dataesr/scientific-paragraphs-categorization huggingface dataset:
To remedy this latter inconvenience, we developed SAMA to 1) obtain an exhaustive and comprehensive analysis of tissue morphogenesis in 3D culture models of the breast, 2) to gain a deeper understanding of biological variability in tissue morphogenesis in 3D tissue cultures, 3) to acquire a rapid, automated and unbiased analysis of every epithelial structure in the 3D images that span a large sampling area and, 4) to combine imaging analysis with a meticulous analysis of the experimental replicates, thus ensuring reproducibility in the data obtained from the 3D cultures. 3D tissue cultures are increasingly becoming a source of new information regarding tissue morphogenesis, which can lead to a more detailed understanding of carcinogenesis in tissues such as the breast. However, lack of proper methodology and software prevents researchers from getting an in-depth analysis of a large population of 3D structures in images.
Acknowledgments We greatly appreciate the editorial assistance by  Cheryl Schaeberle. This research was supported by  Avon  Grant # 02-2009-093 , and  02-2011-095. Additional support was provided by  NIEHS/NIH   ES08314. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of Environmental Health Sciences  or the  National Institutes of Health.
For each model, 200 datasets of the total bacterial population were simulated over 24h using static antibiotic concentrations (256-fold concentration range) or over 48h under dynamic conditions (dosing every 12h; elimination halflife: 1h). Twelve-hundred random datasets (each containing 20 curves for static or four curves for dynamic conditions) were generated by bootstrapping. Each dataset was estimated by all six models via population PD modeling to compare bias and precision. For M1 and M3, most parameter estimates were unbiased (<10%) and had good imprecision (<30%). However, parameters for adaptive resistance and inter-conversion for M2, M4, M5 and M6 had poor bias and large imprecision under static and dynamic conditions. For datasets that only contained viable counts of the total population, common statistical criteria and diagnostic plots did not support sound identification of the true resistance mechanism.
These experimental models provide a wealth of time-course data on bacterial growth and killing  [24]. While a series of time-course models for bacterial growth, killing and emergence of resistance has been proposed, it is currently unknown which type of dataset from in vitro studies is required to soundly develop such PK/PD time-course models. We suspected datasets which only contain viable counts of the total bacterial population, but do not contain data on resistant population(s), may be insufficient to distinguish between competing models with different resistance mechanisms. We are also not aware of a systematic simulation-estimation study assessing the bias and precision of parameter estimates from in vitro antibacterial models. Therefore, our objective was to compare the ability of static and dynamic in vitro infection models to identify the PK/PD model with the true resistance mechanism used during simulation and to estimate model parameters accurately and precisely.
Monte Carlo simulations for static and dynamic in vitro infection models Population mean estimates. Informed by the range of ed parameter values for different antibiotics and bacterial strains  [3, 6, 14, 16, 20, 21, 23, [34] [35] [36] [37]  (Table  1 ), we selected sets of parameter values that yielded comparable CFU vs. time profiles over 24 h for simulation from the six models. These simulations were intended to provide realistic datasets on the antibiotic concentration effect-relationship for the viable counts of the total population for in vitro timekill experiments. A starting inoculum of 10 6 CFU/mL was applied for all simulations. Between curve variability and residual error. For these well-controlled in vitro studies, between curve variability was set to a small coefficient of variation of 10% for log-normally distributed parameters and to a standard deviation of 0.
Each concentration was simulated in duplicate yielding 20 curves per static time-kill dataset. Viable counts of the total population were observed at 0 (pre-dose), 0. 5, 1, 2, 4, 8, 12 and 24 h. For dynamic one-compartment in vitro models, we simulated one dose level with dosing every 12 h and a growth control both in duplicate. The simulated antibiotic peak concentration was 8x KC 50S and the antibiotic concentrations decreased with a pharmacokinetic elimination half-life of 1 h. Bacterial counts were simulated at 0, 1, 2, 4, 8, 12, 24, 28, 32, 36 and 48 h. A short half-life has been used to allow antibiotic concentrations to change over a large range with peak concentrations yielding rapid killing and trough concentrations allowing bacterial regrowth. This choice was expected to support estimation of model parameters and emphasize the features of a dynamic infection model.
Monte Carlo simulations. Our simulation-estimation studies (Fig  1 ) included the generation of 200 datasets for each of the six candidate models via Monte Carlo simulations for static or dynamic antibiotic concentration-time profiles. This included 100 datasets simulated under static and another 100 datasets simulated under dynamic conditions. Each dataset comprised 20 viable count profiles of the total bacterial population for static antibiotic concentration experiments and four profiles for 1-compartment dynamic infection models. These Monte Carlo simulations were performed for six candidate models yielding 1,200 datasets in total. Berkeley Madonna (version 8. 3. 18, University of California) was used for all Monte Carlo simulations. Estimation of population PD parameters. Each simulated dataset was estimated via population PK/PD modeling using the true model as well as the five other candidate models yielding 6 models x 1,200 datasets = 7,200 population estimations in total.
All PD model parameters of the respective models were estimated by simultaneously fitting of all viable count profiles of the respective dataset (Table  2 ). Estimation was performed using nonlinear mixedeffects modeling in the S-ADAPT software via the importance sampling algorithm (pmethod = 4 in S-ADAPT)  [38]. Modeling was facilitated by the SADAPT-TRAN tool and utilized estimation settings that were previously qualified for robust estimation of mechanismbased models  [39, 40]. Viable counts were fitted on log 10 scale and viable counts below the limit of counting were handled by using Beal M3 method as implemented in S-ADAPT  [41]. PD model selection. Many of combinations of the six studied models are nested. The more complex models converge to the simpler models, if the mutation frequency of the resistant population is zero, if the maximum extent of adaptation (Smax) is zero, if there is no conversion of bacteria to a resting stage, or if there is no inter-conversion between the susceptible and resistant population.
Bias and imprecision of parameter estimates. Table  4  (all six models) and Fig 4 (models 1, 3 and 5) compare the true parameter values with the median parameter estimates under static and dynamic conditions (based on the 100 datasets for each model and case). Overall, the precision of parameter estimates tended to be better for static compared to dynamic conditions. The median estimates were within 10% of the true value and the imprecision was <20% CV for most parameters of M1 and M2 under both static and dynamic conditions (Table  4 ). A noticeable exception was the estimated mean time of natural death (MDT) of resting bacteria in M2 which was considerably biased by 326% (estimated: 1,279 min vs. true: 400 min) in the dynamic setting and biased by 20% (estimated: 321 min vs. true: 400 min) in the static setting.
However, several models co-modelled both the total and resistant populations  [28, 43, 44]. In this context, it seems surprising that no systematic simulation-estimation study has yet been published to assess the ability to distinguish between competing antimicrobial PD models with different resistance mechanisms. This lack of knowledge affects the vast majority of mathematical models in antibacterial PD. We addressed this gap by performing Monte Carlo simulations with in total 1,200 simulated datasets that were estimated using six relevant structural models and two common designs for in vitro infection models. These models reflected genotypically stable resistance mechanisms (model M3), phenotypic resistance (i. e. adaptation for M5 and persisters for M2), inter-conversion between bacterial populations (M4), or multiple of these mechanisms (M6,  While models M1 and M2 yielded bacterial killing and death without regrowth (Fig  3A ), models M3 to M6 could all describe viable count profiles with initial killing followed by regrowth due to emergence of resistance in the presence of constant antibiotic concentrations.
It was therefore interesting to assess, whether a robust population PK/PD estimation algorithm (i. e. importance sampling) could adequately distinguish between competing models. Despite the use of one of the latest population modeling algorithms, this simulation-estimation study showed that standard statistical criteria could only identify the true model under both static and dynamic conditions in more than 80% of the cases for models M1 and M3 (Table  3 ). Importantly, M3 was incorrectly selected as the best model in the large majority of cases even if models M4, M5 or M6 were the true model used during simulation. For datasets that only contained viable count profiles of the total population, statistical modeling criteria could therefore not reliably identify the true model in case of regrowth due to bacterial resistance.
Quantitative viable count data of the resistant population(s) from antibiotic-containing agar plates at 0 and 24 h, for example, can provide experimental evidence to accept or reject several candidate models (Fig  2 ). If one observes resistant bacteria on antibiotic-containing agar plates (containing e. g. 3x the MIC of the antibiotic) at 0 h, one can reject models M1, M2, and M5, as those models assume the absence of resistant bacteria at time zero. Mutation frequency studies at a high bacterial density are expected to support calculation of the likelihood of pre-existing resistant mutants  [7]. Quantifying and modeling resistant bacteria over time would further enhance the ability to distinguish between competing models  [28, 43, 44 ]. An in-depth analysis of datasets containing one or multiple observations for resistant bacteria is beyond the scope of the present work.
Model predictions over longer time periods (i. e. extrapolation) led to more biased predictions, as expected. Our predictions were based on the median of the parameters values. As these simulations did not account for parameter imprecision, the discrepancies between the predicted and the actual viable count profiles are likely larger for some sets of estimated parameters. Bias tended to be less for some parameters under the dynamic compared to the static experimental setting for models with heteroresistance or adaptation (M3, M4, M5 and M6; Table  4  and  Fig 4). As expected, the dynamic setting yielded less precise parameter estimates most likely due to the considerably smaller number of observations for the dynamic setting (containing 4 curves per dataset) compared to the static setting (containing 20 curves per dataset).
Some limitations of our study came from the necessity to select PD parameters characterizing the simulated pathogen, to choose an experimental design for static and dynamic killcurves and to set the initial values for parameter estimations. Our chosen parameter values adequately characterized the concentration-effect relationship for each studied PD model and were selected based on biological plausibility and our experience from experimental datasets. Despite a clear concentration-effect relationship in the simulated viable count profiles for each dataset, it was difficult to impossible to mathematically identify the true resistance mechanism based on viable counts of the total population. It is possible that other sets of parameter estimates used during simulation would have yielded a higher likelihood to identify the model with the true resistance mechanism.
This presents a potential limitation of the present study. Moreover, the present study contained simulation-estimation scenarios for static and dynamic in vitro infection models. Our simulated dynamic in vitro model only assessed a scenario with one half-life, one dose level, and one dosing interval. Dynamic infection models with multiple doses, different dosing intervals, and potentially a range of relevant half-lives would provide a more informative dataset which may have supported the identification of the PD model with the true resistance mechanism. While it is a potential limitation of our study that we did not evaluate more dynamic infection model studies, most current papers on antimicrobial PD models use static time-kill experiments to define the concentration-effect relationship and select the PD model and its resistance mechanism.
Finally, we did not assess the sensitivity of the final parameter estimates and the model selection towards the choice of the initial estimates. In our previous work, we found the importance sampling algorithm in S-ADAPT to be robust and efficient despite the use of poor (i. e. 10-fold too high or 10-fold too low) initial estimates for every structural model parameter in a complex PK/PD model  [7]. In summary, for datasets based only on the total bacterial population, standard statistical modeling criteria failed to correctly identify the PD model with the true resistance mechanism (s) in the large majority of cases. These datasets did not contain data on antibiotic-resistant bacterial populations. This finding is highly important, as most published models in antibacterial PD were developed based only on data on the total bacterial population.
For our simulation scenarios, dynamic infection models tended to provide more accurate parameter estimates than static concentration time-kill studies for some parameters. Static time-kill studies yielded more precise parameter estimates compared to dynamic models likely due to the larger number of profiles per datasets. For both static and dynamic conditions, parameters related to adaptive resistance and interconversion of bacterial populations tended to be poorly estimated. Interestingly, predicted viable count profiles over the experimentally studied duration (i. e. 24 to 48 h) were reasonably accurate despite biased parameter estimates. Simulations over longer durations (i. e. extrapolations) tended to show more pronounced mispredictions and should be interpreted conservatively. Overall, it seems highly beneficial to utilize quantitative viable count data of resistant populations and characterize their MICs and resistance mechanisms to support the choice of the most appropriate PD model for bacterial resistance.
High-throughput binary protein interaction mapping is continuing to extend our understanding of cellular function and disease mechanisms. However, we remain one or two orders of magnitude away from a complete interaction map for humans and other major model organisms. Completion will require screening at substantially larger scales with many complementary assays, requiring further efficiency gains in proteome-scale interaction mapping. Here, we report Barcode Fusion Genetics-Yeast Two-Hybrid (BFG-Y2H), by which a full matrix of protein pairs can be screened in a single multiplexed strain pool. BFG-Y2H uses Cre recombination to fuse DNA barcodes from distinct plasmids, generating chimeric protein-pair barcodes that can be quantified via next-generation sequencing. We applied BFG-Y2H to four different matrices ranging in scale from ~25 K to 2.
A BFG-Y2H screen targeting cancer and cell cyclerelated proteins To demonstrate BFG-Y2H at larger scale and broaden discovery of new interactions, we next carried out BFG-Y2H screening on a "CCC" matrix, which includes proteins implicated in cancer according to the COSMIC database  (Forbes et al, 2015) , as well as cyclins, cell cycle kinases, and cell cycle kinase substrates  (Hornbeck et al, 2012) , encompassing 392 bait and 377 prey doubly barcoded ORFs (Table  EV1 ). CCC contained sixfold more protein pairs than the CENT matrix. Screening and scoring of CCC was carried out in essentially the same way as for CENT, except for small differences in the in-yeast assembly procedure (Materials and Methods and Appendix Note S4).
Performance comparison between BFG-Y2H and state-of-theart Y2H To compare the practical performance of BFG-Y2H with state-of-theart Y2H methods, the four BFG-Y2H screen results and a recent high-quality Y2H-based human interactome dataset HI-II-14  (Rolland et al, 2014)  were each evaluated using Lit-BM-13, a highquality set of literature-curated physical interactions  (Rolland et al, 2014) , as a benchmark standard. Each screen was assessed according to its ability to recover the subset of Lit-BM-13 falling within its tested space ( The smaller in-yeast assembly-based BFG-Y2H screens CENT and CCC yielded MCC = 0. 40 and 0. 24, respectively, as compared with 0. 50 and 0. 31 for the HI-II-14 dataset in the corresponding search spaces. It should be noted that HI-II-14 was based on the union of two primary screens and was filtered by pairwise retesting, while the performance of BFG-Y2H was based on single primary screens that were not filtered by secondary Y2H retesting.
BFG-Y2H allows high-throughput Y2H screening at substantially lower cost and effort than the other Y2H procedures (Appendix Fig  S4  and Table EV4 ). We demonstrated performance with a matrix of up to ~2. 5 M pairs, and there are no evident barriers to screening at genome scale. Each of our primary BFG-Y2H screens showed performance on par with a recent large-scale Y2H dataset resulting from two primary screens and filtering by pairwise retesting  (Rolland et al, 2014). We also demonstrated the ability of BFG-Y2H to identify interactions for high-background "auto-activator" baits that might be considered unscreenable by current Y2H. Moreover, quantitative scoring provides greater enrichment of true interactions at higher scores, allowing user-defined trade-offs in precision versus recall. Additionally, we show by pairwise interface modeling and co-crystal structural analysis that quantitative BFG-Y2H scores are related to interaction strength.
Bait ORF fragments were amplified with O023 and O024 primers, and prey ORF fragments were amplified with O025 and O026 primers. The ORF PCR products were analyzed with E-Gel 48 1% Agarose Gels (Invitrogen), and the gel band images were automatically sorted according to the expected product sizes by a Perl script developed based on ImageMagick (ImageMagick Studio) to check the quality of ORF fragments by size. We used the ORF fragments passing the Sanger sequencing and the ORF size quality control. The barcode fragments were amplified from the clonal barcode carrier destination plasmid collection (O027 and O028 primers for bait, and O029 and O030 primers for prey). The Gal4 bait and prey domain fragments were amplified by O031 and O032 primers and O033 and O034 primers, respectively. The common bait and prey backbone fragments were amplified from the destination plasmids by O035 and O036 primers and O037 and O038 primers, respectively, to generate yeast strains for the CENT space.
Gaussia princeps luciferase protein complementation assays Gaussia princeps luciferase protein complementation assay (GPCA)based validation assay was performed to validate the in-yeast assembly-based BFG-Y2H hits as described previously  (Remy & Michnick, 2006; Cassonnet et al, 2011). In order to define the threshold of GPCA signal for each validation screen, positive controls were obtained from the overlap of the GPCA-tested space with the union of the systematic Y2H human interactome HI-II-14 dataset  (Rolland et al, 2014)  and the high-quality literature-curated protein interaction dataset Lit-BM-13  (Rolland et al, 2014). Fifty-four and 36 arbitrarily protein pairs that scored poorly in CENT and CCC screens, respectively, were selected as negative controls (Table  EV2 ). The luciferase signal threshold for each validation assay was defined by the best balance of recall and precision according to the MCC.
Reported protein interaction dataset Reported Y2H-positive human protein interaction dataset HI-II-14 and the Lit-BM-13 dataset were downloaded from the CCSB Human Interactome Database (http://interactome. dfci. harvard. edu), and reported interactions by orthogonal assays were obtained from the BioGRID database (Chatr-Aryamontri et al, 2013) Release 3. 1. 91.
7, 14, 15 Trisomy 12 is the second most frequent recurrent chromosomal aberration in CLL and is associated with clinical and biological heterogeneity, potentially linked to the presence of additional genomic aberrations. This concept is reinforced by our present findings regarding the biological background and clinical presentation/outcome of subgroups of +12 CLL patients defined by the presence of extra trisomies. These subgroups seem to differ from patients with isolated +12, while also exhibiting a constellation of biological and clinical features whose co-occurrence is not likely to be incidental. This conclusion is also supported by our query for CLL cases with trisomy 19 in the Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer (available from: http://cgap. nci. nih.
Acknowledgments: we would like to thank the members of the  Spanish Cooperative Group for Hematological Cytogenetics  for providing clinical and biological data, and  Maria Gaitatzi ,  Zografia Lazarou ,  Olga Asteriou ,  Kristina Durechova  and  Eva Diviskova  for performing cytogenetic and FISH analysis. Funding: this project was supported in part by the  ENosAI  project (code  09SYN-13-880 ) co-funded by the  EU  and the  General Secretariat for Research and Technology of Greece ; the KRIPIS action, funded by the  General Secretariat for Research and Technology of Greece ; the  EU   Seventh Framework Programme under the "Capacities" specific programme ;  H2020  " AEGLE, An analytics framework for integrated and personalized healthcare services in Europe ", by the  EU ;  H2020  " MEDGENET", Medical Genomics and Epigenomics Network ", by the  EU ; the  Swedish Cancer Society ,.
Papanicolaou Hospital, Thessaloniki , Greece; and 14  Cancer Sciences, Faculty of Medicine, University of Southampton, UK the Swedish Research Council , the  Lion's Cancer Research Foundation , and  Selander's Foundation, Uppsala ; research projects  CEITEC   CZ. 1. 05/1. 1. 00/02. 68  and MZ CR projects  NT13493-4/2012 ,  AZV 15-31834A  and  AZV 15-30015A ;  Bloodwise  ( 11052 ,  12036 ,  12050 ,  14027 ); the  Kay Kendall Leukaemia Fund  ( 873 );  Wessex Medical Research and the Bournemouth Leukaemia Fund , with infrastructure support from a  Cancer Research-UK  centre grant ( C34999/A18087 ). TM is recipient of a  Marie Sklodowska-Curie individual fellowship  (grant agreement n. 702714), funded by the  EU   H2020 research and innovation program. The online version of this letter has a Supplementary Appendix.