dataesr/scientific-paragraphs-categorization huggingface dataset:
Spectroscopy Experiments Infrared and Raman experiments were performed with the samples in their solid states. The pure single components were used without any treatment. The cocrystals were scratched from their compact solid organization. Fourier-transform IR (FTIR) data were acquired on a FT-IR spectrometer using attenuated total reflectance, UATR 1 Bounce model (PerkinElmer SAS, Courtaboeuf, France) in absorbance mode with 4 cm -1 resolutions and 50 scans. The scans were conducted at room temperature in the wavenumbers range of 4000 to 650 cm -1 for each sample placed on a zinc selenide (ZnSe) surface and in contact with a stainless-steel tip. Spectrum 65 L125100I software (PerkinElmer SAS, Courtaboeuf, France) was used to collect and analyze the spectra data. The Raman analyses were conducted with a confocal micro-Raman LabRAM HR evolution (Horiba Jobin Yvon, Edison, NJ, United States), equipped with a red He-Ne laser at 632.
Samples of approximately 5 mg were analyzed at a heating rate of 10 K•min -1 under nitrogen gas (flow rate: 50 mL•min -1 ) in pin-holed aluminum pans obtained with a stainless-steel tip. The mass of the analyzed samples was recorded using TA universal analysis software. The thermal analysis experiments were conducted in triplicate and were evaluated with the Stare 16. 30 software (Mettler Toledo, Greifensee, Switzerland).
Nuclear Magnetic Resonance Experiments Proton nuclear magnetic resonance ( 1 H NMR) spectra were recorded at 400 MHz on a Brüker AVANCE III, using either D 2 O (deuterated water) or CDCl 3 (deuterated chloroform) as a solvent, and have been analyzed using TopSpin 4. 1. 3 software. All samples were prepared at 8. 3 mg•mL -1 and dissolved at room temperature for 24 h and 10 min in D 2 O and CDCl 3 , respectively. To obtain more insights into the menthol/lidocaine interactions in CDCl 3 , 2D Nuclear Overhauser Effect SpectroscopY (NOESY) spectra were recorded with the same instrument. For this purpose, a lidocaine/DL-menthol mixture was prepared in CDCl 3 with the same molar ratio as the Lido:DLM cocrystal dissolved in the latter solvent.
Density Measurement An electronic density meter, namely DMA 5000 (Anton Paar, Graz, Austria) was used to obtain the solubility data for lidocaine, Lido:LM, and Lido:DLM. The device is equipped with a borosilicate U-shape tube (volume thereabouts 1 mL) calibrated beforehand with dry air and ultrapure Milli-Q ® water. Its working principle is to maintain in oscillation the U-shape tube filled with a given sample knowing that the measurement of the resulting period is proportional to the volumetric mass of the sample, allowing to determine also the density. For sample preparation, an excess amount of cocrystal was introduced into a 10 mL volumetric flask filled with the phosphate buffer solution (pH = 7. 2). The resulting suspension homogenized at room temperature was agitated by a magnetic stirrer for at least 12 h.
The crystallographic data of powder (CCDC reference number 2232113) and single crystal structures of the Lido:DLM cocrystal at 100 and 233 K (CCDC reference numbers 2,231,851 and 2,248,363, respectively) have been deposited in the Cambridge Crystallographic Data Centre (CCDC). The differences in torsion angles between the enantiopure and the racemic cocrystals are presented in the Table  S1  of the Supplementary Materials. As far as the H-bonding is concerned, it has been previously demonstrated that the formation of two new intermolecular O-H•••O and N-H•••O types between L-menthol and lidocaine molecules allows stabilizing the Lido:LM cocrystal  [44]. Comparing the intermolecular H-bonding network of the enantiopure-vs. the menthol racemate-based cocrystals, the latter cocrystal presents shorter lengths and smaller angles (Table  S2 ), and more importantly, two disordered menthol molecules in a unit cell (Tables  S1  and S2 ), suggesting a less dense crystal structure.
Acknowledgments: P. M. and E. R. are grateful to the  Chinese Scholarship Council for the doctoral fellowship  and the  European Community  for the Erasmus fellowship, respectively. A. S. S. thanks  UTCBS lab  for funding his 6-month internship. L. C. H. is funded by the  Education, Audiovisual and Culture Executive Agency (EACEA)  of the  European Commission  in the field of the  Erasmus Mundus Joint Master Degree (EMJMD) of Nanomedicine for Drug Delivery (NanoMed). L. C. H. is also grateful to the  International Affairs and Foreign Cooperation Office (OAICE)  of the  University of Costa Rica  for the granted scholarship (contract No. OAICE-13-2022 ). T. L. and Y. C. are grateful to the  Orchid PHC France/Taiwan bilateral program  (French reference: 49686TC; Taiwanese reference:  NSTC 112-2927-I-008-506.
Acknowledgments We would like to thank  Navid Abbasi ,  Danny Brattan ,  Aristomenis Donos ,  Paolo Glorioso ,  Blaise Goutéraux ,  Sebastian Grieninger ,  Michael Landry  and  Ashish Shukla  for useful discussions. We would also like to thank  Akash Jain  and  Jay Armas  for discussions on the relation between our results and theirs. M. B. acknowledges the support of the  Shanghai.
Acknowledgments: This work was supported by the  National Eye Institute of the National Institutes of Health  under  Award Number  ( R01 EY021594 , W. A. F. ), and  The New York Stem Cell Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank  C. Fisher  for help with stimulus preparation;  W. Zarco  and  A. Gonzalez  for help with animal training;  S. Serene  for sharing analysis tools;  L. Yin  for logistics support; and the veterinary team of the  Rockefeller University  for the care of the subjects.
Background Gene-modified cell therapy with regulatory T cells (Tregs) is a promising approach to prevent graft rejection and induce immunological tolerance in organ transplantation. We are developing a cell therapy comprising autologous naïve Tregs that are isolated from leukapheresate, transduced with lentiviral vector encoding a chimeric antigen receptor (CAR) recognising human leukocyte antigen class I molecule A*02 (HLA-A*02), and expanded ex vivo before cryopreservation as resultant drug product (TX200-TR101). In an ongoing first-in-human study (NCT04817774), kidney transplant recipients will receive a single infusion of TX200-TR101 2-3 months after transplantation. The phase 0 study described here evaluated the feasibility of manufacture of TX200-TR101 for the target population, i. e.
, Gaithersburg, MD, USA). For information, the HIV-1-derived lentiviral vector used to express the anti-HLA-A2 CAR has been produced in commercially large-scale by Lentigen Technology Inc. The transgene lentiviral backbone and lentiviral vector helpers are Lentigen's proprietary plasmids (confidential). Transduced cells were activated with anti-CD3/anti-CD28 beads, expanded, washed, and harvested to provide TX200-TR101 drug substance. Drug substance was then formulated, finished and cryopreserved as TX200-TR101 drug product. Cryopreserved leukapheresates from 10 consented healthy human donors were purchased from HemaCare (Charles River). Batches of drug product from 5 healthy donors were manufactured at Sangamo Therapeutics France SAS as described above. The manufacturing process was then transferred to a contract manufacturing organisation in Europe, where batches of drug product from 5 different healthy donors were manufactured under GMP conditions.
The box represents the interquartile range; the median value divides the box in two parts; whiskers represent minimum and maximum values; X represents the mean value. Individual data points (other than minimum and maximum values) are shown as open circles. No statistically significant differences in the means between the two populations (ESRD patients vs healthy donors) were observed. 1 There was insufficient final product available from 4 of the healthy donors for all of the tests; consequently, the activation assay and the proliferation assay were only conducted on 6 out of the 10 batches manufactured from healthy donor material of the antigen-specific drug product (HLA-A2 CAR-Tregs). The underlying condition of patients with respect to concurrent illness, concomitant medications or dialysis did not impact on the ability to manufacture good quality CAR-Tregs and all batches of drug product met prespecified release criteria.
Participants in this manufacturing feasibility study did not receive an infusion TX200-TR101 after successful manufacture of drug product. However, a first-inhuman dose-ranging study to assess TX200-TR101 in living-donor kidney transplant recipients is now ongoing (NCT04817774; STEADFAST)  [7]. The STEAD-FAST study represents the next frontier in adoptive cell therapies, being the first clinical study to our knowledge to administer a CAR-Treg therapy to humans. For livingdonor transplantation, the starting material (leukapheresate) for manufacture of TX200-TR101 will be collected up to 6 months before the planned date of transplantation  [7]. Previous studies have shown that pharmacological immunosuppression and previous acute rejection episodes in kidney transplant recipients do not specifically alter the frequency, phenotype, or maturation of natural Tregs in vivo  [3, 22].
Acknowledgements The authors thank the investigator,  Etablissement Français du Sang, Unité d'Ingénierie Cellulaire  and the patients participating in the study. The authors also thank the  University of British Columbia  and  adMare Bioinnovations  (formerly known as the  Center for Drug Research and Development)  for their collaboration on the development of the HLA-A2-CAR. Finally, the authors would like to thank  Dr Michael Molony  for providing statistical assistance, and  Dr Julie Taylor  ( Peak Biomedical Ltd, UK ) for providing medical writing assistance.
The assimilation of S1 SSM products alone, LAI alone, and S1 SSM together with LAI was performed in order to assess the added value of the S1 SSM assimilation. The paper is organized as follows: Section 2 presents the ISBA model and the LDAS-Monde tool, the satellite and in situ datasets, as well as the study areas. Section 3 describes the methodology and the experimental design. Section 4 evaluates the S1 SSM products using in situ measurements of SSM and presents the assimilation results. Section 5 contains a discussion about how results can be interpreted. Section 6 provides conclusions and highlights on future works.
LDAS-Monde Within the SURFEX modeling platform, the joint assimilation of SSM and LAI satellitebased products is possible using the LDAS-Monde tool. LDAS-Monde assimilates satellite products using a simplified extended Kalman filter (SEKF) technique. The variables analyzed were LAI and soil moisture at several depths (up to 1 m). Each analyzed variable had 12 different values corresponding to 12 land surface patch classes. The patch fraction for each model grid cell was calculated from the ECOCLIMAP-II land cover database  [40] , including bare soil, rocks, permanent snow, and ice surfaces, and nine plant functional types (needleleaf trees, evergreen broadleaf trees, deciduous broadleaf trees, C3 crops, C4 crops, C4 irrigated crops, C3 grasslands, C4 grasslands, and wetlands). The assimilation process was performed every 24 h, and the analyzed variables were used as initial conditions for the next 24 h window.
LAI Data The GEOV1 LAI product is distributed by the Copernicus Global Land Service (CGLS). It is a satellite-based estimate of LAI that is derived from observations from the PROBA-V satellite. The product is generated using a statistical algorithm  [50] , specifically, a neural network trained on two other pre-existing products, and is provided globally with a spatial resolution of 1 km × 1 km and a sampling time of 10 days.
Land Cover The land cover data were obtained from the global CGLS dataset based on PROBA-V observations at a spatial resolution of 100 m × 100 m. Since the S²MP inversion model is only applicable to agricultural areas and grasslands (vineyards and orchards are excluded), the CGLS-LC100 map was filtered to retain only the agricultural areas listed in CGLS-LC100: cropland (map code "40") and herbaceous vegetation (map code "30"). In CGLS-LC100, cropland is defined as land covered with temporary crops followed by harvest and a period of bare soil. Note that perennial woody crops are classified as the appropriate forest or shrub land cover type. Herbaceous vegetation is defined in CGLS-LC100 as follows: "Plants without persistent stems or shoots above ground and lacking a definite firm structure (tree and shrub cover is less than 10%)".
Land Cover The land cover data were obtained from the global CGLS dataset based on PROBA-V observations at a spatial resolution of 100 m × 100 m. Since the S 2 MP inversion model is only applicable to agricultural areas and grasslands (vineyards and orchards are excluded), the CGLS-LC100 map was filtered to retain only the agricultural areas listed in CGLS-LC100: cropland (map code "40") and herbaceous vegetation (map code "30"). In CGLS-LC100, cropland is defined as land covered with temporary crops followed by harvest and a period of bare soil. Note that perennial woody crops are classified as the appropriate forest or shrub land cover type. Herbaceous vegetation is defined in CGLS-LC100 as follows: "Plants without persistent stems or shoots above ground and lacking a definite firm structure (tree and shrub cover is less than 10%)".
The assimilation values from the previous window formed the initial conditions for the next 24 h window, allowing fixed estimates of background errors to be used instead of calculating them at the beginning of each cycle. In order to run ISBA-A-gs in offline mode, several surface atmospheric variables are required. In this work, for each day, we used hourly forecast data from AROME-France initiated at 00:00 UTC interpolated on a regular Cartesian grid with a spatial resolution of 1/40 • (about 2. 5 km at 45 • N) made available to the public through the Meteo-France BDAP database (Base de Données Analysées et Prévues). The following atmospheric inputs were used: air temperature, specific humidity, wind speed, and pressure at the lowest vertical level (10 m above canopy), as well as incoming fluxes of shortwave and longwave radiation and liquid and solid (snow) precipitation.
The scatter plots and correlation coefficients between the two datasets for each season are presented in Figure  6 , with winter, spring, and fall showing high correlation coefficient values (R = 0. 92, 0. 88, and 0. 91, respectively). Overall, a good correlation was observed between the datasets for all seasons, except for summer, for which the lowest correlation coefficient was obtained (R = 0. 62). The statistical distribution of the correlation coefficients between the S1 SSM ucts and the observed SSM is shown in Figure  S1  for all seasons and over the three for all SMOSMANIA (Toulouse and Montpellier Regions) and REMEDHUS (Sala Region) stations. A seasonal variation of R can be observed across stations for all re A lower correlation between the satellite products and the observed SSM was ob during the summer season, which supports the results presented in Figure  5  for th The statistical distribution of the correlation coefficients between the S1 SSM products and the observed SSM is shown in Figure  S1  for all seasons and over the three years, for all SMOSMANIA (Toulouse and Montpellier Regions) and REMEDHUS (Salamanca Region) stations.
Can Geology and Land Use Affect the Assimilation? Land use is a critical factor that can significantly affect the accuracy of satellite product assimilation in land surface models  [56]. The accuracy of assimilation results largely depends on the similarity between land surface characteristics, satellite observations, and the land cover database used in LSMs. Several studies have investigated the influence of land use on the assimilation of satellite products in LSMs. For example, Draper et al. [73]  found that the accuracy of soil moisture assimilation was higher in cropland than that in forested areas due to the higher sensitivity of the radar signal to soil moisture in cropland. In this study, the ECOCLIMAP-II land cover map was used in the model simulations, which differs slightly from the CGLS-LC100 map shown in Figure  3.
The LAI-and indirectly SSM-contributions in this specific case could be related to the percentage of forest present in these areas in the ECOCLIMAP-II database, which is less than 50%, indicating the use of other land uses in the ISBA model. The Montpellier region is characterized as a very contrasted area in terms of land use, as shown in Figure  3. The size of the Alès sub-domain (0. 3 • × 0. 3 • ) would correspond to the size of a low-resolution SSM pixel. Figure  10  illustrates the large sub-pixel variability that would be lost at low spatial resolution. Another characteristic of the Montpellier region is the presence of karst areas in the northwest of the domain (Larzac and Cévennes) and flood irrigation for rice production in the southeast of the domain (Camargue). Karstic areas tend to trigger anomalous C-band backscatter signals  [74, 75] , and irrigation is not represented by the ISBA model version we used.
Conclusions This study investigated the benefits of assimilating satellite-derived soil moisture (SSM) products from the Sentinel-1 (S1) satellite in the ISBA land surface model at a spatial resolution of 1 km, from 2017 to 2019, using the LDAS-Monde tool, for two regions in Southern France and one in Spain. The validation of the S1 SSM was carried out by comparing it with in situ measurements of soil moisture at a depth of 5 cm from the SMOSMANIA network and Meteopole-Flux in France and the REMEDHUS network in Spain. The results showed a good agreement between the two datasets for most of the stations at 18:00 UTC, with some difficulties during summer due to irrigation processes in Spain, as the REMEDHUS stations are located in semi-arid croplands. The SSM simulations were generally more precise than the S1 SSM observations, and the ISBA simulations could be used together with in situ observations to validate the S1 SSM.
Data Availability Statement: The S1 SSM product is available from the THEIA data portal (https: //thisme. cines. teledetection. fr/map?c=1. 761582,43. 7451297,8. 04, accessed on 30 August 2023). The PROBA-V LAI product is available from the CGLS portal (https://land. copernicus. eu/global/ products/lai, accessed on 30 August 2023). Land cover data are available from the CGLS portal (https://land. copernicus. eu/global/products/lc, accessed on 30 August 2023). The SMOSMANIA and REMEDHUS in situ soil moisture data are available from the International Soil Moisture Network (ISMN) portal (https://ismn. earth/, accessed on 30 August 2023).
1 Figure 6. 16 Figure 6. SMOSMANIA versus S1 SSM products for the LGC station for (a) winter (Decemb uary, and February), (b) spring (March, April, and May), (c) summer (June, July, and Augu (d) fall (September, October, and November), at 18:00 UTC. The best linear fit as well as th lation coefficient between the two datasets are presented in each sub-plot. RMSD, MB, and ub values are in m 3 m -3. Freezing cases were sorted out by excluding data for which soil temper 5 cm depth was below 4 °C.
Our approach relies on the introduction of adequate constraints on the weights during training, which allows us to concentrate information about labels on a small subset of latent variables. The effectiveness of the approach is illustrated with four examples: the CelebA dataset of facial images, the twodimensional Ising model, the MNIST dataset of handwritten digits, and the taxonomy of protein families. In addition, we show how our framework allows for analytically computing the cost, in terms of the loglikelihood of the data, associated with the disentanglement of their representations.
Informally speaking, this loss is the cost to be paid for enhanced interpretability of the machine. Our paper is organized as follows. We first show that standard learning with RBM generically produces entangled representations on four applications chosen for their diversity and interest:  (1)  the CelebA dataset of face images  [23]  annotated with several binary attributes, (2) the two-dimensional Ising model, where configurations are annotated by the sign of their magnetizations, (3) the MNIST dataset of handwritten digits  [24] , where the digits represented in each image are the labels, and (4) proteinsequence families from the Pfam database  [25]  annotated based on their taxonomic origins. We then present how our approach learns disentangled representations and demonstrate its effectiveness when applied to the three data distributions listed above.
Given a set of data points D, the weights and potential-defining parameters of the RBM are learned through gradient ascent of the dataset log-likelihood, L ¼ hlog PðvÞi D ; ð4Þ where the average h•i D is taken over the data points. In practice, computing the gradient of L requires us to estimate the moments of visible and/or hidden variables with respect to the model distribution  [18]. Regularization of the weights can also be easily included in this approach. Details about the computation of the gradient and the training procedure implemented in this work can be found in Supplemental Material  [26]  Appendix A.
CelebA face images dataset The CelebA dataset consists of a collection of 202 599 color images of celebrity faces, each annotated with 40 binary attributes, including whether the person is smiling, wearing glasses, has a beard, and others  [23]. The images in this dataset cover large pose variations and background clutter. Figure  2 (a) shows a pair of black-and-white versions of CelebA examples; see Supplemental Material  [26]  Fig. S1  for more examples and Supplemental Material Appendix B for processing details.
MNIST handwritten digits The MNIST dataset  [24]  consists of a collection of 70 000 images of 28 × 28 pixels each, labeled by the identity of the 0-9 handwritten digit they represent. We   5 ). Smiling or not smiling for CelebA, sign of the magnetization for the Ising model, whether the digit is a 0 or 1 for MNIST0=1, and whether the KH sequence is from bacterial or eukaryotic origin. show 16 of them in Fig. 2 (a). We hereafter consider in particular (1) MNIST0=1, a simplified version of MNIST consisting only of images of the digits 0 and 1, with binary labels u ¼ 0, 1, and (2) MNIST0=1=2=3, the set of all images of digits from 0 to 3, with four-state labels u. In Supplemental Material  [26]  Fig. S6 , we also consider an additional example consisting of zero digits only in black or white backgrounds (see Sec.
Pfam database of protein family sequences Last of all, we consider protein families in the Pfam sequence database  [25]. A protein family consists of a collection of homologous protein sequences from different organisms, i. e. , sharing common evolutionary origins and common functional or structural features. As an illustration, Fig. 2 (a) sketches some sequences of the K-homology (KH) domain found in nucleic-acid binding proteins. Many families include sequences issued from prokaryotic and eukaryotic organisms, and we use this classification as the label u for sequences in the dataset.
C. RBMs generically learn entangled representations We train RBMs with 200-400 binary hidden units on CelebA images, two-dimensional Ising model configurations, MNIST0=1 digits, and KH domain protein sequences (see Supplemental Material  [26]  Appendix A 6 for details). Consistent with previous results on similar datasets  [19, 20, 28, 29] , the RBMs accurately fit the data and generate high-quality samples in the four cases; see Fig. 2(b ). In addition, training simple classifiers to predict the label from the hidden inputs of the models gives areas under the curve ðAUCÞ > 0. 9 for all cases; see Supplemental Material  [26]  Appendix E for details and Fig. S4. These results demonstrate that the RBM automatically captures information relevant to the labels of interest. We emphasize that in all cases the RBM does not have access to the labels during training.