dataesr/scientific-paragraphs-categorization huggingface dataset:
The fixed modifications were carbamidomethylation at cysteine, and TMT at N-termini and internal lysine residues. Variable modifications included oxidized methionine and N-terminal protein acetylation. Individual spectra were automatically designated as confidently assigned using the Spectrum Mill autovalidation module. Specifically, a target-decoy based FDR scoring threshold criteria via a two-step auto threshold strategy at the spectral and protein levels was used. First, peptide mode was set to allow automatic variable range precursor mass filtering with score thresholds optimized to yield a spectral level FDR of <1. 2%. A protein polishing autovalidation was applied to further filter the peptide spectrum matches using a target protein-level FDR threshold of 0. Following autovalidation, a protein-protein comparison table was generated, which contained experimental ratios.
, 2018)  and chromVARmotifs (https:// github. com/GreenleafLab/chromVARmotifs) R packages to generate motif-level measures of differential accessibility. Signal tracks were generated from bigwig files created from aligned bams using Deeptools v3. 1. 1 bamCoverage command  (Ramírez et al. , 2016)  with the following parameters: "--Offset 1 -bs 50 --smoothLength 150 --maxFragmentLength 2000 --scaleFactor SCALEFACTOR". Scaling factors for each sample were generated by quantifying total cutsites within each sample's IDR-filtered peaks and dividing by 1e6. Signal tracks for combined replicates were generated by first concatenating replicate fastqs, then applying the same processing steps described above. Immunohistochemistry and image acquisition-Immunohistochemical staining (IHC) for MCL1 (Clone 22, 1:100, Santa Cruz), Acetyl-CoA Carboxylase (Clone C83B10, 1:50, Cell Signaling), Phospho-Acetyl-CoA Carboxylase (Clone D7D11, 1:50, Cell Signaling) or AMPKα (clone D5A2, 1:50, Cell Signaling) was performed using an automated staining system (BondRX, Leica Biosystems, Buffalo Grove, IL) according to the manufacturer's protocol and as previously described  (Roemer et al.
Image acquisition was performed using the Mantra imaging platform (Akoya PerkinElmer), as previously described  (Carey et al. , 2017). At least 5 or more representative regions of interest per section at 20x magnification were chosen by the pathologist within the tumor area. Areas without tumor or normal tissue were excluded from the analysis. Image Analysis was performed using the InForm 2. 4 Image Analysis Software (PerkinElmer). All nucleated tumor cells within a field of view were identified via nuclear hematoxylin staining. Image analysis algorithms were trained by a pathologist to identify tumor cells that were positive for each of the targets above. A percentage of the total tumor cells identified that are positive for the stain of interest is given. Quantitative PCR-The ID3 and PRKAR2B quantitative PCR assay was designed using a nested design, with outer primers for preamplification and inner primers for qPCR detection.
Quantification and Statistical Analysis Data analyses were carried out using GraphPad Prism version 7 and R software. The data are summarized as mean ± s. e. m. or s. d. as indicated, and presented as individual values, scatter plots, heatmap or with column bar graphs. Data were analyzed using Student's t-tests. Oneway ANOVA was used to compare three or more groups in time point analyses. Differences were considered significant when p < 0. 05. Synergy analysis for venetoclax drug combinations were performed using the Chou-Talalay method based on the median-effect principle  (Chou, 2010). Combination index values and isobolograms were generated using CompuSyn. Excess over Bliss scores were calculated based on the Bliss Independence model of drug synergy  (Bliss, 1939). Trajectory analyses for effect of venetoclax on metabolism were performed by looking at the least squares regression of OCR/ECAR vs time for each cell line/treatment.
ACKNOWLEDGMENTS We are grateful to  Drs. Ta-Ming Liu ,  Jing Deng  and  Levi Garraway  for constructive and valuable discussion. We are appreciative of  Dr. Guo Wei  ( Broad Institute ) for providing the MCL-1 inhibitor S63845. We acknowledge  Carrie Cibulskis ,  Adam Tracy ,  Lan Nguyen ,  Candace Patterson  and  Alicia Wong  for expert project management. We are grateful to  Amy Goodale  for her skilled execution of the ORF screen, and to  Ben Ferland  for the immunohistochemical staining studies. The authors would like to acknowledge support from  NCI-PO1CA206978 , and particularly, generous support of the  Gerstner Family Foundation  for the genetic screens. R. G. was generously supported by the  Fishman Family Fund. V. M. L. received support from the  American Society of Hematology HONORS Award , the  Harvard Medical School Scholars in Medicine Office , and the  HST Research Assistant Award.
Data and Code Availability The RNA seq and ATAC seq data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. , 2002)  and are accessible through GEO Series accession number GSE 128563 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi? acc=GSE128563). The original mass spectra and sequence database used for searches may be downloaded from MassIVE (http://massive. ucsd. edu) using the identifier: MSV000083512. The dataset is directly accessible via ftp://massive. ucsd. edu/MSV000083512. dbGaP accession number for the depositing of WES data for this study is phs001875. v1. p1.
(G) Proposed model for venetoclax resistance in lymphoid malignancies. Santa Cruz A-769662; CAS: AMPK activator A-769662 Biotechnology 844499-71-4 See also Figure S6 and Tables S1-S4, and S8. Cristical Commercial Assays DNAeasy Blood and Tissue Kit Qiagen #69504 RNeasy FFPE Kit Qiagen #73504 Cell Titer-Glo Luminescent Cell Viability Assay Promega G7571 Pierce™ BCA Protein Assay Kit ThemoFisher #23225 QIAGEN Plasmid Maxi Kit Qiagen #12162 KAPA Biosciences 96-reaction kits Illumina KK8234 Quant-iT™ PicoGreen™ dsDNA Assay Kit ThemoFisher P11496 Nextera Rapid Capture Exome Kit Illumina #20020616 Quant-iT™ RiboGreen™ RNA Assay Kit ThemoFisher R11490 BOND Polymer Refine Detection Ki Leica Biosystems DS9800 Deposited Data RNA seq and ATAC seq data This paper NCBI's Gene Expression Omnibus GSE 128563 Proteomics data This paper MassIVE database MSV000083512 WES data This paper dbGaP phs001875.
It uses as the reference the known reference solution from the Newtonian potential and a particular coordinate transform described in  [13, 14, 15]. It only requires the solution of elliptic integrals and the algorithm for eigenvectors and eigenvalues of a symmetric 3 -3 matrix, for example, using Python-related numerical tools, Scientific Computing Tools for Python (scipy: scipy. special. ellipkinc and scipy. special. ellipeinc), and Numerical Python (numpy: linalg. eig). We present in the appendix 6 an unpublished derivation of the solution based on the Fourier transform method and a change of variable on the unit sphere similar to that presented for a similar elastic solution in [? ]. Details on the numerical integration of averaged tensors are presented in section 7. Averaged tensors and effective conductivity tensors are transversely isotropic with the same symmetry axis as the matrix.
ζ ⊗ ζ. t H -1 ζ. t H -1. σ. H -1. ζ dS ζ (73) 2 Define a transformed ellipsoid with shape tensor R, R = H. σ -1/2 , σ -1/2 = 3 i=1 1 √ σ i E i ⊗ E i ( 74 ) with σ i the eigenvalue of σ and E i the associated eigenvector. P (σ, H) = σ -1/2. 1 4 π S ζ R -1. ζ ⊗ ζ. t R -1 ζ. t R -1. R -1. ζ dS ζ P (i,R). σ -1/2 (75) The anisotropic solution can finally be expressed as P (σ, H) = σ -1/2. P i, H. σ -1/2. σ -1/2 (76) See  [13, 15]  for details on the numerical calculation. This calculation only needs the solution of elliptic integrals and the algorithm for the eigenvectors and eigenvalues of a symmetric 3 -3 matrix. As an example, by using Scientific Computing Tools for Python, available at www. scipy. org, one can use the scipy. special. ellipkinc, scipy. special. ellipeinc, and linalg. eig tools available at www.
Extension to the continuation of quasi-periodic solutions is adressed in  [22]. The continuation solver MANLAB-1. 0 [1]  has been the first attempt to design a general purpose continuation software working on the ANM principle. It allows a user to enter its own algebraic system and to interactively draw the bifurcation diagram. It works well for systems with a few hundreds of equations but the quadratic recast of elementary transcendental functions was a little abstruse and finite element mechanical models could not be implemented because of the lack of performances. A common denominator of the works mentioned above is the requirement to recast the governing equations in a quadratic format. The motivation for this recast is that the Taylor series computation becomes easy and efficient when a system is quadratic.
However, this requirement has turn out to be the most difficult point of the method. Its misunderstanding has prevented many potential users and developpers from succeeding in the use of the method, especially when the system of equation contains elementary transcendental functions (exp, log, sin, cos, power,. ). Introducing Automatic Differentiation (AD) principle inside the ANM process  [9] ,  [10]  has been the first elegant way of removing this difficulty. The DiaManlab software  [7]  allows the users to write the governing equations in a natural way, without any need of a quadratic recast. However, once again, the price to pay for this genericity is a reduction of the computing performance that may be again a drawback for large systems resulting for instance from the finite element (FE) discretization of a continuous problem.
Another important improvement is the use of a sparse tensor formalism for the internal representation of a quadratic system on a computer. This allows an efficient construction of the linear systems that have to be solved for the series computation, including a block inversion for the condensation of the auxiliary variables. Another key point is that the sparse tensors are automatically constructed from the equations by using polarization formula. This allows to write the quadratic equations in a quite natural way. The generic method described here has been implemented in Matlab as the fourth version of the Manlab suite. It permits to deal with a very large class of algebraic systems containing many elementary transcendental functions as it will be shown on three examples in section 5. Another very important potentiality of this method is the capacity to implement a FE mechanical model in a quite simple way and, that time, without suffering from poor computing performances.
o. f is 2616 and the number of auxiliary variables is 12408. The number of nonzero coefficients of the quadratic sparse tensor Q is here 31584. The computing time for these 83 steps of continuation is 25 seconds on a laptop computer (two processors 2,5 GHz Intel Core i7 with 16 Go RAM) with the Matlab software Manlab-4. 0. As discussed so far, the formulation of a mechanical model is not unique and it is also the case for its quadratic recast. A natural question is then "what is the best recast" and also "is it worth to have a minimal number of auxiliary variables". As an answer to these questions, the problem considered above has also been written by taking only the stress tensor S for the auxiliary variables. The consequence of not storing the gradient X and the first Piola-Kirchhoff P is a tremendous increase of the size of quadratic sparse tensor Q which pass from 32 terms per Gauss point for the previous formulation to more than a thousand.
Conclusion An efficient implementation of a Taylor series based continuation algorithm is presented. Its need of a quadratic formulation of the equations is explained and extended to the case of elementary transcendental functions. The decades of experience using the ANM continuation resulted in fourth version of the continuation software Manlab that is used here to treat successfully different systems. The accuracy of the computations is shown following the branches of the classical Logistic map and the twelve first flip bifurcations. The robustness of the method is demonstrated when applied to the Layne-Watson problem. The algorithm is then applied on a stick-slip problem, showing the possibility to make the continuation of a smooth by part system with this method. The last section presents a generic application to mechanical problems issued from FEM.
Acknowledgment The authors want to thank  Stéphane Bourgeois  for fruitful discussions on the finite element analysis part. This work has been carried out in the framework of the  Labex MEC  ( ANR-10-LABX-0092 ) and of the A* MIDEX  project ( ANR-11-IDEX-0001-02 ), funded by the  Investissements d'Avenir French Government program  managed by the  French National Research Agency (ANR).
Acknowledgement Firstly, we would like to thank the COST Association in Brussels for the faith and vision to fund this COST Action and for their invaluable work encouraging and facilitating science and technology across Europe. In particular, we would like to thank Dr. Deniz Karaca and Ms. Tania Gonzalez-Ovin for the essential scientific and administrative support they have provided over the course of the Action. We would like to thank all the GNSS network operators and data providers, as well as the IGS and EUREFwithout which there would be no data for such scientific study. In addition, we would like to recognise and thank a number of European and national research projects whose support has been fundamental. We would like to thank the support from all the participants' institutions (Appendix B), and special thanks must go to those people and institutions who have been involved in hosting meetings and workshops (Appendix A).
Finally, we would like to thank all of the participants of GNSS4SWEC; without your enthusiastic participation, none of this would have been possible. Particular thanks to a number of people who made significant contributions to coordination: • Michal Kačmařík for coordinating and reporting on the WG1 benchmark campaign and slant delays intercomparisons and the help with the Final Report • Hugues Brenot for coordinating and reporting on the WG1 sub-group for asymmetry modelling • Florian Zus for coordinating and reporting on the WG1 sub-group for NWP tropospheric parameter modelling • Pavel Václavovic for organising the WG1 real-time demonstration campaign and for organising the WG1 PPP sub-group • Norman Teferle for coordinating the WG1 sub-group for ultra-fast products • Zhiguo Deng for reporting on WG1 sub-group for multi-GNSS development • Karolina Szafranek for reporting on WG1 sub-group new ACs and networks • Wolfgang Söhne for support with completing the WG1 Final Report • Jan Kaplon, Gregor Möller, Radmila Brožková and Pavla Skřivánková for support with the WG1 benchmark dataset vii 3.
• Coordinate the collection, archiving and exchange of raw GNSS data from various regional networks in Europe. • Establish a database of reprocessed GNSS tropospheric products at global and regional scales and assess their quality by inter-comparison with in-situ and remote sensing techniques. • Collaborate with the climate and meteorological communities, to assess and improve reanalyses and climate models (e. g. by assimilation of reprocessed GNSS tropospheric products) and investigate climate signals (trends and variability).
Working Group 2: Use of GNSS Tropospheric Products for High-Resolution, Rapid-Update NWP and Severe Weather Forecasting This WG will coordinate the application of existing and development of new GNSS tropospheric products for high-resolution rapid-update NWP and forecasting of severe weather. The activities are: • Create a standardised exchange format, and provide the gradients and slant delays from current networks through a central hub facility. • Define and generate specific benchmark datasets in the form of GNSS observations, alternative water vapour and refractivity observations, and NWP products, for assessment and validation. • Evaluate and validate the information content of the enhanced, new products provided by WG1, such as of gradients and slant delays (determine error sources, correlations etc.
). • Develop, validate, and exchange methods for initialization of NWP models using GNSS gradients and slant delays. • Exchange methods of nowcasting applications of GNSS gradients and slant delays: • Organize detailed analyses of special case studies. • Establish a database with case studies of severe weather events. • Organize user Workshops (audience forecasters/NWP modellers). • Coordinate multi-model initialization experiments to obtain insight in the quality of different methods and models used in nowcasting and NWP. • Assess the benefit of multi-GNSS tropospheric products in NWP and for severe weather forecasting. Expected outcomes of WG2: • Assessment reports and guidelines on standardised methods and data formats (in collaboration with WG1) for the initialization of NWP models using new/enhanced operational GNSS tropospheric products and for use in nowcasting.
• Promotion and dissemination of these standardised methods  (STSMs, Training Schools, Workshops). • Produce requirements for enhanced and new operational GNSS tropospheric products. • Benchmark datasets for test, assessments and validations (for each method/ product). • Database with severe weather case studies. • Recommendations and methods for operational GNSS nowcasting tools. • Identify new ground-based GNSS data providers for operational NWP and severe weather monitoring in the data sparse regions such as Eastern and South-Eastern Europe.
g. , inter-annual, intra-seasonal, and synoptic variability, seasonal and diurnal cycle) at global and regional scales. • Bring together GNSS data owners, both private and public, on a European scale, with the goal of including additional Mediterranean partners, to attempt to agree on a strategy for collection of past, present and future data. Expected outcomes of WG3: • Assessment report of potential and existing GNSS datasets, metadata, and products for use in climate research. • Guidelines on the data formats, processing, and homogenisation methods for enhanced use of reprocessed GNSS tropospheric products in climate research. • A database of raw GNSS data and a consortium of GNSS data providers for climate research, at European and Mediterranean scale, and with connections to worldwide organisations (IGS, EUPOS, national positioning services, GNSS campaigns etc.
). • A new climate data record of GNSS ZTD and IWV, suitable for analysing climate trends and variability, and calibrating/validating independent datasets at global and regional scales. Chapter 3 Advanced GNSS Processing Techniques (Working Group 1) numerical prediction and nowcasting will be substantially improved. Such examples are: ultra-fast and high-resolution tropospheric products available in real-time or on a sub-hourly basis, parameters monitoring tropospheric anisotropy above the station (such as horizontal gradients and tropospheric slant path delays), and indicators of severe weather such as extreme convection. Development of advanced GNSS tropospheric products within COST Action ES1206 benefited from two dedicated campaigns prepared for a collaborative effort: (1) the benchmark campaign and (2) the real-time demonstration campaign.
Two were based on global numerical weather modelsthe European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim  (Dee et al. 2011 ) and the Global Forecast System (GFS) of the National Centres for Environmental Prediction (NCEP) available at http:// www. ftp. ncep. noaa. gov/data/nccf/com/gfs/prod/, and one was based on regional model ALADIN-CZ which was provided within the Benchmark dataset. The following tropospheric and meteorological parameters were calculated for all stations: zenith hydrostatic and wet delays, air pressure, partial water vapour pressure, mean temperature, temperature lapse rate, water vapour pressure and zenith wet delay exponential decay rates. GFZ used their direct numerical simulation (DNS) tool  (Zus et al. 2014 ) in order to derive the following parameters from two global NWMs (ERA-Interim and GFS): zenith hydrostatic and zenith wet delays, horizontal (1st, 2nd order) tropospheric gradients and coefficients of hydrostatic and wet mapping functions.
7 km resolution, limited-area hydrostatic model, operational analysis in 6-h interval with forecasts for 0, 1, 2, 3, 5, 6 h, http://www. umr-cnrm. fr/aladin/), ERA-Interim (1 horizontal resolution, 6-h reanalysis), and NCEP-GFS (1 horizontal resolution, 6-h operational analysis, https://www. ncdc. noaa. gov/data-access/model-data/model-datasets/ global-forcast-system-gfs). None of these NWM assimilates data from ground GNSS stations. For more information about the models, see  Douša et al. (2016)  and specifically  Trojáková (2016)  for the ALADIN-CZ model and  Dee et al. (2011)  for the ERA-Interim reanalysis. First, STD solutions using the ERA-Interim and NCEP-GFS models were delivered by GFZ Potsdam using acronym ERA/GFZ and GFS/GFZ, respectively. Two STD solutions were then delivered for the ALADIN-CZ model: (a) ALA/BIRA generated at Royal Belgian Institute for Space Aeronomy (BIRA), and (b) ALA/WUELS delivered by Wroclaw University of Environmental and Life Sciences.
Future Work Three institutions (GFZ, GOP, ROB) delivered GNSS STD solutions not only for the ten GNSS reference stations but for the whole GNSS network within the Benchmark data set. Our future study will therefore focus on (a) larger comparison within the network of stations, (b) an evaluation of azimuthal dependency of post-fit residuals under severe weather conditions and (c) an evaluation of GNSS STDs estimated from real-time and post-processed solutions using a stochastic approach. G. Möller Department of Geodesy and Geoinformation, TU Wien, Wien, Austria e-mail: gregor. moeller@geo. tuwien. ac. at Based on real GNSS measurements, the tropospheric signal in post-fit residuals is difficult to assess since it is superimposed by a series of other unmodelled effects like observation noise, multipath, satellite clock or orbit errors.
Nevertheless, observation stacking methods as applied in  (Kačmařík et al. 2017; Möller 2017 ) allow for the reduction of common parts like multipath or clock errors, but only when longer time periods or larger GNSS networks are processed. Hence, within the COST action an initiative was carried out which addresses the general tropospheric signal in GNSS post-fit residuals, with focus on differenced data processing. Theoretically, the precise point positioning (PPP) and the double difference approach (DD) are equivalent with respect to redundancy and with respect to the estimates, in case a correct stochastic model is introduced. Practically, the DD approach has some advantages in data processing since the satellite and receiver clock errors and therewith the hardware biases cancel out, which allows the fixing of integer ambiguities.
It turned out that in case of PPP methods the anisotropic delay, except for a small offset which was absorbed by the ambiguity parameter, could be recovered from the PPP post-fit residuals but unfortunately not from the DDR. If only a single baseline is processed, the DDR and also the reconstructed pseudo-ZDR are almost zero since the anisotropic effects were differenced out in data processing. In best case, the resulting STD bias and standard deviation was À1 mm +/À37 mm. This was obtained by fixing the ZTD and by taking all possible baselines between the 12 stations into account for the reconstruction of pseudo-ZDR using the method proposed by  (Alber et al. 2000). However, a comparable result (0 mm +/À38 mm) is obtained if no residuals are added to the isotropic STD. In practice, the ZTD is not known and therewith cannot be fixed to its given value.