dataesr/scientific-paragraphs-categorization huggingface dataset:
CIPgs captures images of cloud particles using a 64-element photodiode array to obtain twodimensional images with nominal detection diameters ranging from 15 to 960 µm. CCP-CDP and NIXE-CAS performances have been frequently validated by glass-sphere calibrations. Before or after each flight, CCP-CIPgs and NIXE-CIP calibrations were performed using spinning disks carrying opaque spots sized to the particle range to be detected. Particle concentration data measured with CCP are corrected for compression under measurement conditions using a thermodynamic approach developed by  Weigel et al. (2016).
Optical modeling The backscattering coefficient and depolarization ratio have been computed with the GRASP (Generalized Retrieval of Aerosol and Surface Properties) Spheroid package coupled with Mie scattering computations performed using code available from NASA's Ocean-Color website (https://oceancolor. gsfc. nasa. gov/docs/ocssw/ bhmie_8py_source. html, last access: 20 April 2022), one of Python's avatars of the Bohren and Huffman Fortran code originally published in their book on light scattering  (Bohren and Huffman, 2008). GRASP is the first unified algorithm developed for characterizing atmospheric properties gathered from a variety of remote sensing observations  (Dubovik et al. , 2014)  whose software packages are available on the web project repository (https://www. grasp-open. com/, last access: 27 May 2022).
aspherical ratio of backscattering such that we can extrapolate its results with confidence. Beyond the computational limits of GRASP, we have thus set the depolarization of the particles to be constant, i. e. , equal to its asymptotic value. Moreover, we have used the Mie code for the calculation of the backscattering efficiencies, suitably rescaled by a constant factor so as to make the scaled Mie backscattering overlap with the GRASP asymptotic values. Such a constant factor was calculated from the ratio of Mie vs. T-matrix efficiencies in the radius dimensional region from 5 to 14 µm (60-180 size parameters). It turned out to always be less than unity. We thus calculated, for 25 different aspect ratios from 0. 3 to 3, the backscattering efficiencies Q AR l and the depolarizations δ AR l on a grid of 2000 radius points R l equally spaced on a logarithmic scale and extending from 0.
005 to 1000 µm. We used the GRASP computations for radii below 14 µm, and we extrapolated these result with the Mie code for larger radii, as outlined above. We used the value of 1. 31 as the refractive index relative to ice. To get rid of the oscillating nature of the backscattering efficiency, for each radius R l , the values of Q AR l and δ AR l were actually obtained as averages over a narrow lognormal distribution centered at R l , with a variance of 1. 01. Such averages were computed over the lognormal with a finer subgrid of 500 points R k , equally spaced on a logarithmic scale and extending over 1σ from the lognormal center. Figures  A1  and A2  in Appendix A report the results of such extrapolation. In those figures, the particle backscattering coefficients and depolarization ratio have been calculated for a reference particle density of 1 cm -3 for different ARs and displayed for particle radii from 0.
To retrieve cirrus cloud IWC, the FLASH water vapor measurements were subtracted from the FISH total water data once corrected for inlet enhancements (see  Afchine et al. , 2018). This was done for five out of the eight flights under assessment. For two flights (on 31 July and 4 August) when FISH was not operating, the IWC was computed from NIXE-CAPS particle volumes with an estimate of the ice density of 0. 92 g cm -3. An extensive assessment of the methodology to retrieve IWC from the two hygrometers and from the PSDs, as well as a comparison between the two approaches, can be found in  Afchine et al. (2018) , where the equivalence of the two methods was demonstrated.
The comparison of the particle backscattering β NIXE-CAPS and β CCP computed from the PSD by use of Mie theory again produced β NIXE-CAPS that was larger than the corresponding β CCP by roughly a factor 2 (see Fig. A6 in Appendix A ). An in-depth analysis of the datasets from the two cloud spectrometers showed that, in the common size range of 1. 25-15 µm particle radius, CAS and CDP agreed very well with each other. Conversely, an offset was found between NIXE-CAPS-CIPgs and CCP-CIPgs data, which was not a product of different evaluation methods or filter criteria of the image files but was very likely due to a hardware problem. A failure of the lasers' temperature stabilization was identified to lead to a loss of beam intensity at higher altitudes and therefore a less illuminated sample volume and diode array of the CCP-CIPgs.
This resulted in a slightly lower instrument particle detection sensitivity which could only be identified by a comparison of particle habits like size and number concentrations in the range of both CIPgs instruments measured at significantly elevated altitudes  (Port, 2021 ). An implication of this finding in a similar comparison of the same two instruments from previous simultaneous measurements at lower altitudes  (Mei et al. , 2020)  can be ruled out. Therefore, for all further analyses of the optical and microphysical parameters, only the NIXE-CAPS dataset was used.
This, in turn, is often correlated with high values of total depolarization. Moreover, looking at the entire dataset, we have noted a tendency to have ARs close to 1 associated with small β values and depolarization of less than 20 %, while ARs greater than 1. 5 and less than 0. 5 favor medium-high β values and depolarization greater than 20 % (see Fig. A7  in Appendix A). We have explored a possible lack of linearity in the response of the backscattersonde at high backscattered signals as a possible cause of the discrepancy between the highest calculated and measured β. The linear response of the backscattersonde was checked in the laboratory before the campaign deployment. This was done by screening the receiving optics with a series of calibrated gray filters and controlling the response of the instrument to pulsed light of various intensities.
The average of the two lidar backscattering coefficients observed by them at a distance of 500 m upwards and downwards was then compared with that observed by MAS. The data have been averaged over 60 s. The result is displayed in Fig. 4. Despite the scattering of the data points, which might be attributed to a lack of vertical homogeneity of the cloud, it shows that there is a good correlation between the backscattering coefficient measured in situ and that measured at close range. The lidar backscatter signal is measured in photon-counting mode. The linear dynamic range is identified to be below the count rate of approx. 1. 5 MHz. The complete signal saturation is noticed to be a count rate of approx. 15 MHz. For the cases reported here, the signals are at the level of some tens of KHz, i.
e. , well inside the linearity range. Thus, we may exclude any loss of linearity. As a result, we are tempted to exclude a severe loss of linearity of the backscattersonde and a consequent significant underestimation of the largest backscattering coefficients observed by it. Further sustaining our conclusion, we note that the overestimation of the optical model also appears in regions where saturation of the backscattersonde signal should not be ex-pected, for instance, around 34 000 s on the 10 August 2017 flight. Figures  5  and 6  report the two-dimensional histograms of occurrence of the measured-calculated backscattering coefficients and particle depolarization. While for the β the agreement is good and the analysis of the whole campaign dataset confirms what has already been shown in the previously discussed time series, i.
A9 in Appendix A ). Even where the agreement between the calculated and the measured β is kept within 1 order of magnitude, it fails to reproduce effectively the experimental dataset, as reported in Figs. 5  and 6. In some ways it is unfortunate not to be able to simulate backscattering and depolarization simultaneously with a single choice of AR. However, we are aware of the fact that the modeling of depolarization is an open and highly challenging problem, one that is not easy and that is perhaps impossible to solve given the wide variety of shapes that atmospheric ice crystals can take  (Liou and Yang, 2016). For this reason, we prefer to keep our comparison between optical measurements and calculations limited to the backscattering coefficient.
Backscattering versus particle size distribution bulk parameters Figures  7, 8, 9, 10, and 11  show N ice , R mean , R eff , SAD, and IWC as functions of β, respectively. In the graphs, the black lines represent regression curves, parameterized in the general form X = A • β B. The coefficients of the regression are reported in Table  1  together with the R squared of the fit. The dataset has been fitted between the limits 2 × 10 -8 and 5 × 10 -6 m -1 sr -1 , which have been chosen to maximize the goodness of the fit and to try to avoid outliers at the extremes of the variability range. We can regard those regression curves as guides for estimating the microphysics bulk parameters of the clouds and their variability range from remote measurements of cloud optical parameters. The linearity between β and N ice (Fig.
The reason is that the depolarization signal becomes weaker with decreasing ice particle size and also that the ice particles become less and less aspheric, and the backscattersonde and the cloud spectrometer have depolarization sensitivities that vary differently with the size of the scattering particle. In order to display other parameters along with the former two, which may help us find and possibly disentangle a link between these two quantities, we choose to represent our dataset in a δ T -BR space (in fact, for ease of scale, 1 -1/BR is used instead of BR) and to color code the points with AF, N, R mean , and temperature T as possible parameters of interest. We remind ourselves here that δ T is not as an intensive quantity as δ TA since it simultaneously depends on the average shape of the cloud particles, but also on the backscattering of the whole particle distribution, i.
It should be noted that, if among the possible particle shapes the spherical one is included, then δ TA min would attain a 0 value. In Fig. 12  we report our dataset, with the data points from in-cloud measurements (i. e. , BR > 1. 2, 1 -1/BR > 0. 17) color coded in terms of AF. The y-axis intercepts at BR = ∞ give us the range of variability of δ TA , which, for our cirrus, ranges approximately from 20 % to 50 %. Inspecting the figure, we may argue that clouds with medium AF are often linked to intermediate values of δ TA over the whole BR variability range, while clouds with high AF, associated with high BR, show no clear correlation with δ TA. Interestingly, clouds with low AF show up at both low BR (with low to intermediate depolarization) and high BR (with intermediate to high depolarization).
14  we show the same data points color coded in terms of R mean. Figure  13  clearly shows again the positive correlation between BR and N ice independently of the polarization of δ TA , except at the highest values of BR, where there appears a positive correlation between δ TA and N ice. Figure  14  shows how high δ TA values are often associated with high R mean (red dots along the line δ mol -δ TA max ), and again, peculiarly in the same region of the highest values of BR, small R mean coincides with medium to high δ TA. In Fig. 15 , we show the same dataset, this time color coded in terms of temperature. The points at temperatures above 200 K show both very high and, although in smaller numbers, very low depolarization with intermediate to high BR values. Points below 200 K show the general tendency towards an increase in depolarization at colder temperatures, which was already commented on in Fig.
Discussion As outlined in the previous paragraph, in our study, the T matrix has been used directly to model the scattering properties of particles up to a few hundred size parameters and indirectly to estimate the depolarization and the modification of Mie backscattering for larger particles. We note that, while Mie theory strictly applies to spherical particles, it has no upper size limit and can be applied to the entire cloud particle size range. Despite the limitations outlined above, some conclusions can be deduced from our study. It is possible to make the   measurements coincide with the optical modeling through a suitable and reasonable choice of the particle AR, and this reassures us about the compatibility of the two datasets. This choice generally favors high ARs (i. e. , high Mie backscattering depression) in regions of high backscattering and high depolarization and with a large presence of AF, while ARs near to or less than unity are chosen when backscattering is medium to low and when the presence of AF is not very pronounced.
Acknowledgements. We gratefully thank the  StratoClim coordination team  and the  Myasishchev Design Bureau  for successfully conducting the field campaign. This research has been supported by the  StratoClim  project of the  European Community's Seventh Framework Programme  (project no. FP7/2007-2013 ) under grant agreement no. 603557  and with internal funds of the  Max Planck Institute for Chemistry. Francesco Cairo  is grateful to the  MPI for Chemistry  for supporting his sabbatical stay in Mainz, during which the study behind this publication was initiated. Sergey Khaykin 's work was partly supported by the  Agence Nationale de la Recherche TTL-Xing   ANR-17-CE01-0015  project. Financial support. This research has been supported by the  FP7 Environment Seventh Framework Programme (STRATOCLIM  (grant no.
Volume density Code and data availability. Water measurements (FLASH and  FISH), meteorological data from UCSE and TDC, and MAS backscatter and particle measurements from NIXE-CAPS from the StratoClim aircraft campaign are available on the HALO database at https://halo-db. pa. op. dlr. de/mission/101 (DLR, 2022). Particle measurements from CCP are available upon request to the Principal Investigators. Lidar measurements from MAL are available upon request to the PI. Analysis and plotting scripts for this paper are available upon request to the corresponding author. Author contributions. MS, LDL, FC, and GDD prepared the MAS for campaign activity. FC performed and processed the MAS measurements. LL did the analysis scripts for data comparison. SK performed and processed the FLASH measurements.
Preparation of native and mutant FLT3 structures: The native structure of the FLT3 protein, with a resolution of 3. 20 Å, was obtained from the Protein Data Bank (PDB). The PDB ID for the dimeric FLT3 structure is 4XUF  [8]. For our computational analysis, we utilized only one subunit, specifically Subunit A. The crystallographic structure displayed two missing loops: one between residues Lys 649 and Asp 651 , and the other between Glu 708 and Val 782. These missing loops were reconstructed using the Modeler plugin within the Chimera software. The co-crystal ligand, quizartinib, was excised from the binding site. Point mutations were then introduced into the native FLT3 protein structure at position Y 842 to produce the Y-to-C and Y-to-F mutant proteins. These mutant structures were generated using the Dunbrack rotamer library  [9] , and among them, structures with the lowest energy and highest probability scores were chosen for subsequent computational analyses.
MD simulations: The topologies for both ligand and protein structures were generated using the PRODRG server  [12]  and the editconf script from the GROMACS software, respectively. The protein topologies were derived using the GROMOS96 43a1 force-field  [13]. Subsequently, ligand topologies were combined with protein topologies to create a protein-ligand complex. This complex was situated inside a cubic box populated with the simple point charge (SPC) water model. To neutralize the system, counter ions (Na+ and Cl-) were introduced. The neutralized system then underwent 50,000 steps of energy minimization using the steepest descent algorithm. Position restraints for the ligand and temperature coupling groups were established at this juncture. The energy-minimized systems proceeded to a two-phase equilibration, each spanning 1000 ps.
ConPLex analysis: The kinase domain of FLT3 was identified using the NCBI's Conserved Domains Search. For our analysis, we retrieved the Simplified Molecular Input Line Entry System (SMILES) notations of selected small molecules from the ChEMBL Database. To simulate mutations, the Y842 residue in FLT3 was replaced with both Cysteine (C) and Phenylalanine (F). Using a custom Python script, these modified FLT3 sequences were combined with the small molecules' SMILES notations, resulting in more than 5. 7 million protein-small molecule pairs. These pairs were then evaluated using the pre-trained ConPLex model to predict interaction scores, providing insight into potential binding affinities between the FLT3 variants and the small molecules. Xepto50: Xepto50 is designed to handle data ranging from a single experiment to multiple experiments, encompassing various cell lines and drugs, all within a single Excel file.
The software intelligently detects the number of response columns. When there are two or more response columns, Xepto50 calculates using the mean response for subsequent analyses. If there are three or more response columns, the software not only plots the Standard Error of the Mean (SEM) but also provides functionality to compute and remove outliers. Xepto50 is versatile in its data input capabilities; it can accept response data in the form of viability or inhibition, whether presented as a ratio or a percentage. However, for consistency and ease of analysis, it internally converts all input responses to a format that represents inhibition in percentage terms. For curve fitting and analysis, Xepto50 applies a four-parameter logistic regression function.
𝑅𝑒𝑠𝑝𝑜𝑛𝑠𝑒 = 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑚𝑖𝑛 + 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑚𝑎𝑥 -𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒 𝑚𝑖𝑛 1 + 10 ℎ𝑖𝑙𝑙𝑠𝑙𝑜𝑝𝑒(𝑙𝑜𝑔 10 (𝐼𝐶 50 𝑀)-𝑙𝑜𝑔 10 (𝑑𝑟𝑢𝑔 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 (𝑀))) Xepto50 offers an integrated solution for analyzing drug response experiments. Initially, the tool employs the curve_fit function from scipy. optimize to fit the data. To further refine this fit, the lmfit model is subsequently utilized. In terms of metrics, Xepto50 is equipped to calculate traditional IC50, interpolated IC50, and Area Under the Curve (AUC). Additionally, it determines Drug Sensitivity Scores, DSS1, DSS2, and DSS3. Of note is the unique "Xepto50 score" introduced by the software. This score is derived by determining the AUC between the interpolated IC50 and the sum of the interpolated IC50 and a constant value. The baseline response value used for this calculation is 50.
Identification of FLT3 interacting small molecules using molecular contrastive learning and protein language Molecular contrastive learning is an emerging technique that has garnered significant attention due to its ability to leverage vast datasets of small molecules for probing molecular interactions. A novel integration of this methodology with protein language modeling was observed in a recent publication  [17]. For our analysis, we harnessed an expansive dataset of more than 1. 9 million small molecules sourced from the ChEMBL database. These were juxtaposed with the FLT3 kinase domain (FLT3-KD) and its mutants, Y842C and Y842F. Consequently, the analysis involved over 5. 7 million unique FLT3-KD-small molecule pairs. Employing a pretrained model within the ConPLex platform, we discerned that 938 small molecules manifested interactions with the FLT3-KD, contingent on a ConPLex interaction score threshold of >0.
Binding Free Energy Analysis of Native and Mutant FLT3 Structures with Drug Molecules Using the MM-PBSA Approach As we observed a trend in ConPLex interaction scores where mutants displayed slightly compromised interactions, we wanted to use structure-based approaches to measure the effect of point mutations. We have selected three inhibitors: quizartinib, sorafenib, and midostaurin, due to their wide use in FLT3 research. We utilized the MM-PBSA method to compute thermodynamic binding free energies for both native and mutant FLT3 structures interacting with various drug molecules. The native FLT3 protein structure was sourced from the PDB database  [8]. We introduced point mutations at position Y842 to create models of the Y842C and Y842F mutant structures. The kinase domain of the native experimental structure, in complex with the inhibitor quizartinib, was chosen as the binding site for our free energy calculations.
To further assess various metrics, we introduced Xepto50, a robust tool capable of determining IC50, interpolated IC50, AUC, and drug sensitivity scores (DSS1, DSS2, and DSS3) in batch mode from an Excel file input. Xepto50, a Python-based application with a graphical user interface (GUI), exhibited interpolated IC50 and AUC values consistent with those of GraphPad Prism 9 (Supplementary Figure  1B-C ). Additionally, the trends observed in DSS1 (Figure  3C ), DSS2 (Supplementary Figure  1D ), and DSS3 (Supplementary Figure  1E ) paralleled those of IC50 and AUC metrics, implying that these drug sensitivity metrics might not fully encapsulate theoretical observations. The four-parameter logistic regression curve remains a prevalent model for gauging drug sensitivity. A lateral shift in this curve denotes reduced potency (Supplementary Figure  1E ), whereas a diminished slope indicates compromised cooperativity (Supplementary Figure  1F ).
Discussion The advancements in molecular modeling, combined with the rise of machine learning in drug discovery, are poised to bring transformative changes to pharmacology. Among these innovations, molecular contrastive learning stands out as a burgeoning technique, demonstrating its aptitude in deciphering vast molecular interactions with remarkable accuracy. In line with findings from prior studies  [17] , our research capitalizes on the extensive dataset of small molecules sourced from ChEMBL, shedding light on interactions within the FLT3 kinase domain. We observed a distinct trend in interaction scores, descending in the sequence of FLT3-KD > FLT3-KD-Y842C > FLT3-KD-Y842F. This pattern indicates that mutations within the activation loop might be instrumental in altering inhibitor interactions with the FLT3 kinase domain.
| Computational analysis of allergic multimorbidity Multimorbidity mechanisms were investigated at a molecular level by identifying proteins and cellular processes using data mining with an in silico analysis of the topology of the human interactome. 112, 113 A + R + AD share a larger number of associated proteins than expected by chance, with a significant degree of interconnectedness in the interaction network. In eosinophils, T2-signaling pathways represent a relevant multimorbidity mechanism including IL-4 and TSLP (thymic stromal lymphopoietin) as well as IL1R1-and GATA3-related pathways. In non-eosinophilic cell types,  113  IL-13, LRRC32/C11orf30, and PLA2G7 were associated with A + AR + AD. However, in eosinophils and non-eosinophilic cell types, IL-33 was associated with asthma and AD but not with AR alone.
| IL-33, a cornerstone of multimorbid allergic diseases [116] [117]  In MeDALL, an integrated transcriptomic analysis in peripheral blood was conducted in 786 children from three European birth cohorts. 118 Fifty-four genes were differentially expressed in allergic diseases, 27 associated with rhinitis alone, and none to asthma or AD alone. Eight genes were retrieved in multimorbidity. Eosinophilassociated genes were highly expressed in A + AR + AD. RT-qPCR validated transcriptomic data. A replication phase using data from an independent cohort (EVA-PR, n = 447)  119, 120  and RNA-Sequencing confirmed the MeDALL study. A signature of eight genes (IL5/JAK/ STAT and IL33/ST2/IRAK/TRAF 121 ) was identified in A + R + AD. 21. 27 genes were identified for R alone.