dataesr/scientific-paragraphs-categorization huggingface dataset:
Owing to the rapid growth of biomedical literature in recent years, it is feasible to automatically extract knowledge from the published papers, such as the drugs and diseases they mention and their relations in a large scale  [20, 21]. The knowledge found can be used in many biomedical related fields such as drug discovery, safety monitoring, and drug side-effect detection  [22, 23]. Manually identifying such entities can be very accurate, but is also time consuming and laborious. Developing automatic annotation systems based on Natural Language Processing (NLP) technology is an alternate method to identify drug and disease entities in text  [24]. It may be applied in large scale and provide a way to discover relations among phenotypes and off target effects. In this paper, a pharmacological knowledge mining strategy iss proposed in the form of hybrid phenotypic mining by combining Biomedical Natural Language Processing (BioNLP) with medical informatics.
Therefore, the CDR region sequence of the light and heavy chain of the drug protein sequence was used in this research for BlastP alignment, which consisted of ∼240 amino acids. Taking Pembrolizumab as an example, the amino acid sequences of the heavy and light chains are shown in the Fig. 2. The antibody consists of 4 strands, and the Fab segment is the part that can specifically recognize the antigen. Heavy Chain Sequence As training data for phenotype extraction, we downloaded the gold dataset of the TAC 2017 shared task on adverse reaction extraction  [28] , which consists of 100 drug labels and annotated adverse reactions. Meanwhile, the drug labels of 5 targeted drugs were downloaded from DailyMed  [29]  (https://dailymed. nlm. nih. gov/dailymed/) in XML format.
Omics data sets Several multi-Omics data sets were used in this research. i) HPO. Human phenotype ontology (HPO) contains standardized vocabulary for phenotypic abnormalities in human diseases  [30]. ii) UniproKB. We used the local BlastP  [31]  and protein database UniprotKB  [32]  to obtain homologous sequences of five drugs. iii) STRING. Protein-protein interaction (PPI) analysis helps to study the molecular mechanisms of disease and discover new drug targets. In this paper, antibody-producing proteins may be obtained from the STRING database.
Extraction module of target-centric phenotypes. As shown in the right module section in Fig. 3 , target-centric phenotype terms were extracted from a series of database queries, including BlastP, STRING, and HPO. Step-1: Sequence similarity computation. We used the native BlastP and protein database UniprotKB to obtain homologous sequences of the five drugs. The parameters were set to: Indentities> 30% and E-value > 10 -e4. Step-2: PPI extension for searching target with function relevance. We entered the protein name in the BlastP result into the STRING database, setting the confidence parameter to 0. 7 and the maximum number of display nodes to 500.
Step-3: Target-centric phenotypes extraction with HPO query. We downloaded the HPO database and compared the previous protein interaction results with the HPO database. We thus obtained the side effect phenotype of each drug, and predicted the most likely drug off-target site. Screening module for off-target proteins. A Gene Ontology (GO) analysis is performed using the obtained gene list information. We analyzed the signal pathways involved in each gene to screen for genes related to T cell proliferation activation, immune regulation, such as AKT and PI3K pathways, and matched the genes by crosschecking. Eventually, we performed literature search to find evidence supporting their relevance.
Results for drug-centric phenotypes extraction The precision (proportion of correctly predicted entities), recall (proportion of gold entities actually predicted) and F-score (harmonic mean of precision and recall) obtained on the training data for each entity type are shown in Table  2. After training, the model was used to extract all adverse reactions from the drug labels of the five targeted drugs. The extraction results are shown in Fig. 4. As can be seen from the figure, many side effects are extracted for each drug. An examination of the results shows many synonyms, which need to be further screened. A total of 905 drug side effects were extracted from the five drug labels.
Phenotypic mining strategy on finding BTK The screening process of BTK discovery was represented in Fig. 3  with respect to the numbers of hit counts for drugs, phenotypes and candidate core proteins. First, the module of drug-centric phenotype extraction started with five PD-1/PD-L1 inhibitors, searched the DailyMed database for related side-effect phenotypes, and used a CRF for sequence labeling to mine 905 drug-centric side effects. The precision of that process was 88% on the training data, so a moderate amount of noise is expected. This noise should however be reduced by the intersection with target-centric phenotype extraction. Conversely, recall was 68%, meaning that about one third of the drug side effects are likely to be missed. Second, in the module of target-centric phenotype extraction, a total of 848 amino acid sequences were found to be similar with sequences of the five PD-1/PD-L1 inhibitors by using blastP.
Subsequently, a total of 134 target proteins of drug were found in the STRING database. Finally, the 134 proteins were compared with the HPO database, and a total of 2,071 target-centric side-effect phenotypes were found. Third, in the screening module for off-target proteins, the phenotypes of the first two steps were cross-matched, and a total of 110 target-centered target proteins were found. For more accurate screening, we performed GO analysis and found genes related to AKT and PI3K pathways. A total of three were found: ACTG2, AKT1 and BTK. Since only BTK related literature reports were found during the literature search, only one gene related to PD-1/PD-L1 off-target was found.
Acknowledgements Xiang-Yu Meng  is supported by a fellowship from  ITMO Cancer AVIESAN  within the framework of Cancer Plan. The authors would also like to express their gratitudes to  Mr. Kaiyin Zhou  and  Mr. Sheng Zhang  who offered help in the CRF coding and terms matching, as well as participants in the  HZAU BioNLP  seminar for their attentions and discussions,including  Mr. Chao Zhao ,  Mr. Jiali Ma ,  Ms. Mochi Wei , etc.
Transport The COXINEL line was designed to enable wide tuning and manipulation of the electron beam. Thanks to the tunable high gradient QUAPEVAs, different beam optics can be adopted. Electron beam transport is tuned with BETA code  [55]  up to the second order, with home-made multiparticle tracking code for high order non-linear effects and collective effects like coherent synchrotron radiation  [56]. Hard-edge models are used for the magnets and apertures of the vacuum chamber along the beam line are included. A linear optics from source to image  [39]  is implemented. This optics ensures a minimum beam size (waist) for the on-momentum particles (energy deviation δ = 0) at the focusing point (undulator center). By neglecting the coupling effect and limiting to the horizontal plane and chromatic terms, the transfer matrix from source to image can be expanded up to the second order as  [57] : x x = R 11 R 12 R 21 R 22 + δ R 116 R 126 R 216 R 226 x 0 x 0 , (9) where the matrix R ij6 stands for the chromatic second order perturbation.
Photon Beam Flux and Brilliance The photon flux Ṅ at the first harmonic generated by an electron beam orbitting an undulator can be expressed as  [61] : Ṅ = 1. 43 × 10 14 N u IK 2 u |J J| 2 1 + K 2 u /2. ( 24 ) The flux calculated in the case of COXINEL is 1. 3 × 10 19 ph/s/0. 1% for a K u value of 2. The flux is also simulated, for the total beam (blue) and slice beam (red) cases using an SRWcode  [62]  as shown in Figure  2. The flux is peaked at the resonant wavelength of 176 nm. The difference between the analytical value and the simulated one is due to the small aperture of the observation window. If one uses a larger aperture, the flux attains a value of 1. 5 × 10 19 ph/s/0. 1% bw close to the calculated one. In addition, the relative bandwidth for the total beam and slice beam are found to be 4.
1. The main finding has been that for both CO2 and H2Scontained systems, an increase in total pressure leads to increase in the acid gas partial pressure. To our best knowledge, there are no data reported for the system CH4-H2S-MDEA-H2O and MDEA solutions with concentrations higher than 50 wt. % MDEA-H2O. A 50. 1 wt. % MDEA-H2O and a 70 wt. % MDEA-H2O system were used in this work to obtain vapor-liquid equilibrium data (VLE) with hydrogen sulfide and methane. The new VLE data for the system CH4-H2S-MDEA-H2O with 70 wt. % MDEA-H2O mixtures were obtained at temperatures of approximately 283, 353 and 393 K and pressures of 2000, 6000 and 10000 kPa. The experiments were performed isothermally and the temperature of 283 K was chosen to simulate the low-temperature subsea conditions while the temperature of 393 K was chosen to simulate the high regeneration temperature.
During the evaluation of the data, a sharp increase of partial pressure at loadings > 0. 7 mol H2S/mol MDEA was noticed, resulting in a cross-over of literature data reported for 35 wt. % and 50 wt. % MDEA-H2O solutions. For this reason, the data from Li and Shen  [13]  were not included in our database used in the model parametrization, as chosen also by Huttenhuis et al. [20]. Jou and coworkers  [12] ,  [21]  have published experimental data for a 48. 9 wt. % and for a 35 wt. % MDEA solution. Two observations can be made for the low loading region: a) the data with a 35 wt. %  [21]  and a 48. 9 wt. %  [12]  MDEA solution are very similar and b) the deviations between the data with a 48. 9 wt. % and a 50 wt. % solution look larger than what one would expect with such similar concentrations.
The variable-speed stirrer is composed by a rotating axis inside the cell, two propellers mounted on the rotating axis for stirring both the gas phase and the liquid phase and a magnetic rod mounted on the rotating axis in order to allow for rotation of the axis by a stirring motor located below the cell. Agilent software BenchLink is used for online monitoring of pressure and temperature, enabling the determination of equilibrium. Once the equilibrium is reached, micro samples can be withdrawn and transferred to the GC for analysis. Automatic sampling is allowed through two capillary samplers (ROLSI ® ) Armines' patent  [44]. Two capillaries are fixed in the cylindrical wall of the cell at levels designed to withdraw vapor and liquid phase samples. The samplers are connected to a PERICHROM model PR-2100 gas chromatograph, through a heated transfer line.
The desired global loading, i. e. mol of H2S inside the cell per mol of amine, was first decided and based on the PVT conditions of the H2S gas tank before and after the filling of the cell, the amount of H2S introduced was determined. The calculations were performed using REFPROP software  [45]  and a Helmholtz energy-based equation of state developed by  [46]  for pure H2S was used. The global loading was, thus, calculated.
The analysis of vapor phase concentration was performed in a GC equipped with a Porapak-R column R80/100 mesh (length 2 m, diameter 2 mm) from RESTEK. The carrier gas was helium at a flow rate of 20 ml/min. A constant temperature program at 363 K was used for the quantification of both methane and hydrogen sulfide. Analysis at 383 K was also performed to check for water presence in the vapor phase. In order to check the repeatability of the measurements and to perform uncertainty analysis, five samples at least were withdrawn, the first two of them usually were required to saturate the transfer lines in terms of adsorption. Disturbance to equilibrium was considered negligible due to the small volume of each sample. Knowing the pressure, temperature and the composition of the vapor phase, the density of the vapor phase was estimated using REFPROP software  [45].
Thermodynamic modeling High pressure VLE. An in-house MATLAB-based rigorous model has been developed to describe the chemical and phase equilibrium for the system H2S-MDEA-H2O. The same algorithm has been previously used to successfully describe CO2-amine-H2O systems relevant to carbon capture processes  [47] ,  [48]. Peng-Robinson EoS  [49]  with the original alpha function was employed to describe the non-idealities of the vapor phase, coupled with the traditional van der Waals one-fluid mixing rules. The binary interaction parameters for Peng-Robinson EoS in this work were set to zero. To account for the non-idealities in the liquid phase, the electrolyte non-random two-liquid (eNRTL) model  [50]  was utilized. The models are presented in the Appendix. The required critical parameters and acentric factors for pure components are given in Supplementary Information.
In Table  5  and Table  6  the parameters are provided only with their significant digits. The adjustable parameters for the eNRTL model are the non-randomness factors, α, and the energy parameters, τij. The optimization of the H2S-MDEA-H2O system requires the regression of a total of 78 parameters. In order to reduce this high number of parameters to be adjusted, the following steps have been taken: I) All non-randomness factors α have been given fixed values according to Table  7. II) The energy parameters for the subsystem H2S-H2O have been fixed to the default values used in Aspen Plus V10 simulation software (Table  7 ). III) The energy parameters for the subsystem MDEA-H2O have been fixed to the values obtained by the regression of the literature data presented in Subsection 2. 2 MDEA-H2O system.
However, including those data leads to modeled vapor pressures with only the slightly higher AARD of 5%. The fixed parameters in Table  7  and the regressed parameters for the binary subsystem MDEA-H2O (Supplementary Information) were used for the regression of the ternary system H2S-MDEA-H2O. Initially, all the data from Table  1  were used for the parametrization of the model, except for the data from Li and Shen, as well as the data in the presence of methane (Case A). The data obtained in the presence of nitrogen were all included. The scatter already discussed earlier at low loadings resulted in high AARD, especially for the data points reported in terms of partial pressure of H2S. The high deviations are also attributed to the much lower values of partial pressures in comparison with total ones, leading to higher relative numbers.
Conclusions Experimental vapor-liquid equilibrium data were measured for a 50. 1 wt. % aqueous MDEA at temperature of 323 K and pressure up to 3000 kPa as well as a 70 wt. % aqueous MDEA at temperature of 283 K, 353 K and 323 K and pressures up to 10000 kPa, due to their relevance for subsea H2S removal of natural gas. Therefore, methane was used as makeup gas. The experimental data indicate that the effect of total pressure on the liquid loading of the solvent is within the experimental uncertainties, while for the 50 wt. % MDEA-H2O system the impact on the partial pressure of hydrogen sulfide is attributed to the non-idealities of the vapor phase and it is lower with decreasing total pressure. The system H2S-MDEA-H2O up to 70 wt. %.
This paper presents a rst experiment with random generator of drug prices and a rst simulation on physicians' treatment choices (case on pharmacotherapies) for diabetes type II care. It also aims to compare the effects of the price variables according to public versus private health plans on physicians' choices (Medicare versus commercial Health Plans). The base line model used is a Mixed Logit model with Random Price variables. A series of experiments with random parameters generations is designed with various sequences and number of draws. The model is tested on a real analytical dataset, extracted from the CDC physician survey (National Ambulatory Care Survey, NAMCS), for patients with diabetes type II without complications, for previous predictive econometrics with ENDEPUSresearch, Inc.
The model uses a rst drug choice set with three alternatives: oral agents only, combined therapies, no drug. The choice models introduce qualitative dependent variables and complement the series of cumulative logistic models per disease. The matlab code for the new speci cation test on the Independence of Irrelevant Alternatives at individual level is modi ed to t this type of medical applications; rst runs compare main parameters of a full choice set versus reduced choice sets of alternatives. It is planned to design more experiments for extended choice sets and widespread applications, in order to lead to user friendly tools for medical systems. The collaboration with Professor Jerry Hausman on the US market will help with use of results and new ways to adjust the reliability on the selection of alternatives; it may provide additional guidance to the algorithms used by professionals and for health policies.
Introduction: This paper continues the development of an economic model for medical markets, including physicians' choices models, with data on the US market; it provides a rst series of results on a simulation approach, using a mixed logit model; the medical market case is the diabetic case. Data are extracted from a CDC physician survey, and models are run on an analytical database designed by Prof C Huttin and her coworkers, D Wong and S Atwood  [1, 2] , under her company, Endepusresearch, Inc and during visits to Harvard University (especially for analysis of ICT diffusion  [3] ). A new speci cation test to address the property of Independence of Irrelevant Alternatives (IAA) at an individual level has been developed by Prof Hausman and his co-authors  [4]  for mixed logit choice models and a variant of the mixed logit model  (Berry, Levinsohn et  Pakes (BLP) model  [5] ).
So far, no empirical study has been performed on physicians' choices and real data. This paper provides preliminary ndings on a mixed logit model on physicians' choices, with experiment on random drug prices. In this health care application, the new test may help to determine whether physicians' choices models are well speci ed for this dataset on Type II diabetes without complications. Mixed logit models have already been developed in order to relax the Independent of Irrelevant Alternatives property (IAA)  [6] , but this property still holds at the individual level and the research proposed by Prof J Hausman and his collaborators, aims to address it, with an economic model speci ed by Burda, Harding and Hausman  [7]. This will overcome the limitations of nested logit models and may address in this case, all types of physicians' choices, with and without the IIA property.
I. Review Of First Steps On The Development Of A Physician's Choice Model: Prof C Huttin started this collaboration in 2017, with an invitation at MIT economic department to design physicians' choice models and transform the datasets she previously generated, under her company and research services provided by Harvard Medical school (Countway Medicine library). The data management step is described in the rst working paper from December 2018  [8]. It especially aimed to transform the selected dataset on diabetes, into a long form for qualitative choice models, to impute new random price variables, in order to shift the models from cumulative logistic or partial odd ratio models to mixed logit models. The command asmixlogit in Stata IC15 was used to build this economic model, the results presented at Ispor  [9]  help to guide the simulation approach, described in the next section of this paper, to implement the new speci cation test for a mixed logit model on the selected diabetic dataset.
The rst runs estimated parameters to obtain the convergence of the mixed logit model, using some predictors from the previous studies with cumulative logistic models  [10]. The current simulation presented in this paper, implements necessary computations for this test, using both Stata license and Matlab software for the test, on the same dataset as previous models. This diabetes care case is particularly timely in North America, with the current insulin crisis  [11]  and the policy proposals to import drugs from Canada  [12, 13]. The development of this economic model addresses the heterogeneity of physicians' choices on a medical market, at the interaction of supply and demand. Usually, clinical choices are analyzed mostly from the supply side, within the medical system, with a speci c concept of induced supply; this concept of inducement also applies on the demand side; more recent research from the principal agent literature on asymmetry of information uses the concept of induced demand.
Description Of The Baseline Model On Mixed Logit And Modi cations For The Simulation On The Diabetic Dataset: The current model development aims to use a different approach. It is a mixed logit model with random prices for drugs and procedures. The dependent variables are physicians' qualitative choices, among various combinations/considerations of choice sets. Independent variables combine product and patient characteristics, to analyze market adjustment of supply and demand. In the rst-choice experiments for the baseline mixed logit, drug prices are product attributes, they are generated with random-numbers sequences and patients' characteristics are case speci c variables. This type of model is mainly coming from business research. The base line model was implemented in a computer code, written by Dr J Lustig; the runs provide simulations of consumers' choice sets on a limited number of product characteristics.
Originally, the code was written for a case scenario of 1000 consumers, three product characteristics and combinations of ten choice sets. The implementation of this test requires a comparison between combinations of three alternatives and a comparison of a reduced choice set, removing one alternative out of the three. This paper describes the modi cations from the baseline model for the application on the selected medical market case: diabetes type II, without complications. This rst experiment includes only one choice set: a drug choice set, while the original code could allow up to ten choice sets. This case study on physicians' choices, rst needs to be described to ensure that program modi cations to implement the new test, cover the various issues added to the baseline model  [4] : The mixed logit model run with Stata, is not for consumers choices since the decision maker is the physician, who decides for or with the patient, different treatments and according to different patients' clinical and socio economic characteristics (so both product and patient' attributes) Physicians 'choices are among alternatives on treatment choices for diabetic type II, this requires also decisions on classi cations of drugs, ingredients, procedures for each alternative (usually dependent on clinical guidelines) This case scenario is on drug choices only, and the choice set includes three alternatives, including the "no drug" alternative; the grouping is presented in the next section with descriptive statistics of the sample Drug prices and the Interaction variable: Drug price for Medicare (the main federal public plan versus commercial plans) are random variables Three patient characteristics: age, obesity and sex are case speci c variables for this simulation Therefore, the model for this medical market case includes two random variables on products and three variables on patients.