dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions for NNRTIs and mutants The NNRTI set represented a different data set compared to the GPCR ligand dataset evaluated above, as it consists of a number of highly similar sequences and compounds and, hence, resembles a typical data set one might encounter in lead optimization. It is concluded that in these cases the feature-based descriptor set performs very well; however its good performance can also be caused by our binding site definition. Therefore this type of descriptor set should be included as a possible candidate when working on a data set consisting of several highly related targets. However, as shown in the other benchmarks this is not an observation that can be generalized to all datasets. The final ranking for this dataset is as follows: The best performing feature set was ProtFP (Feature), followed by, ProtFP (PCA3), ProtFP (PCA5) (both of which exhibit similar performance), MS-WHIM, ProtFP (Feature) and Z-Scales (3).
LOSO validation on PIs The LOSO experiment was performed slightly different on this dataset. Given the very large size and the computational infeasibility to repeat this for 16 descriptor sets, not a single target was left out but 10% at a time. From earlier work it was known that this can indeed be done and that the results are still comparable; the key issue is to leave out a full section of the target space with all bioactivity annotations. [7]  Still, this corresponds roughly to leaving out a single target out of 14 in the NNRTI set (+/-10% of target variation). The results of this experiment are shown in Figure  10. Best performing was now the Z-Scales (3) set (RMSE 0. 329 and R 0 2 0. 800), followed by ProtFP (Feature) combined with Z-Scales (3) (RMSE 0. 330 and R 0 2 0. 799), and the Z-Scales (  5 ) set (RMSE 0.
Final descriptor set ranking The final ranking of the individual descriptor sets is given in Table  3  and Figure  12. The table included the individual ranks of all descriptor set in each experiment (on a scale of 1 to 16) and a final overall ranking (which is calculated as the median of the individual rankings). Also included is the median average deviation of the median (MAD) of the median rank in this representation (Figure  12 ). In the  discussion of the following it should be kept in mind that differences in performance were on most datasets rather small, so that differences in ranks usually translate to small quantitative differences in performance. The best performing descriptor sets overall are, ProtFP (Feature) combined with Z-Scales (3) (median rank 4. 0 (±2. 0)), Z-Scales (3) combined with Z-Scales (Avg) (median rank 4.
0 (±2. 5)), Z-Scales (3) (median rank 4. 5 (±1. 5)), MSWHIM (median rank 5. 5 (±2. 5)), Z-Scales (  5 ) (median rank 6. 5 (±3. 5)), and ProtFP (PCA3) (median rank 7 (±2. 0)). Following from the results it is apparent that combining Z-Scales (a physicochemical descriptor) with complementary information leads to synergy, see below for a further discussion on this observation. The worst performing descriptor sets are ProtFP (PCA8) (median rank 12 (±2. 0)), ST-scales (median rank 13 (±1. 0)), and ProtFP (Feature) (median rank 16 (±0. 0)). While their performance was sometimes close to the other descriptor sets, they were found among the lower performing ranks in 80% of the experiments. Therefore it might be wise to avoid these descriptor sets on bioactivity modeling in setups such as the PCM modeling employed here; but this again will surely depend on the particular dataset at hand as well (for example a data set similar to the NNRTI set in the case of ProtFP (Feature)).
In this respect it would be interesting to investigate the use of non-alignment dependent descriptors such as PROFEAT  [42] , CTD  [43] , or descriptors from the PROPY package  [44]. A final option could be the usage of chemogenomics based descriptors. These can consist of phylogenetic trees which are generated based on the tanimoto similarity of ligands known for each protein (via the Similarity Ensemble Approach)  [45, 46].
Training times One final property of the descriptor sets has not been highlighted yet. On a workstation with a core i7 860 CPU and 16 GB memory, considerable differences in training times were found for the individual descriptor sets. On the datasets used in this work, as a rule of thumb ProtFP (Feature) showed the fastest model training while BLOSUM required most time (on average 191% of the training time required for ProtFP (Feature). The reason for this large difference is likely that the feature based descriptor set uses a single variable per amino acid, where the numerical descriptor sets use 3 (ProtFP (PCA3), Zscales (3) and MS-WHIM) to 10 values (BLOSUM).
Conclusions Overall performance differences between amino acid descriptor sets used in this study were rather small, with differences in the order of RMSE differences between 0. 01 -0. 1 log units. Hence, as a first approximationand with some differences between datasets -all descriptor sets considered in this study can be used to train predictive PCM models. Yet, a number of descriptor sets were observed to consistently score good, namely Z-Scales (3) combined with Z-Scales (Avg), ProtFP (Feature) combined with Z-Scales (3), Z-Scales (3), MS-WHIM, Z-Scales (5), and ProtFP (PCA3). Performance on different targets exhibits significantly larger differences in performance than differences between descriptor sets; for example the RMSE difference between the HIV mutant modeled best and worst was 1.
GPCR data set The second bioactivity data set employed for benchmarking different amino acid descriptor sets comprised a subset of 32 human monoamine receptors (class A GPCRs listed in Additional file 1: Table  S2 ; see also Additional file 1: Figure  S17  regarding the subset of receptors used) obtained from ChEMBL version 16  [21]. Receptors were selected only if more than 120 unique ligands (and optimally 200) with annotated activity were present in ChEMBL. A binding site residue selection was obtained using the program JOY  [47]. All known GPCR structures in the pdb up to December 2012 were superposed and residues in contact with the ligand were selected. Subsequently residues were translated to the aminergic GPCRs counterpart using the alignment from GPCRdb  [48]. Residue positions that were gapped in any of the aminergic GPCRs or positions for which no GPCRdb alignment number was available were discarded.
Finally compounds were clustered (using the ECFP_6 fingerprint, also used to train the models) to obtain a total of 100 chemically diverse 'actives' and 75 chemically diverse 'inactives' per receptor, in addition a random 25 compound from ChEMBL were included as presumed inactives (based on the work of Heikamp and Bajorath)  [49]. Compounds were standardized, salts were removed, and ionized at pH 7. 4 in Pipeline Pilot 8. 5  [50]. In total 3,230 distinct compounds were selected to generate a bioactivity model, the final dataset comprising 6,046 ligand-protein data points. This corresponds to 6% of the total of 103,360 possible compound-receptor combinations in the full matrix of 3,230 compounds and 32 targets; see Table  2  for further details.
70-30 validation The first benchmark employed in this study was a '70-30' validation experiment. Each descriptor set was used in turn in combination with each of the datasets, and a model was trained on a random 70% of the data available and used to predict the bioactivities of the remaining 30% of the data. This procedure was repeated ten times with different random splits and from the resulting validation parameters the mean and standard deviation were calculated. For the ACE inhibitors this represented a particularly challenging benchmark as this set only includes peptides and no small molecules. Hence only the amino acid descriptor sets could be used to characterize similarity for unknown data points whereas in the PCM sets the distance is characterized by the combination of amino acid descriptor similarity and ligand descriptor similarity.
For the bioactivity datasets employed for PCM modeling (which takes both ligand-side and protein-side descriptors into account) this benchmark provides an answer to two different questions. Firstly, the model was asked to make bioactivity predictions for those compounds that are not present in the training set and hence to extrapolate in the chemical domain. This part of the validation was particularly emphasized in case of the GPCR and PI data sets set due to the relatively lower average compound similarity. Hence the model is asked to extrapolate the activity of known compounds and targets to unknown compounds. Secondly, a compound can be present in the training set as annotated on one target, and also be present in the test set as annotated on target 2. This part of the validation was hence emphasized in case of the NNRTI data set due to the high average compound similarity.
Leave-one-sequence-out validation This validation experiment was performed for each target in order to assess extrapolation abilities of the PCM models in the biological / target domain. Hence this validation was only applied to the datasets containing targets (GPCR set, NNRTI set, and PI set). In this part of the work, in turn a single target is left out of the training set and subsequently a model is trained on all remaining bioactivity data points. Afterwards activity values of all compounds on the target left out of the initial training procedure are predicted and compared to the experimental values. These steps are repeated for all targets in the data set in turn. The exception was the PI set, here not a single target but 10% of the targets was left out. The reason for this was that performing a 1060-fold LOSO experiment for each descriptor was computationally very demanding.
PCM modeling method Both regression and classification models were generated in Pipeline Pilot Version 8. 5 using the R-statistics modeling package version 2. 12. 1  [50, 59]. Modeling was performed using the 'randomForest' package in R Statistics  [60]. The size of the forest was experimentally determined to be optimal at 1500 trees (1000 in the case of the ACE inhibitors), the maximum number of descriptors to be sampled at each node was set at a fraction 0. 5 of the total number. Class size equalization was turned on and a performance estimate during training was obtained using out-of-bag validation. Furthermore data points were fed into the model in a randomized order (differing between repeats of an experiment). Moreover, in the 70-30 experiments models were trained in ten fold with different randomized splits to get a more reliable performance estimate.
Comparison to QSAR models For the PCM datasets (GPCR, NNRTI, and PI) also dedicated QSAR models were trained per target using a 70% -30% approach. Of these models the average RMSE / MCC and R 0 2 / sensitivity were calculated along with the standard deviation. The results of these models are shown in Additional file 1: Figure  S18. The PCM models outperform the QSAR models (on all datasets and with all descriptors). Only in the case of the PIs the RMSE of the QSARs is slightly lower (0. 23 (±0. 22) log units versus 0. 31 (± 0. 03) log units), however the R 0 2 is considerably worse (0. 13 (±0. 28) versus 0. 85 (±0. 03)).
Descriptor set ranking Finally, to obtain a broadly derived performance measure all 16 amino acid descriptor sets were ranked based on their performance per dataset per experiment per validation parameter. This rank-based assessment prevents a single dataset that is modeled very well or very badly (as expressed in RMSE or MCC) unduly influencing the average performance of this descriptor set. Descriptor sets were ranked twice per experiment using 2 validation parameters (R 0 2 and RMSE in the case of regression and MCC and Sensitivity in the case of classification). Hence this leads to 14 ranks (ACE inhibitors R 0 2 rank, ACE inhibitors RMSE rank, GPCR 70-30 MCC rank, GPCR 70-30 Sensitivity rank, etc. ). These validation parameters were the mean of the ten repeats of each model. Subsequently the median rank and the MAD were determined of all descriptor sets based on these 14 ranks.
Figure12Median rank of the descriptor sets in the bioactivity benchmarks. The median is calculated over all 14 ranks (1 rank per dataset, per experiment, per validation type), also shown the median average deviation (MAD). The best three descriptor sets have a median rank < 5 among which the combinations of Z-scales (3) with other descriptors perform the best. The worst performance is by BLOSUM, ProtFP (PCA8), ST-scales and ProtFP (Feature) with a mean rank > 11. BLOSUM has a large standard deviation due to its inconsistent performance.
DNA Microarray Analysis We analyzed TTK DNA copy number in the different tumor subtypes (46 TNBC; 33 Her2; 39 LB and 35 LA) and in 17 healthy tissue samples. We purified DNA from frozen tumor samples with the use of a standard phenol/chloroform procedure, as described in  [30]  and hybridized 500 ng of genomic DNA onto Affymetrix SNP6. 0 microarrays, according to the manufacturer recommendations. We first normalized and analyzed data from SNP6. 0 microarrays using Affymetrix Genotyping Console. Next we performed a segmentation of the signal from SNP probes using the Colibri function available in the CGHseg R package  [31]  to identify copy number alterations. In a first segmentation round, we identified and discarded outlier probes giving rise to singleton segments. After a second segmentation round, we merged segments that contained less than five probes to the closest adjacent segment.
We then fitted a linear mixed model by using the nlme package using Restricted Maximum Likelihood (REML)  [34]. The model included the tumors, and in normal breast tissues (N). (C) Correlation between TTK protein and RNA levels. Omic data obtained in panels A and B reveal the consistency of TTK measurements between RNA and protein levels as quantified by Spearman correlation within the entire tumor population, within TNBC (O, red filled), Her2 (D), LB (X) and LA (+) tumors. (D) Quantitative analysis of the percentage of TTK positive cells measured by immunohistochemistry (IHC) in human breast cancer tissues. We present the results as the percentage of cells positively stained for TTK within fractions of samples in each BC sub-types, with TNBC in red, Her2 in purple, LB in green and LA in blue.
We dried the processed slides by centrifugation and scanned them using a GenePix 4000B microarray scanner (Molecular Devices). We determined spot intensity with MicroVigene software (VigeneTech Inc). The data obtained for TTK was part of a larger set of 27 RPPA arrays that we used to analyze the same tumor samples and cell lines with different antibodies. Briefly, the data from each RPPA slide was log2 transformed, median centered and scaled (divided by median absolute deviation). We then adjusted sample loadings effects individually for each hybridization by correcting the dependency of the data for individual arrays on the median value of each sample over all 27 arrays using a linear regression. These preprocessing steps (centering, scaling and sample loading correction) were done separately for tumor samples and for cell lines.
Immunoblot (IB) For each extract, we separated 10 mg of protein on 4-12% TGX gels (BioRad) and transferred onto nitrocellulose membranes (BioRad). We saturated membranes with TBST-BSA and incubated them overnight at 4uC with the appropriate primary antibodies diluted in TBST-BSA. After washes, we incubated membranes with the appropriate HRP peroxidase secondary antibodies for one hour at room temperature. For visualization of proteins, we incubated membranes with ECL solution (Amersham, GE Healthcare) for chemiluminescence detection and imaged membranes using the LAS-3000 Luminescent Image analyser and Image Gauge software (Fuji, FSVT).
Cell Cycle Analysis by Flow Cytometry We collected siRNA-treated floating and detached (after trypsinization) cells. Then, we washed them once with PBS and then with PBS containing 0. 5% BSA. We fixed the cells in cold 70% ethanol with gentle vortexing. After fixation, we incubated the cells in PBS containing 10 mg/ml propidium iodide (PI; P3566, Invitrogen) and 200 mg/ml RNase A (Pure Link TM RNase A, Invitrogen) for 30 min at RT. We collected the samples using FACScalibur (Becton Dickinson) and we analyzed a minimum of 20,000 cells per sample using CellQuest software (Becton Dickinson). We quantified DNA content by using Modfit LT software (Verity Software House) and we expressed results as a distribution of cells in each cell-cycle phase plus sub-G1 population.
The error bars represent the standard deviation of the mean and asterisks, the p values from Student's t test (*p,0. 05; **p,0. 01; ***p,0. 001). doi:10. 1371/journal. pone. 0063712. g004 Annexin V assay: Additionally, we determined the proportion of apoptotic cells by using the annexin-V-FLUOS staining kit (Roche) according to the manufacturer's instructions. After sequential staining by annexin V and PI, we performed flow cytometry analyses on a LSRII Instrument (Becton Dickinson). Using CellQuest software, we analyzed a minimum of 10,000 cells per sample and we evaluated the percentage of living cells with low annexin V and low PI staining, apoptotic cells with high annexin V and low PI staining and necrotic cells with high annexin V and high PI staining.
Soft-agar Tumorigenicity Assay A day after siRNA transfection, we trypsinized MDA-MB-231, MDA-MB-468 and HCC70 cells. We resuspended 5610 3 cells in 0. 35% soft-agar medium, consisting of equal volumes of 0. 70% agarose (A4018, Sigma) and 2X culture medium, and plated onto 1 ml of solidified 0. 5% soft-agar in 6-well plates. For MDA-MB-231 cells, which are extremely invasive, we added, on top of the cells, an additional layer of 0. 5% agar medium. We incubated all cell lines for 4 weeks at 37uC in a humidified atmosphere with 5% CO 2. We stained cell colonies by MTT. We automatically quantified the number of colonies by using the LAS-3000 Luminescent Image analyser (Fuji, FSVT) and ImageJ 1. 43u software (NIH). We performed all experiments in triplicate and presented the results as a percentage of colonies normalized to colonies from siCtrl-transfected cells.
High Expression of TTK is Associated with Triple-negative Breast Cancers We have constituted our tumor collection to get a similar number of biopsies for each of the 4 main breast cancer subgroups. Biopsies (46 TNBC, 33 Her2, 35 LA and 40 LB), obtained from patients treated at the hospital of Institut Curie (Biological Resource Center), were characterized by IHC (data not shown). We confirmed by microarray-based transcriptional profiling analyses that the breast cancer subtypes identified by IHC also clustered according to BLBC/TNBC, Her2, LA and LB breast cancer gene-expression signatures  [39]  (data not shown). In addition, our BC cohort, biased to contain all subtypes equally represented and accurately characterized (see materials and methods), contains 18 healthy breast samples obtained from reduction mammoplasty.
For in vitro analysis, we selected three TNBC cell lines, MDA-MB-468, MDA-MB-231 and HCC70 with high, intermediate and low TTK protein levels, respectively (Figure  3 ). These three cell lines are mutated for TP53 gene (www. sanger. ac. uk/genetics/CGP/ cosmic). In addition, we included in our study the non-malignant cell line, MCF10A, which is wild-type for TP53. We performed the depletion of TTK with 2 distinct and specific siRNAs (TTK_6 or TTK_7) and examined their effects on cell viability. The depletion of TTK was verified at the RNA level by quantitative-PCR (data not shown) and at the protein level by immunobloting. We observed that the depletion of TTK significantly impaired the proliferation of the 3 TNBC cell lines, MDA-MB-468 (Figure  4A ), HCC70 (Figure  4B ) and MDA-MB-231 (Figure  4C ).
TTK Depletion in Triple-negative Breast Cancer Cell Lines Induces Apoptosis We performed additional experiments to assay whether TTKdepleted cells undergo cell death by apoptosis. For the same reasons as previously mentioned, we measured the induction of apoptosis at different times, 72 h post-transfection for MCF10A cells and 120 h post-transfection for TNBC cells. First, we examined one of the earliest events in apoptosis, the externalization of phosphatidylserine by measuring annexin V staining in living cells. We found that TTK depletion increases the percentage of apoptotic cells both in normal and TNBC cells (Figure  6 ) whereas the percentage of necrotic cells did not change in comparison to cells transfected with the control siRNA (data not shown). We observed a more pronounced effect in HCC70 cells (Figure  6B ), which is consistent with the FACs analysis (Figure  5B ).
Acknowledgments We are grateful to  Dr Bernard Asselain  ( Institut Curie, Research center, Inserm U900 ) and  Martine Yann  for their help during the selection of the human tumors of our cohort. We are indebted to  Dr Xavier Sastre-Garau  ( Institut Curie, Department of Tumor Biology ), his colleagues from the  Biological center of Institut Curie , and the patients for the human breast tumor samples and, to  Dr Fabien Reyal  ( Institut Curie, Surgery Department and Research center, CNRS UMR144 ) for providing some normal breast tissues. We thank  Dr Niclas Setterblad  for the  FACs facilities (Plateforme Technologique de l'Institut Universitaire  d'hematologie, Paris 7). We thank  Benoit Albaud ,  Caroline Hego , Cecile Reyes from the platform of  Molecular Biology Facilities , and  Aurelie Barbet ,  Lamine Coulibaly  and  Emilie Henry  from the RPPA platform.
Introduction Making health-related decisions can be difficult due to the amount and complexity of information available. Audio-visual information may improve memory for health information but whether audio-visual information can enhance health-related decisions has not been explored using quantitative methods. The objective of this systematic review is to understand how effective audio-visual information is for informing health-related decision-making compared with audio-only or visual-only information. Methods and analysis Randomised controlled trials (RCTs) will be included if they include audio-visual and either audio-only or visual-only information provision and decision-making in a health setting. Studies will be excluded if they are not reported in English. Twelve databases will be searched including: Ovid MEDLINE, PubMed and PsychINFO.
Eligibility criteria Peer-reviewed articles will be included, as well as ongoing research where datasets are available (eg, via clinicaltrials. gov, opengrey. eu or the Open Science Framework). The Participant/Intervention/Comparator/Outcome framework will be used to identify papers. Only articles published in English will be considered given time constraints and lack of funds for translation. There will be no date restrictions placed on the literature searches. Study designs will include RCTs. Studies will be screened for inclusion of audio-visual stimuli, and audio-only and/or visual-only stimuli. Audioonly stimuli can include spoken speech, for example, telephone consultations or audio recordings. Visual-only stimuli can include pictures, animations and written text. The audio-visual stimuli can include videos, television, or a combination of the audio-only and visual-only stimuli listed.
Information sources Databases to be searched: Cochrane Database of Systematic Reviews; Database of Abstracts of Reviews of Effects; Cochrane Central Register of Controlled Trials; Cochrane database of methodology reviews; Cochrane methodology register: EBSCO, Ovid EMBASE, Ovid MEDLINE, PubMed, PsychINFO, Scopus and Web of Science (Science and Social Science Citation Index). Articles identified from the searches will be managed in Refworks to deduplicate citations. Abstracts will then be uploaded to Covidence for screening. Preliminary searches to test search terms were carried out in October 2021; the full search will be conducted in March 2022, and the searches will be repeated before publication, with the estimated submission date in December 2022. Table  1  shows the search strategies developed with input from a specialist librarian.