dataesr/scientific-paragraphs-categorization huggingface dataset:
Target variable This study aims at building an ML model to predict the Xe/Kr ambient-pressure selectivity faster than standard techniques. To obtain reference values (ground truth in this study), we use the RASPA2 software  21  to run GCMC calculations of 20:80 Xe/Kr mixtures at 298 K and 1 atm on our cleaned database. The van der Waals interactions are described by a Lennard-Jones (LJ) potential with a cutoff distance of 12 Å. The LJ parameters of the framework atoms are given by the universal forcefield (UFF),  22  and the guest atoms (xenon and krypton) have their LJ parameters taken from a previous screening study. 23  The study only focuses on a given Xe/Kr composition usually obtained by cryogenic distillation of ambient air 1 as a first step towards predicting other mixtures at different physical conditions (e.
Database and data generation We test this methodology on a set of realistic MOFs by considering the 12 020 all-solvent removed (ASR) structures of the CoRE MOF 2019 database  19. After removing the disordered and the non-MOF structures as well as the ones with a large unit cell volume of 20 nm 3 , the database is reduced to a set of 9 748 structures. Then, with the string information given by the Zeo++ software  24  this number is reduced to 9 177 by removing the structures that are not tridimensional, where solvents are still detected (wrongly classified in "all solvent removed"), or where the metal is radioactive or fissile (e. g. , Pu-MOF TAGCIP  25  , Np-MOF KASHUK  26  , U-MOF ABETAE  27  or Th-MOF ASAMUE  28  ), which is a source of risks in a nuclear waste processing plant. Furthermore, the structures with pore sizes allowing the adsorption of xenon are selected using a condition on the largest cavity diameter (LCD): this is the case for 8 529 structures with an LCD higher than 4 Å (approximately the size of a xenon molecule).
This is equivalent to removing the structures with very unfavorable adsorption enthalpies, that are not promising for our adsorption-based separation. Then, we calculate the descriptors summarized below (and fully detailed in SI) on this restrained dataset. At this stage, 140 structures fail in the GCMC calculation due to RAM limitations and 83 have no standard deviation for the pore distribution (skewness and kurtosis cannot be retrieved). In the following training-testing process, we will therefore use a final dataset of 8 300 structures used to perform our ML-assisted method of screening the Xe/Kr adsorption selectivity. Based on this final set, the model is trained on 80% of randomly selected structures (6 640 structures) and tested on the remaining structures (1 660 structures). However, Jablonka et al.
, energy descriptors greatly influence ML models for selectivity prediction. The geometric analysis of crystalline porous materials is typically based on the predefined van der Waals (vdW) radii from the Cambridge Crystallographic Data Centre (CCDC). This forcefield-independent definition can create a gap between geometrical descriptors and thermodynamic values obtained through molecular simulations. Inspired by a recent work comparing PLDs and self-diffusion coefficients,  33  we define a list of vdW radii to be read by the Zeo++ software (more details can be found at github. com/eren125/zeopp_radtable). In this study, all Zeo++ calculations utilize an atomic radius that corresponds to the distance at which the LJ potential reaches 3𝑘 B 𝑇 /2 at 𝑇 = 298 K. We test several values of the surface area exposed to different probe sizes (1.
We will refer to this new sampling technique as the GrAED algorithm in the following text. To highlight the improvement in performance in this procedure: the average void fraction for a 1. 2 Å probe radius equals to 0. 16 and the average number of symmetric images equals to 5. 8 on the CoRE MOF 2019 database (most MOFs present symmetry operations). On average, the "blocking" procedure means that only 16% of the grid points really need to be calculated. The "symmetry" reduces this number of points to 17%, and the combination of both reduces it to only 2. 7% of the grid. This leads to a significant reduction in the CPU time of the calculation, as shown in Table  1. From the energy values of this grid, we can now calculate many useful descriptors that are used in our final model. These energy-based descriptors are calculated using the GrAED algorithm except for the ambient-pressure case which is handled using GCMC simulations.
ML model performance In this section, we present the performance of the ML model that learns the joint effects of all the newly introduced descriptors to detect and evaluate the drop between the easily accessible low-pressure selectivity and the more computationally demanding ambient-pressure selectivity. A GCMC simulation of a 20:80 xenon/krypton gas mixture takes in average 2 400 s per structure on the CoRE MOF 2019 database, while our grid-based adsorption calculation only takes about 5 s per structure (on a single Intel Xeon Platinum 8168 core at 2. 7 GHz). Computing all the necessary features for the prediction would take less than a minute per structure, significantly faster than the 40 minutes required for a GCMC calculation. The ML-based approach clearly demonstrates a speed advantage over standard molecular simulations.
5 and 0. 07 on the log 10 of the selectivity, which means that the order of magnitude of the selectivity is known with a very good accuracy. To prove that this good performance is not fortuitous, we use a 5-fold cross-validation procedure on the whole dataset and found an average RMSE of 0. 37 kJ mol -1 with a standard Figure  2 : Scatter plot of the exchange free energy predicted by the model. There is a good agreement between the predicted and true values. On the test set, there is an RMSE of 0. 37 kJ mol -1 and an MAE of 0. 21 kJ mol -1. This plot is equivalent to the scatter plot between the logarithm of the ambient-pressure selectivity values (see Figure  S5 ). The corresponding errors for the ambient-selectivity are 2. 5 and 1. 1 for respectively the RMSE and MAE of the selectivity, and 0.
To gain a deeper understanding of separation processes within nanoporous materials, a machine learning prediction of Xe/Kr ambient-pressure selectivity was performed, aiming for faster results compared to standard GCMC calculations. The CoRE MOF 2019 database was utilized for MOF structures, enabling the evaluation of xenon/krypton selectivity in less than a minute, whereas an equivalent GCMC calculation typically requires approximately 40 min. Unlike the majority of selectivity predictions in the literature, the decision was made to predict selectivity on a logarithmic scale that focuses on the order of magnitude rather than the exact value of selectivity for highly selective materials. Moreover, converting to an exchange Gibbs free energy allowed for a more thermodynamic approach based on enthalpy, entropy, and free energy values.
RNA Purification and Sequencing Formalin-fixed and paraffin-embedded (FFPE) sections of stored kidney biopsy tissue were microdissected, as previously reported  [16] , to obtain glomerular cross-sections. RNA was extracted and purified from enzymatically digested specimens using the High Pure FFPE RNA Isolation Kit ® (Roche Molecular Systems, Inc. , Pleasanton, CA, USA) according to previously described techniques  [20]. RNA libraries were generated using the TruSeq RNA Access kit (Illumina Inc. ,San Diego, CA, USA) and sequenced (75 base-pair paired-end RNAseq) according to protocols previously utilized by our group  [21, 22]. Gene expression data are available in the GEO repository through accession number GSE216841.
Mapping Transcriptional Landscapes To avoid introducing arbitrary significance thresholds prior to the contextualization of changes in gene expression as a whole, a gene set (GS)-based approach to analyzing these data was chosen  [24]. In this context, a GS is an a priori-defined group of genes annotated to reflect one specific trait that its members share  [24]. A total of 26,251 GSs compiled, curated and updated by research consortia, such as the Gene Ontology project and Reactome were interrogated. The method for assembling this GS collection is described in  [25]. For this study, the resources were compiled on 1 March 2019 and downloaded from http://download. baderlab. org/EM_Genesets/ (accessed on 15 March 2019) were they are archived as well. Of the 26,251 GSs, 10,325 passed the default size thresholds of >10 and <500 member genes, therewith mitigating the limitations of gene set enrichment analyses (gsea) with respect to very small or very large GSs.
Immunohistochemistry To optimize immunohistochemical (IHC) staining protocols, several different antibodies were used on test tissues, and Proteinatlas (Proteinatlas. com) recommendations were carefully followed. Finally, IHC was used for IL-12 receptor beta 2 (IL12RB2) and human AXL detection, and immunofluorescence (IF) for IL-7 receptor alpha (CD127). For IL12RB2, we used an antibody from Sigma Aldrich (Burlington, MA, USA) cat. no HPA024168 with a dilution of 1:1000 for primary antibody, and an incubation time of 1 h in a humidity chamber. For AXL detection, we used an Invitrogen 7E10 monoclonal antibody from Thermofisher (Waltham, MA, USA) cat. no MA5-15504, with a dilution of 1:10,000 and 1 h incubation time in a humidity chamber. For the detection of IL-7 receptor alpha (CD127), we used immunofluorescence due to a lack of signaling by using standard IHC on tonsil test tissues.
More recently, podocytes simulation by IL-7 was shown to impair the glomerular filtration barrier, thus leading to nephrotic syndrome in adriamycin-treated rats. Moreover, IL-7 induced podocyte apoptosis with actin cytoskeletal disorganization and suppressed nephrin activation in experimental models  [14]. Glomerular transcriptome data indeed indicate that genes involved in IL-7 receptor signaling are upregulated in MCD, as compared to MN and control samples. Notably, in a fraction of samples, expression of activated, e. g. , phosphorylated, IL-7R alpha (CD127) protein was detectable by immunofluorescence staining. However, intriguingly, no evidence of increased expression of genes governing IL-7 production was observed, suggesting an ongoing modulation of glomerular responsiveness to a distantly produced cytokine.
We present results of a Transiting Exoplanet Survey Satellite (TESS) search for short-period pulsations in compact stellar objects observed in years 1 and 3 of the TESS mission, during which the southern ecliptic hemisphere was targeted. We describe the TESS data used and the details of the search method. For many of the targets, we use unpublished spectroscopic observations to classify the objects. From the TESS photometry, we clearly identify 43 short-period hot-subdwarf pulsators, including 32 sdB stars, eight sdOB stars, two sdO stars, and, significantly, one He-sdOB star, which is the first of this kind to show short-period pulsations. Eight stars show signals at both low and high frequencies, and are therefore "hybrid" pulsators. We report the list of prewhitened frequencies and we show the amplitude spectra calculated from the TESS data.
TESS photometry TESS is deployed in an elliptical, 2:1 lunar synchronous orbit with a period of 13. 7 days. Each annual cycle of TESS observations is split into sectors lasting two orbits, or about 27 days. The detector consists of four contiguous CCD cameras, each covering a 24 • × 24 • field of view (FoV), making up a 24 • × 96 • strip aligned along ecliptic latitude lines. During years 1 and 2, the data were stored with the short cadence (SC), lasting 120 s, and the long cadence (LC), lasting 1800 s. Since year 3 there has been an additional ultra-short cadence (USC) available, lasting 20 s, while the LC was trimmed to 600 s. The number of targets observed in the USC and SC is limited, and varies from sector to sector. When observations of one sector have been completed, the instrument's FoV is shifted eastward by 27 • , naturally pivoting around the ecliptic pole.
During years 1-3, it took 13 sectors to pivot around one pole, then the FoV was shifted to the other hemisphere for the next cycle. As a result, the regions near the ecliptic poles are observed during every sector and are known as the continuous viewing zones of TESS. We downloaded all available data of our targets from the Barbara A. Mikulski Archive for Space Telescopes (MAST) 2. To probe the p-mode region in an amplitude spectrum, we are limited to either the SC or USC data. The Nyquist frequency of the former is 4166 µHz, while for the latter it is 25 000 µHz. Our preference is to use the USC data, if available, with which the entire p-mode region is sampled. In the case of the SC data, the Nyquist frequency is in the middle of the p-mode region and aliasing becomes a serious issue. This issue can be resolved by the finite speed of light, which clearly separates the sub-and super-Nyquist regions in the amplitude spectrum, first discovered by  Baran et al.
Fourier analysis About 4000 hot subdwarfs (and hot-subdwarf candidates) have been observed by TESS in either the SC or USC mode. The list was assembled by members of Working Group 8 (WG8) of the TESS Asteroseismic Science Consortium (TASC)  3. Following a recent report by  Baran & Koen (2021) , we applied a detection threshold at 4. 5 times the median noise level to both the SC and USC data sets, regardless of the data coverage. In the case of data from just one sector, such a threshold corresponds to a false alarm probability (FAP) of 5% for the SC data and 14% for the USC data. For FAP = 0. 1%, the threshold would be around 5. 5, and hence frequencies below this threshold should be considered tentative. Precise confidence levels for other numbers of sectors can be derived from Eq. (  5 ) in  Baran & Koen (2021).
To search for a significant signal in amplitude spectra, we applied the FELIX tool  (Charpinet et al. 2010; Zong et al. 2016). Firstly, an automatic search was done, looking for variations beyond 1500 µHz in one sector, without combining multiple sectors (if a star was observed during more than one sector). Secondly, an individual check was carried out on the ∼100 candidates to see if detected variations were consistent with p-mode pulsations. We detected signals in the p-mode region in 43 targets. Twelve targets were recorded only in the SC mode, while 31 were recorded in the USC mode. We found ten new detections in SC data and 11 new detections in USC data, while the remaining two (SC) and 20 (USC) hot-subdwarf pulsators were known prior to the TESS mission. We present the full list of p-mode hot subdwarfs in Table  1.
Based on previous detections of p-mode sdB pulsators, we can see a correlation between the effective temperature and the frequencies of p modes: the lower the temperature, the lower the frequencies at which signals can appear. To verify our frequency choice for targets observed in the SC only, we need either the USC data or at least one sector of additional SC data. The latter argument is clearly seen for TIC 366656123. While analysing the contamination of TIC 1001123, we found TIC 754255960 to be a variable star. It shows four frequencies in the amplitude spectrum. However, the three higher frequencies all appear to be harmonics of the lower frequency, which indicates non-sinusoidal flux variation. This can be explained, for example, by binarity or pulsations in classical pulsators. Concerning the latter type and given the period of 0.
19942(6) days, it could be a high-amplitude Delta Scuti, RR Lyrae, or anomalous Cepheid star. We retrieved the light curve from the SC data and calculated the amplitude spectrum. Both plots are shown in Fig. 2. TIC 19690565 (Gaia DR2 5753155495252812544)  is a new sdB pulsator. Our fit to a spectrum taken with the 2. 56-meter NOT 4  in 2015 gives T eff = 31 150(300) K, log (g/cm s -2 ) = 5. 76(5), and log(n(He)/log (H)) < -3, indicating an sdB classification. TESS observed the star during Sector 34. We detected two peaks close to the Nyquist frequency and we selected the one in the super Nyquist region. We show the amplitude spectrum in Fig. 1 , and list the prewhitened frequencies in Table  2. TIC 95752908 (Gaia DR2 5756471068269831552 = TYC 4890-19-1) is a known sdB pulsator. Holdsworth et al.
Targets observed in the USC mode For all targets observed in the USC mode, there is also corresponding SC data available. In addition, some targets listed below were observed during specific sectors (1-13) in the SC mode only. Since the USC data provide us with a unique frequency identification, we decided not to include any SC data, even those taken in sectors without available USC data. Table  1  provides detailed sector information and we do not mention sectors with the SC data below. observed by TESS during Sector 37. We confirmed the low frequency, and we also detected its harmonic. Based on the shape and the high amplitude of the flux variation, the orbital signal must be a reflection effect, as in the case of TIC 142200764. We detected only one significant high frequency. We show an amplitude spectrum in Fig.
Its amplitude is much smaller in the TESS data than in the ground observations. The intrinsic amplitude could have changed since the last time the star was monitored, or remained the same but the wide band pass of the TESS photometric system could have averaged the flux variation. While the intrinsic effect is not easy to predict, it certainly takes place. We show an amplitude spectrum in Fig. 6  and list the prewhitened frequencies in Table  4. TIC 47377536 (Gaia DR2 3806303066866089216, UY Sex, PG 1047+003) is a known sdB pulsator and has been extensively studied since its independent discovery by  Billères et al. (1997) and O'Donoghue et al. (1998). Five and eight independent frequencies were reported by the former and latter authors, respectively. O'Donoghue et al. (  1998 ) confirmed all frequencies reported by  Billères et al.
The sdB classification is confirmed with a spectrum taken with the 1. 9 m telescope at SAAO. TESS observed the star during Sector 33 and we detected 11 frequencies in the p-mode region, although the frequencies are rather low for p-modes. We show an amplitude spectrum in Fig. 6  and list the prewhitened frequencies in Table  4. TIC 60257911 (Gaia DR2 656345533398638464, EPIC 211 823779) is a known sdB pulsator. Reed et al. (2018)  found the star to be a binary consisting of an sdB star and a main-sequence companion. The authors analyzed K2 data and reported 16 frequencies in the p-mode region. Just a few candidates for rotationally split modes were marked and a possible 11. 5(8)-day spin rate was estimated. TESS observed the star during Sectors 44-46. We detected only two frequencies, which were found in the K2 data to be the highest-amplitude frequencies.
Randall et al. (2014)  reported results of a mode identification of several frequencies detected in the spectrophotometric data collected by the authors. TESS observed the star during Sector 30. We show an amplitude spectrum in Fig. 6  and list the prewhitened frequencies in Table  4. The majority of frequencies we detected overlap with those listed by  Reed et al. (2012). In addition, we found one significant frequency in the g-mode region at a relatively high frequency. Such a frequency in this star has not been reported thus far. As shown by  Reed et al. (2012) , this star exhibits frequency and/or amplitude variation of the pulsations modes between two observational seasons. The signal in the amplitude spectrum calculated from the TESS data A48, page 8 of 18 A48, page 9 of 18 is also very unstable.
(2020a). The authors analyzed only the SC data reporting 43 frequencies, with six being high frequencies. TESS photometry was also taken during Sector 29. We list the USC frequencies in Table  4  and show an amplitude spectrum in Fig. 7. We detected only 34 frequencies, which is nine fewer than in the list reported by  Sahoo et al. (2020a) , and we did not find any new frequencies in Sector 29. TIC 220573709 (Gaia DR2 4720417758386878080, EC 03 089-6421) is a known sdO pulsator. Kilkenny et al. (2015)  listed it as an sdO star, and  Kilkenny et al. (2017)  reported two very A48, page 10 of 18 Fig. 7. Same as in Fig. 6 , but for another seven targets observed in the USC mode. A48, page 11 of 18 high frequencies, at ∼32. 12 mHz and ∼29. 26 mHz, detected in the photometric data. The authors speculated that this star could be a field counterpart of the ω Cen sdO variables  (Randall et al.
(1988)  listed it as an sdB star. Reed et al. (2019)  analyzed data collected during the K2 mission and reported 46 frequencies in the p-mode region and 16 frequencies in the g-mode region. The g-modes are rather surprising at the high T eff of ∼36 000 K. Among both p-and g-modes, the authors identified consistent rotationally split frequencies, derived the rotation period of 16. 18(57) days, and concluded that the star rotates as if it were a solid body. The authors analyzed spectroscopic data, reporting a clear contribution from a main-sequence companion. TESS observed the star during Sector 46. We detected just one frequency, which is the closest to f 37 listed by  Reed et al. (2019). This is surprising, since we would expect the frequency in the TESS data to be one of the highest-amplitude frequencies in the K2 data.
We show an amplitude spectrum in Fig. 9  and we list the prewhitened frequency in Table  4. While checking on the contamination of TIC 434923593, we found TIC 434923595 and TIC 434925198 to be variable stars. We retrieved the light curves from the SC data and calculated the amplitude spectra. There is only one frequency in each star detected. In TIC 434923595 we found 203. 674(32) µHz at 2. 18(29) ppt (S/N = 6. 3), while in TIC 434925198 we found 202. 291(39) µHz at 1. 12(  19 ) ppt (S/N = 5. 1). TIC 436579904 (Gaia DR2 3308791681845675136, V1405 Ori, KUV 04421+1416) is a known sdB pulsator. The  Kilkenny et al. (1988)  catalog lists it an sdB star, and  Koen et al. (1999)  analyzed time-series data and reported seven frequencies. Reed et al. (2020)  reported on their analyses of K2 data, which detected a flux variation caused by binarity, confirming the suspicion of  Koen et al.
(1999). The flux variation is caused by the reflection effect and the orbital period is 0. 398 days. Reed et al. (2020)  listed 107 high frequencies attributed to p modes. Some of the frequencies were explained by rotationally split multiplets. Additionally, 19 frequencies were detected in the low-frequency region and attributed to g modes. The authors used an asymptotic period spacing to identify the modal degree. TESS observed the star during Sectors 32, 43, and 44. The phase-folded light curve showing the reflection effect is presented in Fig. 11 , while the ephemeris is given in Table  3. First, we calculated amplitude spectra from single-sector data. In each spectrum we detected a handful of high frequencies. However, they vary significantly between sectors, and therefore we decided to analyze the sector data separately.
Summary We present the results of our search for short-period pulsating hot subdwarfs. We used TESS space data collected mostly during years 1 and 3, covering the southern ecliptic hemisphere. We report the stars that show signals at high frequencies, which we associate with p-mode pulsations. In most cases the amplitudes of p modes dominate amplitude spectra. However, there are two stars that have gravity modes of comparable amplitude. Except for those two stars, 41 hot subdwarfs in our sample can be considered p-mode-dominated pulsators. In total, we detected short-period pulsations in 43 hot-subdwarf stars. We found 32 (17 new) sdB stars, eight (three new) sdOB stars, two sdO stars, and one new He-sdOB star. We expect that the new hot-subdwarf p-mode pulsators presented in this paper will trigger follow-up monitoring in the future, either from the ground or from space (e.