dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Collection Together with the NPi, pupil size and constriction rate (CH) for both eyes on admission, we also collected data on demographics, comorbid diseases, mechanism of trauma, ICU and hospital length of stay, ICU and hospital mortality. The severity of disease on admission was estimated using the Sequential Organ Failure Assessment (SOFA) score  [18]. TBI severity was estimated using the Glasgow Coma Scale (GCS) on admission  [19]  and the Marshall scale on the initial cerebral CT-scan  [20]. The use of different therapies (i. e. , mechanical ventilation, sedative, analgesic, vasopressor, inotrope, antiepileptic, barbituric and/or osmotic drugs), as well as different interventions (i. e. , ICP monitoring, hypothermia, hypocapnia and decompressive craniotomy) was collected, together with relevant biochemical and physiological parameters.
Statistical Analyses Data were analyzed using R statistical software version 4. 0. 3 (R Foundation for Statistical Computing, Vienna, Austria), Prism (GraphPad Software Inc. , San Diego, CA, USA) and IBM SPSS Statistics for Macintosh 27 (Armonk, NY, USA). Discrete variables were expressed as count (percentage) and continuous variables as median (25th to 75th percentiles). The Kolmogorov-Smirnov test was used, and histograms and normal-quantile plots were examined to verify the normality of distribution of continuous variables. Demographics, clinical, and pupillary patterns differences between groups (UO vs. FO; survivors vs. non-survivors) were assessed using the chi-square test or Fisher's exact test for categorical variables and Mann-Whitney U-test or t Student test were used for continuous variables, as appropriate.
01). A statistically significant difference in the worst and mean NPi on admission was observed across different TIL values (Figure  3 ; p = 0. 01). S3  and S4. Also, worst and mean NPi on admission were significantly different analyzed across different predicted UO and mortality rates according to the TBI-IMPACT Database (Figures  4  and 5 ). S3  and S4. Also, worst and mean NPi on admission were significantly different when analyzed across different predicted UO and mortality rates according to the TBI-IMPACT Database (Figures  4  and 5 ). A statistically significant difference in the worst and mean NPi on admission was observed across different TIL values (Figure  3 ; p = 0. 01). S3  and S4. Also, worst and mean NPi on admission were significantly different when analyzed across different predicted UO and mortality rates according to the TBI-IMPACT Database (Figures  4  and 5 ).
Of note, multiple fragments (30-32 unique peptides) of Jak1 were identified with high abundance in all 3 independent biological replicates of the CALM-AF10 dataset and none in IgG controls (Fig. S3A  and Table  S4 ). To confirm the AF10 FP-JAK1 interaction in AML cells, we used immunoprecipitation (IP) of CALM-AF10 or MLL-AF10 FPs using Flag antibodies (Fig. 4C  and Fig. S3B ). Reciprocal co-IP using antibodies targeting endogenous Jak1 further confirmed the interaction (Fig. S3C ). We then asked how AF10 FP expression impacted Jak1 protein mechanistically. Expression of either CALM-AF10 or MLL-AF10 was associated with higher levels of Jak1 protein compared to AF10 FP-Off AML cells (Fig. 4E ), although JAK1 mRNA levels were unchanged (Fig. S3D ). This observation suggests that the presence of AF10 FPs stabilizes Jak1 and protects it from degradation.
Despite a strong rationale for targeting tumorpromoting fusion oncoproteins in cancer, most of these oncoproteins remain difficult drug targets. One way of circumventing this problem is by identifying downstream transcriptional or signaling networks activated by these FPs and targeting these networks instead. In this study we developed mouse models where the key AF10 fusion oncoproteins CALM-AF10 and MLL-AF10 can be rapidly switched off, enabling the systematic mapping of genetic and biochemical networks driven by AF10 FPs. In addition to these transcriptomic studies, our extensive characterization of the AF10 FP interactome helps expand the molecular network of AF10-R leukemia, which will help in understanding mechanisms of AF10-R leukemia that have so far remained elusive. More importantly, these datasets on AF10-R leukemia will help identify and prioritize therapeutically targetable candidates in the disease.
Cells ware harvested at initial (day 3 post-puromycin selection) and final (20 population doubling times after the initial) time points. Extraction of genomic DNA was performed using the Zymo Quick-DNA™ Miniprep Plus Kit (Zymo Research, Cat. D4068) according to manufacturer's instructions. Two-step PCR-amplification and sequencing library preparation was conducted with TaKaRa Ex Taq™ Polymerase (TaKara, Cat. RR001) and custom primers to achieve adequate sequencing coverage in 1x75bp single-end reads, following published guidelines  (39). Barcoded libraries were pooled and sequenced using Hiseq 500 (Illumina). The sequence data were de-multiplexed and analyzed using PinAPL-py  (42).
ACKNOWLEDGEMENTS The authors gratefully acknowledge  Dr. Brian James  for valuable assistance with the project. We would like to thank  Yoav Altman  from the  Flow Cytometry core  for his excellent support with cell sorting and flow cytometry. We would like to thank  Dr. Warren Pear  for the MSCV-Trib2 plasmid. We also acknowledge the support of our funding sources:  NIH   R00 CA154880 ,  NIH/NCI   P30 CA030199 , the  Rally Foundation for Childhood Cancer Research and Luke Tatsu Johnson Foundation  under Award Number  19YIN45 , an  Emerging Scientist Award  from the  Children's Cancer Research Fund , and the  V Foundation for Cancer Research (TVF)  under Award Number  DVP2019-015. We acknowledge the support of the  Lady Tata foundation  to A. D and from the  MSKCC Support  Grant  NIH P30 CA008748 , and grants to R.
Five new excited narrow Ω 0 2 Detector and simulation The LHCb detector  [46, 47]  is a single-arm forward spectrometer covering the pseudorapidity range 2 < η < 5, designed for the study of particles containing b or c quarks. The detector includes a high-precision tracking system consisting of a siliconstrip vertex detector surrounding the pp interaction region, a large-area silicon-strip detector located upstream of a dipole magnet with a bending power of about 4 Tm, and three stations of silicon-strip detectors together with straw drift tubes placed down-  background shape is fixed according to a wrong-sign sample, consisting of Ξ + c K -π + combinations processed in the same way as the right-sign Ξ + c K -π -combinations. The shape of the partially reconstructed decays is taken from simulated samples generated using the RapidSim package  [57].
Viability of cells in GelMA and proliferation assay The viability of the encapsulated cells was assessed using a standard Live/Dead staining kit and using the metabolic assay PrestoBlue on days 1 and 3. Calcein AM and Ethidium Homodimer-1 comprised the Live/ Dead staining kit and were used according to the manufacturer's protocol with an incubation time of 30 min. Afterward, fluorescent images were taken using a fluorescence microscope (Axio Observer D1, Carl Zeiss, Weimar, Germany), and ImageJ (v1. 52p, NIH) software was used for image analysis. PrestoBlue proliferation assay was performed according to the manufacturer's instructions with an incubation time of 2. 5 h. Cell growth was evaluated using spectrophotometry with absorption measurements at 570 nm and 600 nm.
Image processing Image processing was required to differentiate between aggregates, mastic (asphalt binder and aggregate particles), and air voids, as well as, to determine the total and interconnected air void content. This was done using an open-source image processing software named ImageJ, v 1. 0 from National Institutes of Health  (Schneider et al. , 2012). A flowchart depicting the process is presented in Figure  2  and it is described in more detail than here in  Gong (2020). After the slice import, the image extension is changed to an 8-bit image format to allow further analysis. Cropping is applied to obtain the area of interest. The minimum auto threshold function is then applied to adjust the grey levels to more clearly identify the air voids in relation to the other material phases.
For the test campaign, the small strain (viscoelastic) range is considered for both test methods. The asphalt mixtures are selected based on binder stiffness (i. e. , soft and relatively harder polymermodified binder mixes). Moreover, the study investigates the correlation between E* and Mr, and Mr with storage dynamic modulus (E 0 ) using the superposition principle and through the use of master curves. In addition, a material database is obtained for the Norwegian asphalt concrete mixes.
TESTING AND EVALUATION PLAN The testing plan adopted in this study to assess the cost effectiveness of the stabilized bases evaluated in this study involved: FWD testing, Pavement ME Design simulations and pavement condition assessment. FWD tests were conducted on all five field sections approximately one year after they were originally constructed. At each test location on the sections, four drop heights that corresponded to target loads of 29. 4 kN, 41. 2 kN, 58. 8 kN, and 78. 4 kN, respectively were utilized. Geophones were spaced at 0, 200 mm, 300 mm, 450 mm, 600 mm, 900 mm, and 1200 mm, respectively from the applied load. The collected pavement deflection data was then utilized to back-calculate the elastic moduli for all layers in all pavement sections. The back-calculation process was performed using the BAKFAA software which is a computer program developed and used by the Federal Aviation Administration (FAA).
In this computer program, initial "seed" values for the moduli of respective pavement layers are used as inputs in BAKFAA. Deflections are then calculated by a subroutine program which runs a static-linear elastic analysis using concepts from the multilayered elastic theory. During this process, the original seed layer moduli values are altered using an iterative method in which the root mean squared error between computed and measured deflections is minimized by matching the radius of curvature of the measured and calculated deflection basins. The Pavement ME Design simulations entailed the use of level 3 inputs. Table  1  presents a summary of all inputs utilized in conducting the Pavement ME Design simulations. Data collected from weigh in motion (WIM) stations that were in close proximity to Route 165 was used to determine a vehicle class distribution that was representative of traffic on Route 165.
The Federal Aviation Administration (FAA) has developed FAARFIELD (FAA 2017) which is commonly used in the USA and many other countries around the world. In France the software Alize is used  (Balay et al. 2009) , while in Australia, Airport Pavement Structural Design Software (APSDS)  (Wardle & Rodway 1998)  is common. APSDS is similar to its parent software for road pavement design, known as Circly  (Wardle 1977). Despite differences in user interfaces, flexibility, conservatism and some internal calculation processes, all these softwares are based on the same principles: The pavement layers and materials are selected. The layer thickness, material modulus and Poisson's ratio are assigned to each layer. A subgrade is selected for each pavement profile, characterised by a CBR and converted to an elastic modulus.
This difference can lead to a pavement being designed for a specific aircraft and then having a PCN assigned that requires that same aircraft to operate under a Pavement Concession. That is, the pavement is assigned a PCN that is lower than the ACN of the aircraft that it was designed for, which is illogical. To avoid this challenge in the future, ICAO has developed a replacement for the ACN-PCN system. The ACN is replaced by an Aircraft Classification Rating (ACR) and the PCN is replaced by a Pavement Classification Rating (PCR). The ACR-PCR system is intended to operate on the same principles as the ACN-PCN system with one primary difference. ACR values are calculated using the critical strain in the same layered elastic model that is used in the FAA's pavement thickness design software FAARFIELD (FAA 2017).
DEVELOPMENT OF A MATLAB APPLICATION The model described in the previous paragraphs has been implemented in MATrix LABoratory (MATLAB®) in order to compute the stopping distance of the aircraft within different EMAS materials. The developed application named ABIAS (Aircraft Behavior in Arresting System) can be used for any airport with any type of land restrictions. The aircraft characteristics can be numerically inputted, and runway geometry can be reported in coordinates in the referring mesh calculation, pointing the threshold at which the runway ends and EMAS installation starts. The application then simulates the aircraft behavior inside the EMAS by means of the following inputs: Maximum Take-Off Weight (MTOW), average wingspan, main gear load, mean touchdown velocity, headwind/tailwind velocities, runway length, EMAS dimensions, material with desired crushing stress (Pc) to be used in EMAS, etc.
CONCLUSION An application named ABIAS (Aircraft Behavior in Arresting System) was developed by the authors in the platform of MATLAB® in order to simulate the behavior of aircraft within engineered material arresting system. Thanks to this application it is possible to compute the stopping distance of an aircraft within different EMAS materials. ABIAS is principally applicable for those airports with land restrictions. Moreover, aircraft behavior is analyzed for materials with different crushing stress levels to be used in EMAS. Outputs of the calculations are plotted as arresting distances contour intervals, alongside the arrestor bed configuration (100 × 60m) for low-density concrete mixes with crushing stress levels of 172500 (type A), 345000 (type B) and 930000Pa (type C). Aircraft arresting distance within the EMAS area decreases when the crushing strength of the material increases.
The device scans the crack path on the drill core surface and visualizes the measuring object in 3D. The results were imported and evaluated in MATLAB. A crack length of 3 cm is measured for drill core No. 21. The mean crack width is 0. 737 mm and is clearly above the maximum width of 0. 5 mm. The crack width at the pavement surface is 0. 958 mm, which is below the limit of 1. 0 mm. Drill core No. 22 shows two wider cracks along the drill core depth, forming a continuous crack. The first crack has a length of 5 cm and extends in depth into the bottom concrete. The average crack width is 0. 527 mm and thus slightly exceeds the maximum crack width. In the lower part of the drill core, the second crack has a length of 2. 5 cm and a significantly higher mean crack width of 0. 951 mm. Overall, the crack length extends along the drill core length with different crack widths.
Layers' thicknesses reported in database system of road authorities are often not available at the required precision or not reliable. Consequently, thicknesses used for back-calculation of the layer moduli are conventionally taken by drilling cores or using the borescope. The ground penetrating radar (GPR) has become an alternative even if some processing limitations must be mitigated. With regards to the second point, layers moduli have been back calculated using TSD data but still some uncertainties are in place about the precision of calculated remaining equivalent single axle loads (ESALs) from stresses and strains obtained using a viscoelastic mechanistic model. Based on these challenges, the Danish Road Directorate (DRD) and the German Federal Highway Research Institute (BASt) cooperated in a project with the objective of developing simplified models to classify the remaining lifetime of a pavement using Surface Curvature Index data.
Simulated FWD method A three-layer linear elastic model consisting of asphalt layer, granular layer and subgrade was selected to model the pavement sections subjected to FWD load of 50 kN (with circular contact area of 15 cm radius). When needed, different asphaltic layers were combined in a single layer using Odemark method. BISAR ® software was applied for the analysis of the model pavement sections. Fatigue cracking was considered as the failure criteria and therefore horizontal tensile strain at the bottom of the asphalt layer was taken as the critical response. To select the German pavements, the national pavement design guideline known as  RStO 12 (FGSV 499, 2012)  was used. The guideline is a catalogue-based pavement design which propose different thicknesses based on the pavements traffic class and the type of its material in 7 different traffic classes (known as BK 100 to BK 0.
The following equation (equation 3) has been used to correct the asphalt modulus to the reference temperature of 20°C. where E(T) is the modulus of the asphalt layer at the temperature T. For each measurement, two different empirical approaches were used and compared: i) remaining lifetime is assessed using the empirical equations implemented in the Danish pavement design guide (MMOPP); ii) the lifetime is given by the back-calculation software ELMOD ®. In both cases, all pavements were represented as three layers system and modeled using mechanistic model based on multilayer elastic theory (MLET). Three different failure points were evaluated based on: a) tensile strain at the bottom of the asphalt layer, b) stress on top of the granular layer and c) stress on top of the subgrade. Equation 1 is used to assess the durability of the asphalt layer while equation 4 is used to evaluate the potential failures based on the vertical stress for the failure points b) and c).
ANALYSIS OF THE RESULTS As described in the previous paragraph, all the models, used to assess the remaining lifetime of a pavement structure, have as input a physical stress or strain calculated trough a mechanistic model of the pavement structure subjected to a specific loading condition. To provide a better understanding of pavement structures included in this investigation, distribution of the critical tensile strains, used to calculate the remaining lifetime, are shown in Figure  1. For the sample of pavements of the historical FWD dataset, also vertical stresses on top of the granular base and subgrade are presented (Figure  2 ). Considering the data processed using the simulated FWD method, almost 30% of the BASt structures included in this analysis have a peak of tensile strain at the bottom of the asphalt between 50 and 100 μstrain.
It is important to select representative pavement and material properties in order to cover as many pavement types as possible. Each dataset was used to define a reliable power regression model. While fitting the historical data back calculated using MMOPP and Dynatest Elmod ® , two additional models have been added. In both cases, a 3rd degree polynomial function was defined. To be conservative, both functions were shifted by a constant value towards the lower edge of the data. All the regression models have been summarized in Table  5. For all the models, y represents the remaining number of 10-ton ESALs while x is the SCI300 at the reference temperature of 20°C. Even with the R-squared higher than 0. 8 in all the studied cases, it is possible to foresee big challenges if residual life of a pavement should be assessed only based on SCI300.
The number of ballast particles N i in each sample ranged from 7373 to 7590. Based on the probability density distribution of the morphological feature indices gained by 3D laser scanning, corresponding index values were obtained in the range of each morphological index with the uniform distribution random number. Adopting the particle shape reconstruction method established above, polyhedral particles were modelled in the discrete element program YADE. The modeling of the sleeper and subgrade were conducted by open-source code OOFEM (Patzák 2012) as FEM parts. The concrete sleeper was simulated with the linear elastic constitutive model, whereas the Drucker-Prager constitutive model was used for the subgrade. Some parameters used in the DEM-FEM modeling are presented in Table  1. The train load applied on the rail pad, with a length of 0.
CASE STUDY AND RESULTS For this study, FWD measuring stations were installed at 20 m intervals and thickness measuring stations (GPR) were placed at 8 m intervals. A part of thickness data produced by the same station is used to draw the matrices for input and output. As a result, 158 data series of input and output were obtained (Rahimi Nahoujy 2020). In this paper, the MLP network is implemented in the MATLAB® programing language and computing environment, where the MLP model is trained with FWD data. In order to obtain an optimum architecture, different hidden layers, neurons and training algorithms were applied. Each architecture was trained with the same input and output dataset. The models are the outcome of training measuring point, asphalt body temperature, and thickness as input and deflection (d0) as output.
Relying on such simplifying assumptions can be detrimental to the accuracy of the analysis of structural impacts of SHL vehicles with demanding loading conditions. In addition to the elaborated shortcomings, acceleration and deceleration of the non-conventional SHLs with taxing loading conditions, as well as their impacts on the pavement service life is often overlooked in the literature. The highlighted issues and concerns were the motivations for our research team to devise a protocol study to assess the influence of acceleration and deceleration of SHL vehicles on the imparted damages and loss of service life of pavements for several Farm-to-Market (FM) roads, State Highways (SHs), and US Highways in ten overload corridors. The developed mechanistic framework, accounts for the demanding loading conditions attributed to SHLs, 2 site-specific Axle Load Spectra (ALS) databases, various acceleration/deceleration patterns, realistic tire-pavement interactions, and unique features of pavement facilities in the network.
Subsequently, the authors deployed Portable Weigh-In-Motion (P-WIM) devices to collect sitespecific ALS during summer and winter months, for accurate characterization of traffic in ten representative sites. GVWs, vehicle classifications, axle load distributions, axle weights, axle configurations, and wheel loads were the most relevant traffic information that were extracted from the ALS databases. The authors further developed a procedure for routine measurements of tire pressure and tire footprint in the calibration process of P-WIM devices to achieve an accurate account of the loading conditions. Additionally, the research team conducted an extensive data mining on the permit records and SHL vehicle plans to complement the information on the axle and tire characteristics of the SHLs operating in Texas overload corridors.
e. , ESAL (steady rolling) , ESAL (acceleration) , and ESAL (deceleration) , to characterize the traffic in numerical simulations for further comparison purposes. The cumulative 18-kip (8. 16 tons) ESAL values were calculated from Equation 1 as: where, ðEALFÞ i represents the calculated EALF value for i th -axle; and n i is the projected number of passes of i th -axle load group during the design period. As shown in Equation  1 , pavement damage equivalency factors, calculated at the second stage of the analysis, are integral components for accurate characterization of the accumulated pavement damages. The number of axle load repetitions were also derived from the site-specific ALS databases. Then, the EALF values for each axle type and axle weight were multiplied by the projected number of load repetitions to calculate the projected ESAL values considering the design life of pavements.