dataesr/scientific-paragraphs-categorization huggingface dataset:
Transcript abundance normalized to sequencing depth and gene length for 4,948 extracellular protease genes from bacteria (4846) and fungi (102) was significantly affected by time (3-way PERMANOVA, F = 2. 8, p = 0. 038), litter amendment (F = 118. 9, p < 0. 001) as well as interactions between time:treatment (F = 26. 9, p < 0. 001), time:location (F = 7. 7, p < 0. 001) and location:amendment (F = 15. 5, p < 0. 001). Expression in unamended soil (no litter) generally increased slightly over time (Fig. 1A ), whereas root litter-amended soil had initially high expression that then decreased and leveled off over time (Fig. 1B ). The 73 distinct chitinase transcripts were expressed at a substantially lower level than the extracellular proteases (Fig. S2 ). The expression of the chit1 gene increased over time in litteramended soils and was higher than in the unamended treatments (3-way PERMANOVA, p < 0.
Taxonomy of extracellular proteases De novo assembled ORFs of extracellular proteases were taxonomically assigned by BLASTP best hit against the NCBI non-redundant database (NR). Since extracellular proteases are not marker genes and potentially lack high taxonomic resolution, we considered taxonomic assignments only at the order level or higher. In the rhizosphere, Gammaproteobacteria (formerly Betaproteobacteria) proteases were significantly upregulated (ANOVA, p < 0. 05) at all time points except 12 days (Fig. 3A ). Of 547 variants of proteases from this class, 442 were assigned as Burkholderiales. In contrast, at most timepoints, proteases of Cyanobacteria and Thermoproteota (formerly Thaumarchaeota) were significantly upregulated in bulk soil compared to the rhizosphere with the exception of 12 days (Fig.
DATA AVAILABILITY The R code used in this work is publicly available at https://github. com/ellasiera/ Protease_ISME_2022. All data used in this publication, including raw reads and genome collection, is publicly available. Metagenomes assembled MAGs can be found in NCBI PRJNA517182, stable isotope probing MAGs in http:// ggkbase. berkeley. edu/, single amplified genomes in IMG under study name Mediterranean Grassland Soil Metagenome and single amplified genomes in IMG, see sup. Table  S2  in Nuccio et al. , 2020 for accession numbers  [44]. Raw reads can be found in JGI IMG. For JGI ID's (accession numbers), see Supplementary Table  S1  in Nuccio et al. 2020  [44].
5σ confidence)  13. The H 2 O features are smaller compared to predictions for a cloud-free composition, indicating the presence of a thick high-altitude condensate layer in the planet's atmosphere  13. Considering the relatively low equilibrium temperature of ∼740 K of WASP-107b  6, 14, 15  , atmospheric models based on a solar abundance pattern predict that CH 4 is likely to be the dominant carbon-bearing molecule  16. However, the diagnostic power of the HST data in precisely determining the methane abundance is limited due to the overlap with a water feature. Despite this limitation, the absence of a prominent CH 4 feature in the HST data suggests that methane is likely depleted compared to what would be expected based on a solar abundance pattern  13. WASP-107b was observed with the Low-Resolution Spectrometer (LRS) of the Mid-Infrared Instrument (MIRI) on board JWST as part of the Guaranteed Time Observation Program 1280 on 19 -20 January 2023.
Fig. 3  provides evidence that only models incor-porating photochemistry predict a detectable level of SO 2 in WASP-107b. The large atmospheric scale height of WASP-107b enables highly efficient photochemical processes to operate within the ∼740 K temperature regime of this low-density planet, resulting in SO 2 volume mixing ratios being >5×10  -7  at pressures between 10 -7 -10 -4 bar. In contrast, the impact of eddy diffusion on the abundance of SO 2 remains minor at those pressure levels (see Extended Data Fig. 2 ), where the MIRI SO 2 features predominantly emerge (see below). Oxygen and sulphur elemental abundances are both determined by the atmospheric metallicity. We therefore explored the sensitivity of SO 2 to both the metallicity and the C/O ratio itself. Extended Data Fig. 2  shows that the SO 2 molar fraction in the upper atmosphere of WASP-107b is not very sensitive to the explored C/O ratio.
4 ). These findings reinforce the importance of photochemistry in establishing the SO 2 abundance in WASP-107b. At super-solar metallicity values, our models also predict a detectable CH 4 feature in the JWST MIRI wavelength range at 7. 8 µm (see Extended Data Fig. 3 ). The confirmation of the HST CH 4 non-detection  13  in the MIRI wavelength range is an interesting retrieval result as it raises major questions regarding the predominance of CH 4 in the atmosphere. One way of exploring this observation is to constrain the C/O ratio to be sub-solar, making the exoplanet being more oxygendominated  13. We investigated the impact of a sub-solar C/O ratio on the detectability of CH 4 and derived that the maximum molar fraction decreases by a factor ∼15 when lowering the C/O ratio from solar ( = 0.
A spatial filter of outlier detection was then applied to remove any hot pixel that would have been left in the sub-array. An optimal spectral extraction with a half-width source aperture of 4 pixels was then performed using the Eureka! package  18. We extracted 51 spectroscopic light curves between 4. 6 and 12. 1 µm with a 0. 15 µm bin width, ran a sigma-clipping of 20 integrations with a rejection threshold of 5σ, and trimmed the 250 first integrations to get rid of strong persistence effects. Light curves were then fitted using the MCMC emcee sampler  57  , the batman transit model  58  and both an exponential and a 2 nd order polynomial model for systematics. We used the white-light curve fit to refine the mid-transit timing, the semi-major axis and inclination parameters. The ratio of the planet radius over the stellar radius, the limb-darkening coefficients and the systematics parameters were then used as free parameters for all spectroscopic channels.
TEATRO data reduction setup We processed the data using the Transiting Exoplanet Atmosphere Tool for Reduction of Observations (TEATRO) that runs the jwst package, extracts and cleans the stellar spectra and light curves, and runs light curve fits. We subtracted the background per integration and per detector row and corrected for flagged pixels. We extracted the stellar spectra by summing the flux in a 12 pixel wide aperture, summed them between 5 -12 µm to obtain the white-light flux, and binned them in 51 wavelength bins from 4. 6 to 12. 1 µm (bin width of 0. 15 µm) to obtain the spectroscopic light curves. We discarded the first 1. 4 hr to remove the initial decay, normalised the light curves by the out-of-transit flux, and removed outliers. We fit the light curves by a transit light curve model computed with the exoplanet package  59, 60  and a linear trend.
We fit that model to the data using a MCMC procedure based on the PyMC3 package as implemented in exoplanet  59, 60. We refined the mid-transit time and impact parameter from a white light curve fit, and let only the ratio of the planet radius over the stellar radius and linear trend as free parameters for the spectroscopic light curve fits. We used the median of the posterior distributions as final parameters, and computed the transit depth uncertainties by a quadratic sum of the standard deviations of the residuals of the in-and out-of-transit points divided by the square root of their respective number of points. The TEATRO transmission spectroscopy results are provided in Extended Data Table  1  and shown in Fig. 1.
The white light curve fit was used to refine the mid-transit timing t 0 , the semi-major axis a and inclination i. The transit ephemeris were taken from ref. 61  , other system parameters from ref. 62  , and quadratic limb-darkening coefficients computed with the ExoTETHyS package  55  , using the parameterised quadratic parameters from ref. 63. The parameters retrieved from the white light curve fitting are in agreement; see Extended Data Table  2. We found that with a single-transit observation we reached a spectro-photometric precision of ∼80 ppm in the 7 -8 µm range at a spectral resolution R = 50 (see Extended Data Fig. 1 ). We used the JWST Exposure Time Calculator (ETC)  64  to estimate the signal-to-noise on a single integration. Using this estimate, we simulated the light curves per spectral channel assuming a constant transit depth equal to the observed band-averaged transit depth.
The simulated light curves were then fitted using the CASCADe package to estimate the error of the simulated transit spectrum. This estimate is shown as the solid curve in Extended Data Fig. 1. All three data reductions are consistent with this estimate, and show that our results are close to the photon noise limit of the instrument. Note that the noise limit estimate based on the ETC still contains uncertainties about the exact value of the detector gain and thus photon conversion efficiency and the level and modelling of the infrared background at the longer wavelengths. The derived band-averaged transit depth is 20 463 ± 39 ppm (see Fig. 1 ), which is within 1σ of the previously measured transit depth at near-infrared wavelengths with the HST (see Supplementary Information). The difference between pairs of data reduction methods was then calculated as TD 1 (λ) -TD 2 (λ) err 2 1 (λ) + err 2 2 (λ) , (1) with TD 1 and TD 2 being the transit depth of reduction method 1, or 2, respectively, at wavelength λ and err 1 and err 2 being the corresponding 1σ errors shown in Extended Data Fig.
ARCiS retrieval setup The atmospheric modelling and retrieval code ARCiS 19 was used to perform retrievals using a free parameterised retrieval setup. 77. For each species a log-uniform prior from 10 -12 to 1 was taken. The remaining atmosphere consists of H 2 and He with a number density ratio of 0. 85:0. 15. The cloud is modelled as a Gaussian layer with a certain width and optical depth at 9 µm. The specific cloud density as a function of pressure P is given by f cloud = g τ cloud κ cloud P σ P √ 2π exp - 1 2σ 2 P log P P 0 2 , ( 2 ) where g is the gravitational acceleration of the planet, κ cloud is the cloud opacity at 9 µm. The parameters τ cloud (the cloud optical depth), P 0 (the cloud pressure), and σ P (the cloud width) are retrieval parameters. Finally, we consider partial cloud coverage using a retrieval parameter f coverage between zero and one.
In Extended Data Fig. 8  we show the resulting detection significance as a function of the maximum wavelength used in the analysis. It is clear that clouds are required no matter what wavelength range we choose. As expected, if we remove the entire wavelength range where the silicate feature is present (so wavelengths above 9. 5 µm) the silicate clouds are no longer detected. In these cases it is seen that the model prefers the setup with less parameters, which is the parameterised cloud setup. Silicate clouds are preferred with a significance of 5. 7σ even if we remove all MIRI observation with wavelengths longer than 10 µm. This significance quickly increases if we increase the maximum wavelength used in the analysis showing that the detection of silicate clouds is a robust result. (Photo)chemical model setup The goal of the forward (photo)chemical models is to understand the gas-phase formation of molecules in the atmosphere of WASP-107b and to derive the sensitivity of the predicted molar fractions on the model's input parameters.
The rotation period of ∼47 days 104 implies an age of ∼5. 6 Gyr for HD 85512, hence considerably older than WASP-107 with an estimated age of ∼3. 4 Gyr 7. Therefore, it is not unexpected that HD 85512 is significantly less magnetic and/or has less coronal activity than WASP-107. However, for our photochemical models mainly the NUV flux is of importance, since the X-ray emission primarily impacts photoionization which is not included in our models. We therefore can use the MUSCLES HD 85512 spectrum as representation of WASP-107's SED. Each chemical model was executed with a vertical resolution comprising 130 layers spanning the pressure range from 10 -7 to 100 bar. Subsequently, the hydrodynamical input and the abundances resulting from the chemical kinetics simulations are used to compute synthetic transmission spectrum, using the radiative transfer package petitRADTRANS 20 (see above).
Next to the line opacities described in the petitRADTRANS retrieval setup, we also include the line absorption opacities listed in ref. 97. Since the primary goal of the forward model computations is to understand the gas-phase formation of SO 2 in this planet independent of cloud-formation, no condensate opacity was added in this last post-processing setup. For each pressure layer, the mean molecular weight is calculated based on the mixture that resulted from the disequilibrium chemistry models. The planetary radius at reference pressure (0. 01 bar) was set to 0. 9 R p. Finally, the predicted synthetic spectra are rebinned to the spectral resolution of the WASP-107b JWST MIRI data. Extended Data -Figure  1 : 1σ uncertainties on the transit depths as a function of wavelength for the three data reductions.
The blue dots show the results from the CASCADe code, the green dots using the Eureka! package and the black dots the TEATRO routines. The purple line shows the photon, dark and read noise limited performance estimate based on ETC calculations for comparison. This plot displays the performance of the MIRI/LRS instrument and the reliability of the three data reduction methods. The plot demonstrates that within the uncertainty of the error estimates, the data reductions are consistent with photon noise limit performance and that no major systematics are left.
Acknowledgments This work is based [in part] on observations made with the  NASA/ESA/CSA James Webb Space Telescope. The data were obtained from the  Mikulski Archive for Space Telescopes  at the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , under  NASA  contract  NAS 5-03127  for JWST. These observations are associated with program 1280. MIRI draws on the scientific and technical expertise of the following organisations:  Ames Research Center, USA ;  Airbus Defence and Space, UK ;  CEA-Irfu ,  Saclay, France ;  Centre Spatial de Liège, Belgium ;  Consejo Superior de Investigaciones Científicas, Spain ;  Carl Zeiss Optronics ,  Germany ;  Chalmers University of Technology, Sweden ;  Danish Space Research Institute, Denmark ;  Dublin Institute for Advanced Studies, Ireland ;  European Space Agency, Netherlands ;  ETCA, Belgium ;  ETH Zurich, Switzerland ;  Goddard Space Flight Center, USA ;  Institut d'Astrophysique Spatiale, France ;  Instituto Nacional de Técnica Aeroespacial, Spain ;  Institute for Astronomy, Edinburgh, UK ;  Jet Propulsion Laboratory, USA ;  Laboratoire d'Astrophysique de Marseille (LAM), France ;  Leiden University, Netherlands ;  Lockheed Advanced Technology Center (USA) ;  NOVA Opt-IR group  at  Dwingeloo, Netherlands ;  Northrop Grumman ,  USA ;  Max-Planck Institut für Astronomie (MPIA) ,  Heidelberg, Germany ;  Laboratoire d'Etudes Spatiales et d'Instrumentation en Astrophysique (LESIA), France ;  Paul Scherrer Institut, Switzerland ;  Raytheon Vision Systems, USA ;  RUAG Aerospace, Switzerland ;  Rutherford Appleton Laboratory (RAL Space), UK ;  Space Telescope Science Institute, USA ;  Toegepast Natuurwetenschappelijk Onderzoek (TNO-TPD) ,  Netherlands ;  UK Astronomy Technology Centre, UK ;  University College London, UK ;  University of Amsterdam, Netherlands ;  University of Arizona, USA ;  University of Bern, Switzerland ;  University of Cardiff, UK ;  University of Cologne, Germany ;  University of Ghent ;  University of Groningen, Netherlands ;  University of Leicester, UK ;  University of Leuven, Belgium ;  University of Stockholm, Sweden ; Utah.
D. B. is supported by  Spanish MCIN /AEI/10. 13039/501100011033  grant  PID2019-107061GB-C61  and and No. MDM-2017-0737. L. D. acknowledges funding from the  KU Leuven   Interdisciplinary Grant  ( IDN/19/028 ), the  European Union   H2020-MSCA-ITN-2019  under Grant no. 860470  ( CHAMELEON ) and the  FWO  research grant  G086217N. I. K. acknowledges support from grant  TOP-1 614. 001. 751  from the  Dutch Research Council (NWO). O. K. acknowledges support from the  Federal Ministery of Economy (BMWi)  through the  German Space Agency (DLR). J. P. P. acknowledges financial support from the  UK.
Data availability The JWST MIRI data presented in this paper are part of the JWST MIRI GTO program (Program identifier (PID) 1280; P. I. P. O. Lagage). The JWST data will be publicly available in the Barbara A. Mikulski Archive for Space Telescopes (MAST; https://archive. stsci. edu/) after January 20, 2024, and can be found either using the program identifier or using the Data Object Identifier (DOI): https://doi. org/10. 17909/as3s-x893. The DOI link can also be used to retrieve the publicly available HST WFC3 data used in this paper from MAST. The NUV data are in an online publicly-accessible archive: https://heasarc. gsfc. nasa. gov/cgi-bin/W3Browse/swift. pl. The HST data is available in the MAST archive and can be found using DOI: https://doi. org/10. 17909/as3 s-x893. The XMM X-ray data archive is available at https://nxsa.
Code availability The codes used in this publication to extract, reduce, and analyse the data are as follows. The data reduction pipeline jwst can be found at https://jwst-pipeline. readthedocs. io /en/latest/. The data analysis codes Eureka!, CASCADe, and TEATRO can be found respectively at https://eurekadocs. readthedocs. io/en/latest/, https://gitlab. com/jbouw man/CASCADe, and https://github. com/ncrouzet/TEATRO. The CASCADe-filtering, and CASCADe-jitter sub-packages can be found, respectively, at https://gitlab. com/jbouw man/CASCADe-filtering, and https://gitlab. com/jbouwman/CASCADe-jitter. The atmospheric model codes used to fit the data can be found at https://www. exoclouds. com/ for the ARCiS-code  19  and at https://petitradtrans. readthedocs. io/en/latest/ for the petitRADTRANS-code  20.
The XSPEC package  48  is available at https://heasarc. gsfc. n asa. gov/xanadu/xspec/. The HEASoft package (including the Swift UVOT tools) is available at https://heasarc. gsfc. nasa. gov/lheasoft/download. html (version 6. 31. 1 was used in our work). The NUV Swift-project pipeline is available at https://swift. gsfc. nasa. gov/qu icklook/swift_process_overview. html. The VULCAN chemical network can be found at https://github. com/exoclime/VULCAN/blob/master/thermo/SNCHO_photo_netw ork. txt.
15. 15 Our base model used in Extended Data Fig. 2 has an intrinsic temperature of 400 K, a solar C/O ratio, a metallicity of 10× solar, and a log 10 (K zz , cgs) = 10. Our 1D chemical kinetics model treats thermochemical and photochemical reactions. The thermochemical network is based on the C-H-N-O-S network from VULCAN for reduced atmospheres containing 89 neutral C-, H-, O-, N-, and S-bearing species and 1028 total thermochemical reactions (i. e. , 514 forward-backward pairs). The photo-absorption cross-sections are taken from the KIDA database 101 and complemented with additional sulphur photo-absorption cross sections (O. Venot, private communication). The full network cross-sections were benchmarked against WASP-39b 5.
Science and Technology Facilities Council, and the UK Space Agency, and acknowledges the advice of the Swift project team, and especially Kim Page (UK Swift Science Data Centre at University of Leicester) in planning and analysis of the Swift observations. G. O. acknowledge support from the Swedish National Space Board and the Knut and Alice Wallenberg Foundation. P. T. acknowledges support by the European Research Council under Grant Agreement ATMO 757858. I. P. W. acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 758892, ExoAI), from the Science and Technology Funding Council grants ST/S002634/1 and ST/T001836/1 and from the UK Space Agency grant ST/W00254X/1. F. A. M. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no.
860470. E. D. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie actions Grant Agreement no 945298-ParisRegionFP. G. V. L. acknowledges that some results of this work were partially achieved at the Vienna Scientific Cluster (VSC). L. H. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 860470. T. K. ac- (c)   not included. reduction knowledges funding from the KU Leuven Interdisciplinary Grant (IDN/19/028). L. C. acknowledges support by grant PIB2021-127718NB-100 from the Spanish Ministry of Science and Innovation/State Agency of Research MCIN/AEI/10. 13039/501100011033. E. vD. acknowledges support from A-ERC grant 101019751 MOLDISK.
In the realm of social media, users frequently convey personal sentiments, with some potentially indicating cognitive distortions or suicidal tendencies. Timely recognition of such signs is pivotal for effective interventions. In response, we introduce two novel annotated datasets from Chinese social media, focused on cognitive distortions and suicidal risk classification. We propose a comprehensive benchmark using both supervised learning and large language models, especially from the GPT series, to evaluate performance on these datasets. To assess the capabilities of the large language models, we employed three strategies: zero-shot, fewshot, and fine-tuning. Furthermore, we deeply explored and analyzed the performance of these large language models from a psychological perspective, shedding light on their strengths and limitations in identifying and understanding complex human emotions.
Our evaluations underscore a performance difference between the two approaches, with the models often challenged by subtle category distinctions. While GPT-4 consistently delivered strong results, GPT-3. 5 showed marked improvement in suicide risk classification after fine-tuning. This research is groundbreaking in its evaluation of large language models for Chinese social media tasks, accentuating the models' potential in psychological contexts. All datasets and code are made available at: https://github. com/HongzhiQ/SupervisedVsLLM-EfficacyEval.
11  It is in this context that the emergence and proliferation of large language models are particularly noteworthy. Large language models, characterized by their expansive parameters and the depth of their training datasets, stand as the state-of-the-art in the framework of computational linguistics. 12  Their potential lies in their ability to comprehend and emulate human-like text nuances. Despite their promising potential, several studies have sought to validate their practical implications. For instance, Xu et al. 13  examined four public datasets related to online social media sentiment detection. However, their study focused solely on English data, and the classification granularity was relatively broad. To date, there is a notable gap in research concerning the Chinese context, particularly in the area of fine-grained emotion recognition, which is often of greater significance.