNet50 model (the control visual model) trained on ImageNet-1K, as well as BiT-M, a ResNet50 backbone trained on the significantly larger ImageNet-21K dataset  [20]. We also included adversarially robust models (AR-L2, AR-L4, AR-L8) from  [21]  in our comparisons.
These models are trained to be robust to minute perturbations to the input images by explicitly incorporating such perturbed (adversarial) images  [22]  in the training dataset. These models have been observed to possess more human-like features compared to standard feedforward versions  [23] , making them particularly relevant to our analysis. Unlike human observers who rely on shapes, standard ImageNet models are strongly biased by the texture of images  [24]. Therefore, Geirhos et al. [24]  designed a stylized version of ImageNet to train models that have a stronger bias towards shape than texture. To assess whether representations optimized for human-like biases are better at explaining brain activity in MTL regions, we included three StylizedImageNet models in our comparisons: Finally, apart from visual and multimodal models, we also included language models: GPT-2  [25] , BERT  [26] , as well as CLIP-L.
In particular, Xu and Vaziri-Pashkam  [28]  casted doubt on the utility of DNNs for explaining higher regions in the brain. Our findings provide a potential way forward to address this limitation: building models that explain higher regions in the brain might require using datasets spanning different modalities. This can be further combined with bio-plausible architectural changes to the DNNs. For example, it would be interesting to investigate the effects of training a bio-inspired recurrent neural network  [29]  using multimodal objectives. Combining these architecture-and objective-based approaches could potentially have synergistic effects in learning human-like representations. We hope the findings in this work further contribute to the efforts in bridging the gap between machine learning and neuroscience.
Acknowledgments and Disclosure of Funding RV is funded by  ANR  grant  OSCI-DEEP  ( ANR-19-NEUC-0004 ). LR and RV are both funded by an  ANITI (Artificial and Natural Intelligence Toulouse Institute) Research Chair (ANR  grant  ANR-19-PI3A-0004 ), as well as  ANR  grant  AI-REPS  ( ANR-18-CE37-0007-01 ).
As part of a co-clinical trial of DIPG patients from the UK enrolled in BIOMEDE, an adaptive, international, phase II clinical trial in newly diagnosed DIPG (NCT02233049), we have generated patient-derived in vitro and in vivo models subjected to integrated drug screening and molecular profiling. We identify specific genetic dependencies associated with response to trametinib in vitro, explore mechanisms of acquired resistance which emerge to single agent targeted therapy which emerge, and present rational drug combinations which may circumvent this in future clinical trials. Cancer Research. on November 30, 2021. © 2021 American Association for cancerdiscovery. aacrjournals. org Downloaded from Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
RESULTS MAPK alterations confer in vitro sensitivity to trametinib in DIPG models from a co-clinical trial BIOMEDE (NCT02233049) was a phase II, biopsy-driven clinical trial in DIPG patients with randomisation of stratification between dasatinib, erlotinib and everolimus. From UK patients, we undertook to generate patient-derived in vitro and in vivo models from biopsy material excess to trial inclusion. From the first 12 cases, exome sequencing was performed on the biopsy specimen itself, and panel sequencing performed on the in vitro cultures established under stem cell conditions in 2D (on a laminin matrix) and 3D (as neurospheres), as well as tumours grown as orthotopic xenografts in mice (Figure  1A ). Seven cases presented with H3F3A (now H3-3A) K27M mutations, one had HIST2H3C (now H3C14) K27M, and another presented EZHIP overexpression by RNA sequencing, recently described as phenocopying the H3K27M mutation-driven loss of H3K27me3  (43).
DISCUSSION In the setting of a co-clinical trial of prospectively-established DIPG patient-derived models, we show the identification of biomarkers of response to trametinib, a selective reversible inhibitor of MEK1/2 that binds to the allosteric pocket of MEK. Specifically, these involve multiple nodes of the MAPK signalling pathway, and include PIK3R1_N564D, NF1_I1824S and BRAF_G469V. These results suggest a possible rationale for the use of trametinib in DIPG, such as the ongoing phase II clinical trial called TRAM-01 (NCT03363217), which is exploring the use of trametinib in paediatric gliomas harbouring MAPK alterations independently of the tumour entity  (46). PIK3R1_N564D is an oncogenic hotspot mutation known to promote cell survival in vitro and oncogenesis in vivo  (47) , and lies within the regulatory subunit of PI3-kinase resulting in lossof-function, predicted to destabilise protein interaction which may impact tumour suppressive function (FATHMM pathogenic score of 0.
99)  (48, 49). Such PIK3R1 oncogenic mutations have been shown to activate the MAPK pathway and exhibited sensitivity to MAPK inhibitors  (50). NF1_I1824S lies in the neurofibromin chain and the lipid binding region, which may affect the protein folding and the tumour suppressor gene function  (48) ; it has also been reported in a patient with neurofibromatosis type 1  (51) , and is absent from population databases. The efficacy of MEK inhibitors has previously been shown in NF1-deficient glioblastoma cell lines  (52) , in addition to clinical benefit in refractory neurofibromatosis-associated glioma harbouring NF1 mutation  (53) (54) (55). By stochastic selection, an imbalance of variant allele frequency between 3D and 2D cultures from the same patient was observed in ICR-B181 (PIK3R1_N564D) and ICR-B184 (NF1_I1824S).
Single nucleotide variants were called using the Genome Analysis Tool Kit v3. 4  fragmentation and random primed cDNA synthesis (strand specific). Adapter ligation and adapter specific PCR amplification was performed before sequencing on Illumina sequencers (Hiseq or Novaseq) using 150 bp paired-end reads chemistry according to the manufacturer's instructions. RNAseq data was aligned with STAR and summarized as gene level fragments per kilobase per million reads sequenced using BEDTools and HTSeq. Following rlog transformation and normalization, differential expression was assigned with DESeq. 2. Fusion transcripts were detected using chimerscan ver 0. 4. 5a filtered to remove common false positives. Gene set enrichment analysis was carried out using the R package fastGSEA (fGSEA) based upon curated canonical pathways (MsigDB, Broad).
Methylation A total of 50-500 ng of DNA was bisulphite-modified using the EZ DNA Methylation-Direct kit (Zymo, D5006), loaded onto the Illumina Infinium MethylationEPIC BeadChip and the array intensities were read on the Illumina iScan system at the University College London Genomics Centre, according the manufacturer's instructions. Methylation data from the Illumina Infinium HumanMethylation850 BeadChip was pre-processed using the minfi package in R (v11b4). DNA copy number was recovered from combined intensities using the conumee package. The Heidelberg brain tumour classifier (67) (molecularneuropathology. org) was used to assign a calibrated score to each case, associating it with one of the 91 tumour entities which feature within the current classifier. Clustering of beta values from methylation arrays was performed based upon correlation distance using a ward algorithm.
DNA copy number was derived from combined log2 intensity data based upon an internal median processed using the R packages minfi and conumee to call copy number in 15,431 bins across the genome. ddPCR Custom TaqMan-based quantitative PCR genotyping assays (Applied Biosystem, Thermo Scientific) were designed to specifically detect PIK3R1-N564D and MEK1-K57N, MEK1-I141S and MEK2-I115N mutations (Supplementary Table  S2 ). ddPCR was performed by using the Bio-Rad automated droplet generator (BioRad, QX200 AutoDG), and the Bio-Rad QX200 droplet reader, based on the manufacturer's guidelines. Results were analysed using Bio-Rad QuantaSoft Analysis Pro software. At least two single FAM-positive droplets were required to call a sample positive.
The compounds were diluted in DMSO to a final concentration of 10 mM. Drug plates were prepared using the acoustic liquid handler Echo 550 (Labcyte). Each plate included six compounds at eight different concentrations as well as a cytotoxic chemotherapeutic (camptothecin) as positive control, as well as DMSO as a negative control. Drug combinations were assessed by adding one compound in rows and another in columns with serial dilutions resulting in a 6 x 10 dose matrix using different fold dilutions manually prepared. The stand-alone web-application SynergyFinder (https://synergyfinder. fimm. fi) was used for interactive analysis and visualization of multi-drug combination profiling data following the Bliss independence model  (68, 69). or by exposing the cells to a constant concentration of trametinib of 0.
The arrays were rinsed a further 3 times in wash buffer then covered by Streptavidin-HRP in 1X Array Buffer and incubated at room temperature for 30 minutes. They were visualised using the LI-COR Imaging System and analysed using the Quick Spots Image Analysis Software (R&D Systems). Briefly, the pixel density of each spot on the array was measured, and then the average signal of the pair of duplicate spots representing each p-RTK was calculated. Finally, the averaged background signal from each RTK spot was subtracted. For treatment with trametinib or dasatinib, cells were incubated in complete media with vehicle (DMSO) or increasing concentrations of drug (0. 01, 0. 1, 1, 10 µM) and protein was collected at 30min post-treatment. Samples were lysed by using lysis buffer (CST) containing phosphatase inhibitor cocktail (Sigma, Poole, UK) and protease inhibitor cocktail (Roche Diagnostics, Burgess Hill, UK).
All raw files were processed in Proteome Discoverer 2. 3 (phospho-proteome) or 2. 4 (full proteome) (Thermo Fisher) using the SequestHT search engine. Spectra were searched against reviewed Uniprot Homo sapiens entries (November 2019) and an in-house contaminate database. Search parameters for phospho-proteome were: trypsin with up to 2 miss-cleavage sites, mass tolerances at 20 ppm for Precursor, and 0. 02 Da for fragment ions, dynamic modifications of Deamidated (N, Q), Oxidation (M) and Phospho (S, T, Y), and static modifications of Carbamidomethyl and TMT6plex (peptide N-terminus and K). For full proteome, fragment ion tolerance was 0. 5 Da, dynamic modification of Oxidation (M) and acetylation (Protein N-terminus), and static modifications of Carbamidomethyl and TMT6plex (peptide N-terminus and K).
phenome Archive (ebi. ac. uk/ega) with accession number EGAS00001004495 (sequencing) or ArrayExpress (ebi. ac. uk/arrayexpress/) with accession number E-MTAB-9282 (methylation Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on November 4, 2021; DOI: 10. 1158/2159-8290. CD-20-0930 arrays). Proteomic data has been deposited in the Proteomics Identifications Database (PRIDE, ebi. ac. uk/pride) with accession number PXD019701. Curated gene-level mutation, copy number, and expression data are provided as part of the pediatric-specific implementation of the cBioPortal genomic data visualisation portal (pedcbioportal. org) Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
ACKNOWLEDGEMENTS This work was supported by  Christopher's Smile, Abbie's Army, IslaStones , the  CRIS Cancer Foundation, Children with Cancer UK and Cancer Research UK. The BIOMEDE trial was carried out as a collaboration between the  European Innovative Therapies for Children with Cancer (ITCC) consortium , the  SIOPE Brain Tumour Group  and the  Australian Children's Cancer Trials Group (ACCT) , and was supported by  Imagine for Margo, Cancer Research UK  and the  Brain Tumour Charity. FC is partly funded by The  Giant Pledge via the Royal Marsden Cancer Charity. TSJ receives funding from  Great Ormond Street Children's Charity, Children with Cancer UK, Cancer Research UK ,  NIHR  and the  Olivia Hodson Fund. We acknowledge NHS funding to the  NIHR Biomedical Research Centre at The Royal Marsden  and the  ICR, Experimental Cancer Medicines Centre (ECMC)  funding to the  Paediatric ECMCs at The Royal Marsden and Great Ormond Street Hospitals , and  Royal Marsden Cancer Charity (RMCC)  funding to the  Paediatric Oncology Drug Development Team at The Royal Marsden/ICR.
It is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users. It provides the implementation of most of the international standard climate indices such as ECAD, ETCCDI, ET-SCI, including the correct methodology for calculating percentile indices using the bootstrapping method. It has been validated against R. Climdex as well (https://cran. r-project. org/web/packages/climdex. pcic/index. html ). The new 5. x version of icclim is now based on functions from the xclim python library, which was inspired by earlier.
versions of icclim, but using xarray and dask for data access and processing. icclim is also a candidate as the software to calculate climate indices for the C3S toolbox (https://cds. climate. copernicus. eu/cdsapp#!/toolbox ). An example of a complex analysis tool used in climate research and adaptation studies is a tool to follow storm tracks. In the context of climate change, it is important to know if storm tracks will change in the future, in both their frequency and intensity. Storms can cause significant societal impacts, hence it is important to assess future patterns. These tools are integrated in the IS-ENES C4I 2. 0 platform (https://climate4impact. eu/ ), using a Jupyter notebook collection in a SWIRRL environment (Software for Interactive Reproducible Research Labs https://gitlab.
Change h"ps://rebrand. ly/icclim The IS-ENES3 project has received funding from the European Union's Horizon 2020 research and innovaHon programme under grant agreement N°824084 : https://is. enes. org/ icclim v5. 0. 0RC1: https://github. com/cerfacs-globc/icclim (pip install icclim) climate4impact 2. 0 beta: https://dev. climate4impact. eu/ IN45G-0512 2020 Hurricane Delta causes damage to Louisiana's Gulf Coast 2021 Germany Er<stadt, southwest of Cologne h@ps://dev. climate4impact. eu -Intra-period extreme temperature range [°C] -ETR -Warm days (days with mean temperature > 90th percentile of daily mean temperature) -TG90p -Summer days (days with max temperature > 25 °C) optimized ➤Fully compliant to CF and Standards ➤Validated against climpact & xclim ➤Easy install: pip install icclim ➤Implement the proper percentile indices calculations when calculation period overlaps reference period: bootstrapping method From Users Surveys ➤Difficult to deal with the large number of climate simulation datasets ➤Lost in which climate model, experiments to use ➤Many climate indices are complex to calculate ➤Cannot reproduce results easily ➤Urgent needs of impact assessments ➤Identify mitigation solutions ➤Extreme events attribution ➤Multiple domains: infrastructures, urban, agriculture, transportation, etc.
➤Flexible tools needed for very diverse users ➤Using xclim climate indices functions ➤xclim functions are using xarray, dask, pandas and numpy: optimized and parallel ➤icclim 5 implements a specific API very similar to v4 ➤Extended capabilities: userdefined indices, user-specific thresholds, etc. ➤Expand testing of 5. 0. 0RC versions to more users in order to release 5. 0github. com/clix-meta/clix-meta ➤Release support tools: testing suite ➤Provide more Jupyter Notebooks to include in C4I https://gitlab. com/is-enes-cdi-c4i/notebooks ➤Compound Events.
Single-particle tracking data of MET The data used within this study, together with experimental details, were published previously  (Harwardt et al. , 2017). They include SPT data on the mobility of the membrane-bound MET receptor in a total of 117 HeLa cells. Of these, 57 were treated with InlB and 60 cells were untreated. The raw data are single-molecule movies with a length of 1000 frames recorded with an exposure time of 20 ms per frame. For InlB-treated cells, an average of 9168 ± 5435 trajectories per cell (mean ± SD) were recorded with a trajectory length range of  [5, 9, 19]  (first, second, third quartile) frames and 4143 ± 2807 trajectories (mean ± SD) with a trajectory length range of  [5, 9, 20]  (first, second, third quartile) frames were recorded for untreated cells. The higher degree of receptor labeling with InlB may be due to the high affinity of InlB for the MET receptor  (Dietz et al.
Training hidden Markov models on single-particle tracking data HMMs were trained on MET receptor SPT data with the aim of reducing the information to the optimized HMM parameter set Θ = (π, A, B). The optimal model with N mobility modes was determined by assessing the HMM quality using the Bayesian information criterion (BIC) and likelihood ratio tests. Subsequently, the parameter space was expanded to include mode occupancy (ω) and the HMM mobility parameter (B) was replaced by its associated diffusion coefficient (D) to increase the physiological interpretability of the parameter data. Cells with abnormal parameters were identified using isolation forests and density-based spatial clustering of applications with noise (DBSCAN) analyses and removed from the parameter data.
Anomaly detection Nine cells had an anomalous set of parameters (π opt , ω opt , A opt , D opt ) identified by isolation forests and DBSCAN-based analysis (Supplemental Figure  4 ). These abnormalities, of which six were untreated and three were InlB-treated cells, were excluded from further analysis, resulting in a balanced data set with 54 cells from each group. The resulting dataset is available as a text file in the Supplemental Material of the article ("met_diffusion_hmm_parameters. csv").
Furthermore, the diffusion coefficients of the immobile modes of both groups, which were previously defined as Internalin B-treated and untreated cells can be classified based on MET mobility with a balance accuracy >90% = = D a All classifiers trained with this dataset were able to distinguish InlBtreated cells from untreated cells with a balanced accuracy of >93% (Table  2 ). Nevertheless, the random forest classifier did solve the binary classification task best in all of the measured metrics (ROC-AUC, balanced accuracy, F1 score, precision score, and recall score) and was therefore chosen for further investigation of the differences  between the two groups. If all performance measurement metrics were ranked equally, then the XGBoost performed second best, followed by the ANN and finally the CART (Table  2 ).
Data acquisition was performed using an N-STORM microscope (Nikon, Duesseldorf, Germany) equipped with a 647-nm laser, an oil immersion objective with a numerical aperture of NA = 1. 49 (100 × Apo TIRF oil), and an electron-multiplying charge-coupled device camera (DU-897U-CS0-BV; Andor Technology, Belfast, United Kingdom). Receptors labeled with ATTO 647N were excited by illuminating the sample with an evanescent field at an intensity of 0. 1 kW/ cm 2 in total internal reflection mode. The fluorescence signal was collected by the same objective, cleaned by appropriate optical filters, and detected by the camera. The image size was set to 256 × 256 pixels with a pixel size of 0. 158 μm. The camera's electron-multiplying gain was set to 200 and the integration time per frame was 20 ms. For each cell, a movie of 1000 frames was recorded without temporal delay between frames.
Computational setup All experiments were performed on an Intel Xeon E5-2620 v3 (12) at 3. 200 GHz equipped with 128 GB of memory and a GeForce GTX 750 Ti GPU running Linux (openSUSE Leap 15. 2). Machine learning on the GPU was enabled using the CUDA library (v. 10. 2). Data-science pipelines are implemented in the python language (python 3. 7. 7) using the libraries matplotlib  (Hunter, 2007) (v. 3. 3. 1) , numpy  (Harris et al. , 2020 ) (v. 1. 19. 1), pandas (McKinney et al. , 2010)   (v. 1. 1. 1), seaborn  (Waskom, 2021) (v. 0. 11. 1) , scikit-learn  (Pedregosa et al. , 2011) (v. 0. 23. 2) , SciPy  (Virtanen et al. , 2020) (v. 1. 5. 2), tensorflow  (Abadi et al. , 2016)  (v. 2. 2. 0), and xgboost  (Chen and Guestrin, 2016) (v. 1. 3. 3). Experimental development of data science pipelines was performed using the Spyder-IDE (v.
Analysis of single-molecule localization microscopy data Single-molecule localization. The positions of isolated MET ligand complexes were determined by modeling the photon distribution emitted from the ligand bound fluorescent probe with a normal distribution using the ThunderSTORM  (Ovesny et al. , 2014)  plugin of the free image processing software suite ImageJ  (Schindelin et al. , 2015). Model parameter optimization was performed using maximum likelihood estimation. The option "Multi-emitter fitting analysis" was enabled with the parameter "maximum numbers of molecules per fitting region" set to 3. The parameter "limit intensity range" was set to the 2σ interval of the photon distribution in log-space of localizations found with "multi-emitter fitting analysis" disabled. The "remove duplicates filter" was applied.