dataesr/scientific-paragraphs-categorization huggingface dataset:
475). The data was coded independently by two researchers and discussed in multiple rounds within the research team to ensure intra-and intercoder reliability. In summary, the final template featured three main categories along horizontal, vertical and spatial patterns: themes discussing entrepreneurship in the region, the embeddedness of the firm within the region and family and finally, codes describing growth patterns and issues of family firms (for an example of the coding template see Table  3 ). We're pioneers, aren't we? When we built the hotel in there, we were fighting against windmills. Against the politicians, but we always marketed ourselves, we set the tone. We invited journalists and so on. We changed the awareness of tourism a little bit, but that also meant a "C3/R1, 86" etc.
Raising tobacco prices effectively reduces smoking, the main risk factor for chronic obstructive pulmonary disease (COPD). Using the Health Impact Assessment tool "DYNAMO-HIA", this study quantified the reduction in COPD burden that would occur in Italy, England and Sweden over 40 years if tobacco prices were increased by 5%, 10% and 20% over current local prices, with larger increases considered in secondary analyses. A dynamic Markov-based multi-state simulation modelling approach estimated the effect of changes in smoking prevalence states and probabilities of transitioning between smoking states on future smoking prevalence, COPD burden and life expectancy in each country. Data inputs included demographics, smoking prevalences and behaviour and COPD burden from national data resources, large observational cohorts and datasets within DYNAMO-HIA.
13 to 19. 83) and the relative risk of smoking on total mortality by sex (2. 07 for males and 1. 74 for females). The data used and its sources are summarized in Supplementary Table  S6. Most datasets used are integrated within the DYNAMO-HIA tool  16  (further details available: https ://www. dynam o-hia. eu/en/docum ents-and-publi catio ns) with three notable exceptions. First, we used updated information on population size and projected number of newborns obtained from national sources. Second, smoking prevalences were based on recent data from large country-specific population-based observational studies or representative national surveys, as described in the Supplementary File and summarised in Supplementary Table  S6. Third, the probabilities of transitioning between smoking states (initiation/cessation/restart) were calculated per age, sex and region in Europe (Italy considered "South" and England and Sweden both considered "North", corresponding to the United Nations geoscheme and tobacco epidemiology  44  ) using pooled data from six large multicentre observational studies participating in the ALEC consortium, as previously described  5, 23.
Statistical analyses. All possible smoking states were categorized as never, former and current. Additionally, we made the following assumptions: • Smoking could only begin as of 11 years of age, since it has been reported that uptake of smoking at younger ages is unlikely 5 ; • Smoking cessation was only possible as of 16 years of age  23. Restarting smoking was only possible as of 22 years of age. These cut-offs were selected because of the difficulty in generating precise estimates of cessation and restart rates at younger ages because of data sparseness; • As summarized in Table  4 , an increase in tobacco price would affect both the prevalence of never smokers (as fewer people start smoking) and former smokers (as current smokers quit) by the same percentage. Consequently, the prevalence of current smokers was calculated as the complement to 100% of the prevalence of never and former smokers.
We also assumed that the rates of initiation, cessation and restart would be affected by the same percentage. All changes were assumed to occur at the beginning of the simulation (i. e. there is no delay in the delivery of the intervention); • As COPD onset is rarer before age 40 years, COPD prevalence was only considered as of this age  48. Using a simulated population for each year of age (from 0 to 95) and sex, we performed country-specific analyses over a 40-year time period (from 2018 to 2058). Given the long latency period of COPD, this long timeframe was required to allow changes in COPD prevalence due to changes in smoking patterns in younger age groups to be observed. All figures were created using the statistical program R (version 3. 6. 2)  49  except Figure  S10  which was created using the DYNAMO-HIA tool.
Acknowledgements This work was supported by the  Ageing Lungs in European Cohorts (ALEC)  study (https ://www. alecstudy. org), which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 633212. The funding source was not involved in the study design, in the collection, analysis and interpretation of data, the writing of the report and in the decision to submit the article for publication.
Comparing Experimental and Numerical Data The simulation results were compared to the DNA combing data from Platel et al. [35]. The fluorescence intensities for total DNA and replicated tracks of each fiber were measured and binarized on a Matlab ® platform by using a thresholding algorithm. The threshold value was chosen to minimize the difference between the replicated fraction measured by α 32 P-dATP incorporation and by DNA combing. Replicated tracks larger than 1kb were scored as eyes. Gaps were considered significant if >1 kb, otherwise the two adjacent eyes were merged. The eyes whose lengths span from 1 to 3 kb were considered as new origin firing events. The time interval in which these new detectable events can occur was calculated as ∆t = 3 min assuming a constant replication fork velocity of v ≈ 0.
Modeling Experimental Data: Parameters Optimization To estimate the parameters of the model, we fitted the six experimental observables (I( f ), N f ork ( f ), replicated fibre, eye-to-eye distances, eye and gap length distribution) using a genetic optimization algorithm (Matlab ® ). The fitness function was defined as the sum of the square of the differences between experimental and simulated data curves divided by the squared mean of the experimental data curve. The genetic optimization algorithm was set over three subpopulations of 20 individuals with a migration fraction of 0. 1 and a migration interval of 5 steps. Each individual defined a set of variables for the simulation and the variables were chosen within the range reported in Table  1  for the model that best fit the data. At each generation, 3 elite children were selected for the next generation.
Acknowledgements A. D. , A. C. , S. A. , J. R. , N. C. R and F. F. were supported by the  EU Horizon 2020 Research and Innovation Program (Marie Sklodowska-Curie  Grant  642870 , ETN-Immunoshape). N. C. R additionally acknowledges funding from the  Ministry of Science and Education (MINECO)  Grant No. CTQ2017-90039-R  and  RTC-2017-6126-1  and the  Maria de Maeztu Units of Excellence Program  from the  Spanish State Research Agency -Grant No. MDM-2017-0720. Also, J. R. acknowledges funding the  MINECO , Grant No. CTQ2017-86265-P  and  ISCIII RETICS ARADyAL  ( RD16/0006/0011 ). These grants were co-funded by the  European Regional Development Fund (ERDF). This work used the  Multistep Protein Purification Platform of the Grenoble Instruct-ERIC center (ISBG ;  UMS 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology , supported by  FRISBI  ( ANR-10-INBS-05-02 ) and GRAL, within the  Univ.
Michael Arad reports receiving an Advisory board fee and a  Research Grant  from  Pfizer  and from  Sanofi Genzyme. Urs Eriksson  reports consulting fees and grant support from  Pfizer  and  Sanofi. Institutional grants from  Pfizer. Marianna Fontana  is supported by a  British Heart Foundation   Intermediate Clinical Research Fellowship  ( FS/18/21/33447 ) and reports consulting fees from  Pfizer ,  Akcea, Ionis, Alnylam, Alexion ,  Sanofi  and research grant from  Pfizer. Martha Grogan  reports grant/clinical trial support from  Alnylam, Eidos ,  Prothena , and  Pfizer. reports grant support from  National Institutes of Health  [ R01HL139671-01 ], [R21AG058348] and [K24AG036778], consulting income from  Pfizer ,  GSK ,  EIdos ,  Prothena ,  Akcea and Alnylam , and institution received clinical trial funding from  Pfizer ,  Prothena ,  Eidos  and  Alnylam Claudio Rapezzi  reports speaking fees from  Pfizer ,  Alnylam , and  Akcea.
composed of 20 mM ammonium acetate, pH adjusted to 5. 6 with acetic acid (mobile phase A) and 140 mM ammonium acetate and 10 mM ammonium bicarbonate pH 7. 4 (mobile phase B) was prepared  [ 26 ]. Second, an in-house buffer was used with a final composition of 10 mM ammonium acetate and 10 mM acetic acid (mobile phase A) and 50 mM ammonium acetate and 25 mM ammonium carbonate (mobile phase B). Last, the commercial Ion-Hance CX-MS pH concentrate A and B were diluted 10 times with Milli-Q water to reach a composition of 50 mM ammonium acetate with 2% of acetonitrile, titrated to pH 5. 0 (mobile phase A) and 160 mM ammonium acetate with 2% of acetonitrile, titrated to pH 8. 5 (mobile phase B). This dilution procedure is suggested by the vendor  [ 30 ]. Instrument control and data acquisition were performed by Empower Pro 3 software (Waters).
With the MabPac column, gradients were 35% to 55% B for elotuzumab, 45% to 55% B for trastuzumab and infliximab, and 55% to 75% B for NIST mAb, dalotuzumab and adalimumab. The mass spectrometer was used in ESI positive mode with an acquisition range of 400 to 7000 m/z. The system was calibrated by using a 200 pg/μL sodium iodide solution diluted in a mixture of water/isopropanol 50/50 (v/v) with 0. 1% FA. For mAb analysis in their native state, the desolvation temperature was set at 350 °C, source temperature at 120 °C, the cone voltage to 150 V and the capillary voltage to 1. 5 kV. UNIFI was used as data acquisition software. Native protein mass spectra data treatment was performed with MassLynx software (Waters).
Acknowledgements The authors wish to thank  Henri Kornmann  and  Kudzai Mutenda  ( Ferring Pharmaceuticals, Saint-Prex, Switzerland ), for the financial support of this work, and  Jean-Luc Veuthey  from the  University of Geneva  for his fruitful comments and discussions. Rowan Moore  and  Guillaume Bechade  ( Waters Corporation ) are also acknowledged for the loan of the BioAccord system used in this work.
It is common to populate models with in-vitro kinetic constants  [9, 10]. Although online databases such as BRENDA  [11, 12]  collect and aggregate many of these measured parameters, large gaps remain when dealing with larger models or relatively unstudied organisms. An alternative, suggested by Heckmann et al. [13] , is to infer unknown k cat values by machine learning. Alternatively, one can estimate in-vivo kinetic constants directly from flux  [14] , metabolite, and enzyme data  [15, 16] , or use the kinetic model itself to fit its parameters to collected data  [17] [18] [19]. On the flip side, too much data on kinetic parameters can also pose a problem. For instance, Wegscheider and Haldane conditions (which are derived directly from the laws of thermodynamics) impose strict dependencies between equilibrium and kinetic constants and are likely to be violated when all the parameters are measured independently  [1].
Applied to E. coli data, this method was able to provide relatively precise estimates of k cat values when compared to literature data (from the BRENDA database). The simplicity of the method makes it scalable and easily applicable to any organism whose metabolic network has been reconstructed. However, there are a few severe limitations. First, due to the max function, a single large outlier value can completely distort the result. Such outliers may arise if measured protein concentrations are small and imprecise. Another caveat is that an enzyme may stay below its maximal capacity in all of the samples, especially if the number of measured metabolic states is small or when considering complex (compartmentalised) cells. This could explain why the correlation between max s k s app and literature k cat values was lower for plants than for E.
In the original problem (top), it is non-convex; with a lower α (centre), the enzyme terms are non-convex, but the total score is convex due to the convex term for x; and in the truncated α = 0 version, all terms are convex and so the total score is known to be convex as well. (a). By further adding the metabolite term (which is strictly convex), we obtain the posterior score (dashed line), which is non-convex. In the case of α = 0. 2 (dark blue), the enzyme term is still non-convex, but the total posterior score becomes convex. In the case α = 0, the enzyme term is convex, and the total posterior score is convex as well. Implementations for Matlab and Python are freely available (see Supplementary Materials and  [48] ). As a test case for model balancing, we consider a model of E. coli central metabolism and corresponding metabolite, enzyme, and kinetic data from  [43]  (for details, see  [48] ).
We considered different scenarios (Figure  2  in Supplementary Materials) in which variables were either fitted (metabolite and enzyme concentrations, and kinetic constants given as data) or predicted from the other data ("unknown" kinetic constants). Figures  3  and 4  show the model balancing results with artificial data and different combinations of noise-free or noisy kinetic and noise-free or noisy state data. We chose a stringency value of α = 0. 5 (results for other stringency values can be found on github. com/liebermeister/model-balancing; accessed on 20 october 2021). Results for noise-free kinetic and state data are shown in Figure  3. Scatter plots compare true and fitted variables (metabolite concentrations, enzyme concentrations, and different types of kinetic constants). Deviations from the diagonal line indicate estimation errors.
The reconstructions are perfect, with small deviations arising from conflicts between priors and data. The other rows show estimation results using equilibrium constants only (centre row) or using no kinetic data at all (bottom row). Metabolite and enzyme data were used in all cases, either without or with noise. The predictions of k cat and K M values from complete and noise-free data are rather good. To assess the effect of noise, we next used state data (metabolite concentrations, enzyme concentrations, and fluxes) with a noise level of about 20 percent (geometric standard deviation 1. 2) and noisy kinetic data, with a geometric standard deviation of 1. 5. With noisy data, the estimates become worse (Figure  4f ), and especially K M values are predicted poorly. Using equilibrium constants as data improves the results, and k cat values can be reconstructed more reliably (Figure  4d, e ).
With these constants used as data (top row, red dots), slight adjustments were sufficient to obtain a consistent kinetic model. Using only equilibrium constants as data (centre row) yields reasonable predictions of the k cat values. In the bottom row, both equilibrium constants and other kinetic constants were predicted, but the correspondence to in-vitro values is relatively small. Results for E. coli central metabolism with experimental data (aerobic growth on glucose). The kinetic data stem from previous parameter balancing based on in-vitro data. Top: estimation using kinetic data. Centre: model fit using equilibrium constants as data. Bottom: estimation without usage of kinetic data. Metabolite, enzyme, and kinetic data were taken from  [43]. A stringency parameter α = 0. 5 was used. The fact that our kinetic data were obtained from parameter balancing, based on the same network model and the same priors, leads to a bias.
33 Figure 5. 335 Figure 5. Results for E. coli central metabolism with experimental data (aerobic growth on glucose). The kinetic data stem from previous parameter balancing based on in-vitro data. Top: estimation using kinetic data. Centre: model fit using equilibrium constants as data. Bottom: estimation without usage of kinetic data. Metabolite, enzyme, and kinetic data were taken from [43]. A stringency parameter α = 0. 5 was used. The fact that our kinetic data were obtained from parameter balancing, based on the same network model and the same priors, leads to a bias. However, a test with original in-vitro kinetic data (and thus fewer data points, not shown) yielded similar results. Predicted kinetic constants may be validated by cross-validation; the results can be expected to lie in between the results from our "fitting" (dots) and "prediction" scenarios (crosses).
g. , Danionella, fruit flies, wasps, and mosquitoes) or localized pigments scattered across the structures are consequently challenging to measure. Here we built a simple and versatile measurement setup (i. e. , a tissue spectrometer) to measure the ballistic and total transmission of samples customizable for different wavelengths, spatial resolutions, and sample sizes. We measured the ballistic and total transmission with a photodetector coupled to a single-mode fiber (SMF) and an integrating sphere, respectively. We verified that the SMF effectively isolates ballistic photons by measuring tissue phantoms of known optical properties, and then comparing the results with theoretical predictions and measurements from a typical double-aperture setup  [18]. We built a processing pipeline to calculate the transmission map of each sample and extract transmission data from different regions of interest (ROIs).
I. DATA ACQUISITION Data acquired were spatially resolved power measurements inside and outside the sample, denoted as P and P 0, respectively (Fig. 1 inset ). They were processed with a three-step pipeline (Fig. 1(C )) using a custom script written in Matlab (Matlab 2020b, Mathworks): first, P was normalized to P 0 (see 2. 6 for more details), forming a transmission map; second, transmission maps measured at different wavelengths were aligned with image registration using similarity transformation; third, ROIs were segmented manually to extract transmission data of distinguishable features on the sample.
1-mm-thick deionized water (BT121925-4L, VWR) was used (Fig. 2(B) ). Ballistic transmission measurement results from the tissue spectrometer were compared at five wavelengths (911 nm, 1056nm, 1300 nm, 1552 nm, 1624 nm) to both calculated values using Mie scattering and measurement results from a double-aperture setup (Fig. 2(C) ). Mie function developed in Matlab  [19]  was used to simulate the ballistic transmission of the tissue phantoms. The double-aperture setup used the beam collimated by L1 and followed by four 2 mm apertures in separations of 100mm, 90mm, and 100mm. The aperture separations provided an acceptance half-angle of 0. 395°. The sample was placed between the second and the third aperture. A power meter (S132C, Thorlabs) was placed immediately after the fourth aperture. Four tissue phantoms were used for the comparisons: two 1-mm-thick phantoms with 2-µm-diameter polystyrene beads at a concentration of 2.
Samples from three individual animals were measured (N = 3), and the number of spatially resolved data points acquired was also indicated (n). The animal-to-animal variation is minimal. It is indicated in the violin plots (Fig. 4  and 5 ), showing unimodality for most distributions. The mean of transmission from each animal was indicated as crosses in the plot. Transmission through distinguishable ROIs in the samples was shown separately to account for the differences in optical transmission. "Non-pigment" showed the transmission of the non-pigmented areas, while "pigment" showed the transmission of pigmented areas. "Cau" and "Ros" showed the transmission of areas caudal and rostral from ocelli, respectively. We further compare the ex-vivo measurements in this study to the in-vivo and ex-vivo measurements reported in a previous study in the case of mouse skull.
Acknowledgments. We thank members of the  Xu research group  for valuable discussions and technical suggestions. We thank  Dr. Bo Li  for his help setting up the laser sources. We thank  Prof. Andrew Bass ,  Prof. Laura Harrington  and  Prof. Michael Sheehan  for providing the Danionella, mosquitos and wasps. We thank  Prof. Nozomi Nishimura ,  Dr. Chi Yong Eom , and  Christopher Scott Brunkhorst  for their advice in rat dura removal. We thank  Dr. Jonathan Perelmuter  and  Najva Akbari  for their discussion on Danionella skin removal and mounting. This work used the  Cornell Center for Materials Research Shared Facilities  supported through the  NSF MRSEC program. Disclosures. The authors declare that there are no conflicts of interested related to this article.
PET-CT Analysis Injected dose, time between injection and acquisition and capillary glycaemia were recorded to seek EANM recommendations fulfilment  (20). A single observer delineated volumes of interest (VOIs) that encompassed the entire breast tumor by using a gradient-based method implemented in MIM software (MIM software, version 5. 6. 5). When multiple lesions were depicted, only the biggest lesion was considered. VOIs were then saved as DICOM RT structures and loaded in LifeX v5. 10 software (21) (www. lifexsoft. org) to extract SUV max , histogram parameters and the following TFs: -Inverse difference, angular second moment, variance, correlation, entropy, dissimilarity from gray-level cooccurrence matrix (GLCM) that considers the arrangements of pairs of voxels -coarseness, contrast and busyness from neighborhood graylevel different matrix (NGLDM) that corresponds to the difference of gray-level between one voxel and its 26 neighbors in 3 dimensions.
Digital-Immunochemistry Computation The ScanScope CS microscope slide scanner (Leica Biosystems) was used to digitize whole slide images of histological sections at 20 × (0. 5 µm/pixel) and record them as tiled tiff images. For each image, regions of interest (ROIs) were drawn using the ImageScope software (Leica Biosystems) in order to select only tumor tissues and remove the artifacts. The images were processed as reported in the previous study  (25). Briefly, squares of 2000 pixels size corresponding to 1 mm 2 area were used in this study. The squares were generated to fit the area of the ROI. A ratio between the stained area (brown color) and the surface of tissue was computed and assigned to each square based on their coordinates. Local ratio computed for each square was ranked according to the following ten intervals: level 0 (0-10%), level 1 (>10-20%), level 2 (>20-30%), level 3 (>30-40%), level 4 (>40-50%), level 5 (>50-60%), level 6 (>60-70%), level 7 (>70-80%), level 8 (>80-90%), and level 9 (>90-100%).
The importance of variables in classification was assessed by measuring the mean decrease accuracy (31) of class prediction. Variables of importance were compared between 8y-EFS 0 and 8y-EFS 1 groups using non-parametric Mann-Whitney tests. Receiving operating characteristics (ROC) analyses for 8y-EFS were then undertaken on variables identified as significantly different between groups to define optimal cut-off values based on the Youden index. Eight-year EFS analyses were finally performed using univariable Kaplan-Meier analyses, log-rank tests for comparison of survival curves and finally multivariable Cox regression analysis. The end-point used for survival analysis was the time from diagnosis until relapse or progression, unplanned retreatment, or death as a result of breast cancer. When appropriate, non-parametric Mann-Whitney tests and Spearman correlation tests were used to explore the relationship between clinical data and tumoral heterogeneity variables.