dataesr/scientific-paragraphs-categorization huggingface dataset:
For our benchmark, we downloaded the ECOD (F99 v. 20230309) PDB database and applied the same MMseqs2 and Foldseek clustering procedure used for the AFDB. We conducted an ECOD cluster purity analysis on all non-singleton clusters by measuring the pairwise cluster member consistency at different hierarchy levels. The analysis revealed high average consistency rates of 99. 6%, 98. 6%, 97. 4%, 96. 8% and 72. 8% for ECOD's A-group, X-group, H-group, T-group and F-group, respectively. This indicates an effective clustering of homologous proteins, demonstrating a nearly exclusive distinction between homologues and analogues. The high level of consistency in our clustering is mainly attributed to the stringent E-value of 10 -2 ; when raising it to 10, the consistencies decrease to 69. 7%, 55. 7%, 53.
Cluster purity analysis To assess cluster purity, we followed a two-step approach. First, we calculated the average LDDT and TM score per cluster to assess the structural similarity. For this, we aligned the representative to the cluster members using the structurealign -e INF -a module in Foldseek and reported the alignment LDDT and TM score using --format-output lddt,alntmscore. For each cluster we computed the mean illustrated in Fig. 1c. Second, we evaluated the Pfam consistency of each cluster by using Pfam labels obtained from UniProtKB. We took into account only the clusters that have at least two sequences with Pfam annotations and we calculated the fraction of correctly covered Pfam domains for all Pfam sequence pairs ignoring self-comparison. We define true positives as a pair of Pfam domains belonging to the same clan.
For each pair, we computed the consistency scores by true-positive count divided by the count of Pfams in the reference sequence. Finally, we computed the mean overall pair scores. This approach enabled us to determine the proportion of sequences within a given cluster that shared the same Pfam annotation. Finally, we also calculated the EC number consistency of each cluster. EC numbers were extracted from UniProtKB. The EC consistency was evaluated similarly to the Pfam consistency but was done four times according to the four classes of the EC number. We considered only the clusters with at least two sequences that have EC annotations. At each class of the EC number, the annotation without any code at the class was ignored. For each pair as the Pfam consistency, the consistency scores were computed by the true-positive count divided by the number of ECs in the sequences in the pair avoiding self-comparison.
Dark clusters and LCA To eliminate clusters similar to previously known experimental structures, we conducted a search using Foldseek against the PDB (v. 2022-10-14) for each cluster representative, with an E-value threshold of 0. 1. We then excluded clusters annotated with Pfam domains by searching the cluster representatives using MMseqs2 with parameters -s 7. 5 --max-seqs 100000 -e 0. 001 against the Pfam database. Finally, we removed clusters with members annotated with Pfam or TIGRFAM20 annotations in the UniProt/TrEMBL and SwissProt database. To determine the LCA of each cluster, we used the lca module in MMseqs2 (ref. 46) ignoring the two taxa (1) 12,908 unclassified sequences and (2) 28,384 other sequences. We visualized the LCA results using a Sankey plot generated by Pavian  47.
Web server We developed a web server to allow for user-friendly exploration of clusters, their members and related similar clusters. The server was implemented using a REST-based client-server architecture, with a VueJS front-end and a NodeJS back-end. The clustering-related information is accessed through an SQLite database and information related to individual structures through Foldseek compatible databases through a C++-based NodeJS-extension for fast read-in and search. Similar to the Foldseek webserver, we used NGL  50  to visualize structures and WebAssembly-based versions of PULCHRA  51  to restore full protein structures from our stored C-alpha traces and TM-align for pairwise structure alignments of cluster members to their representatives. To visualize the taxonomic distribution, we implemented Sankey diagrams inspired by Pavian.
Acknowledgements M. S. acknowledges support by the  National Research Foundation of Korea  grants ( 2020M3-A9G7-103933 ,  2021-R1C1-C102065  and  2021-M3A9-I4021220 ),  Samsung DS  research fund and the  Creative-Pioneering Researchers Program  through  Seoul National University. P. B. is supported by the  Helmut Horten Stiftung  and the  ETH Zurich Foundation. M. M. acknowledges support by the  National Research Foundation of Korea  (grant  RS-2023-00250470 ). We thank  E. L. Karin  for revising text and  H. Kim  for improving the visualization.
Data availability Clustering data are freely and publicly available (CC-BY) online (https://cluster. foldseek. com/). All data generated and used for the analyses can be downloaded online (https://afdb-cluster. steineggerlab. workers. dev). AlphaFold database v. 3 (https://alphafold. ebi. ac. uk/) was used for the analysis and is currently available at gs:// public-datasets-deepmind-alphafold. For the analysis, we used Pfam v. 34. 0 (https://ftp. ebi. ac. uk/pub/databases/Pfam/releases/Pfam34. 0), PDB (14 October 2022; https://www. rcsb. org), UniProt TrEMBL 2022_03 (https://ftp. ebi. ac. uk/pub/databases/uniprot/), SwissProt 2022_03 (https://ftp. ebi. ac. uk/pub/databases/uniprot/), ECOD 20230309 (http://prodata. swmed. edu/ecod/) and the MALISAM (http://prodata. swmed. edu/malisam/) database.
Code availability The structural clustering method is available at https://foldseek. com/, is implemented in Foldseek v. 4. 645b789 and is available as free and open-source software (GPLv3). MMseqs2/Linclust v. 14. 7e284 is available online (https://mmseqs. com/). The cluster analysis was performed using goatools v. 1. 2. 4 (https://github. com/tanghaibao/goatools), Deep-FRI v. 0. 0. 1 for GO predictions (https://github. com/flatironinstitute/ DeepFRI) and ColabFold v. 1. 5. 2 for structure prediction (https://colabfold. com). For plotting, Python v. 3. 10. 6 (https://www. python. org/), Matplotlib v. 3. 6. 2 (https://matplotlib. org/), seaborn v. 0. 12. 2 (https:// github. com/mwaskom/seaborn), ChimeraX v. 1. 5 (https://www. cgl. ucsf. edu/chimerax/), Pavian commit: cd2f21 (https://fbreitwieser.
Follow-up observation When blind searching CRAFTS data (described in the next section), ten candidates, including three previously known Hi absorption systems (UGC 00613, 3C 293 and 4C +27. 14) were detected. Two known absorbers (UGC 00613 and 3C 293) and all new absorption systems are further confirmed by the CRAFTS data of several neighbouring beams with a high signal-to-noise ratio (see  Sects. 3. 4 and 4). In order to confirm the signal of the candidates and obtain a more significant Hi absorption feature for 4C +27. 14, we also carried out a nighttime follow-up To suppress the fluctuations in the bandpass and remove the sky signal, the follow-up observation used the ON-OFF tracking mode. The integration time for both source-on and source-off observations is 990 s. The cycle of 330 s source-on followed by 330 s source-off observation was repeated three times per A40, page 4 of 18 target.
Analysis To begin, we extracted spectra at each time point from the data cubes. We processed the XX and YY spectra separately to search for Hi absorption and combined them to create the final spectra if selected as candidates. For each XX and YY polarisation, we masked the RFI and obtained the band baseline by smoothing with a low-pass filter using the medfilt function  (Van Rossum & Drake 1995; Virtanen et al. 2020). The median filter window size is set to 0. 65 MHz, carefully selected to be smaller than the chronically present RFI and bandpass fluctuations yet larger than the typical size of Hi absorption profiles. We note that we only flag strong RFI ( 5σ) in our data. This is because some true Hi absorbers may reside in the location of chronically present RFI. Furthermore, the spurious candidates produced by RFI will be excluded later in our blind search pipeline.
We then searched for the Hi absorbers blindly by cross-correlating the flux spectrum with templates. This matched-filtering approach was developed for identifying emission signals in the Arecibo Legacy Fast ALFA survey  (Saintonge 2007)  and is more sensitive and faster than the usual peakfinding algorithms. Here, we provide a brief summary of the key steps. Spectra at each time point were extracted from the data cubes. After the removal of the baseline of the bandpass, the matched-filtering approach was applied to find the absorption profiles. The final candidates were selected using the transit information recorded by the 19 beams of FAST.
Standing waves However, even with the threshold shown in Sect. 3. 1, tens of thousands of candidates have passed this screening. Nearly all of the spurious candidates originate from the fluctuations of the bandpass and chronically present RFI. The instability of the bandpass, which is mainly the result of standing waves, makes it difficult to remove the bandpass baseline accurately and find the true absorption signals. Most of these are excluded by using the multi-beam information in the drift scans, but the variation of the bandpass can be a significant source of error in such searches. In the Hi absorption feature search above, we ran our pipeline with the raw data before bandpass calibration. We also tried to carry out the analysis of the bandpass-calibrated data. The built-in noise diode of the FAST receiver is used as a real-time calibrator to estimate the bandpass variations.
In Step 2, candidates that could be detected in more than six continuous or discontinuous time points in the data from every single beam were excluded. In Step 3, we gave priority to candidates in the remaining catalogue if they could be detected in more than one beam distributed along the direction of the scanning. In Step 4, we visually examined the candidates that survived after Step 1 and Step 2 and compared these visual candidates with the automated candidates from Step 3. We take the known Hi absorption system UGC 00613 as an example of how our candidate selection pipeline works. Using the matched-filtering approach, the absorption profile of UGC 00613 can be detected in the data from Beam 10, Beam 4, and Beam 13, respectively. In Fig. 6 , we show the raw spectra (left column) and baseline-removed spectra (right column) from Beam 10, Beam 4 and Beam 13.
The difference between the T b (ν) and T c is then If T s > c f T c , the spectral line will be seen in emission, while if T s < c f T c , the absorption dominates. For most Hi absorption observations, ν) ). After converting the frequency (ν) to velocity (V), the optical depth can be expressed as ∆T (ν) = T b (ν) -T c = (T s -c f T c )(1 -e -τ(ν) ). ( 10 T s c f T c , then ∆T (ν) = -c f T c (1 -e -τ( τ(V) ≈ -ln(1 + ∆T (V)/(c f T c )) (11) in the limit T s c f T c , where T c can be deduced from the linefree parts of the spectrum or the other database. In this work, we used the T c from the NASA/IPAC Extragalactic Database 1 (NED). The optical depth τ is proportional to N Hi /T s , and the Hi column density is given by N Hi [cm -2 ] = 1. 82 × 10 18 T s [K] τ(V)dV[km s -1 ]. ( 12 ) Throughout this paper, we assume that c f = 1  (Maccagni et al.
4C +27. 14 The galaxy 4C +27. 14 is a Type II Seyfert galaxy and displays the properties of a radio-loud AGN  (Parisi et al. 2014). A broad Hi absorption profile has been observed in the direction of radio continuum source 4C +27. 14  (Stanimirović et al. 2014)  for the first time. The Hi absorption lines are detected in the CRAFTS drift-scan data with a blind search and the follow-up observation using ON-OFF tracking mode. The absorption signal of 4C +27. 14 is faint, only detected in Beam 11 in the CRAFTS data. The fluctuations in the bandpass are suppressed by subtracting the source-off data from the source-on data. The calibrator 3C 48 was also observed to calibrate the data. A second-order polynomial function was fitted to the absorption-line-free parts of the spectrum to remove the baseline of the bandpass.
69-052548. 8) has been detected by the Wide-field Infrared Survey Explorer (WISE,  Cutri et al. 2013 ) and cross-identified as the radio source NVSS J231240-052547 in the NASA/IPAC Extragalactic Database. The left and right panels of Fig. 13  show the image centred at NVSS J231240-052547 (or WISEA J231240. 69-052548. 8), constructed using A40, page 10 of 18 Notes. The values are retrieved from the NASA/IPAC Extragalactic Database  (Helou et al. 1991). The redshifts of NVSS J231240-052547 and NVSS J053118+315412 are SDSS and 2MASS photometric estimates for the nearest optical galaxies cross-matched to the radio sources. it is an AGN candidate. Combining with the W2-W3 value of 3. 752 mag, this source lies in the intersecting region of Seyferts and the Ultra-luminous Infrared Galaxies (ULIRGs) in the WISE colour-colour diagram  (Wright et al.
85 ± 0. 77 mJy, an FWHM of 89. 53 ± 2. 85 km s -1 , τdv = 55. 81 ± 2. 23 km s -1 and N Hi = 1. 016T s × 10 20 cm -2 K -1. The radio source has also been cross-identified to a source in the 2MASS Two Micron All Sky Survey (2MASS;  Skrutskie et al. 2006)  and one in the mid-infrared band by WISE by the NASA/IPAC Extragalactic Database. We show the image centred at NVSS J053118+315412 (or WISEA J053118. 83+315412. 0), constructed using infrared data at 4. 618 microns (W2) from WISE and radio data at the S band from VLASS, in the middle and right panels of Fig. 15. The straight cross marks the position of NVSS J053118+315412. The WISE magnitudes for WISEA J053118. 83+315412. 0 are presented in Table  3. The W1-W2 colour is 1. 630, which is higher than the value of 0. 8 often used to select AGN candidates and implies that the mid-infrared emission comes mainly from the AGN.
Combined with the W2-W3 value of 3. 598 mag, NVSS J053118+315412 lies in the region of QSOs in the WISE colour-colour diagram. The 2MASS counterpart has J-band[1. 235 µm], H-band[1. 662 µm], and K s -band[2. 159 µm] magnitudes of 15. 180 ± 0. 043, 13. 943 ± 0. 044, and 13. 219 ± 0. 028. We should note that although the velocity of the absorption is ∼ 1500 km s -1 higher than the velocity of WISEA J053118. 83+315412. 0 (or NVSS J053118+315412) as shown in the NED database (given by 2MASS Redshift Catalog,  Bilicki et al. 2014) , this velocity difference is smaller than the typical precision of 2MASS redshift estimates (∼12%), indicating the velocity of absorption is similar to that of NVSS J053118+315412. The complicated structures of the absorption profile indicate the interaction between ISM and host AGNs.
Spurious detections The other five candidates in the follow-up observations are also checked and found not to be true Hi absorptions. The spectra of these sources, together with 4C +27. 14, are shown in Fig. 12. The spectra are obtained by subtracting the source-off spectra from the source-on spectra and are calibrated with 3C 48. The orange dashed lines show the central frequency position of the absorber candidates (see Fig. 8  for a comparison). Compared with the spectra in CRAFTS (Fig. 8 ), we found that Candidates 1, 3, 4, and 5 arise from the combination of the fluctuations of the bandpass and the Hi emission, while Candidate 2 comes from the fluctuations of the bandpass. Although there are significant Hi emission profiles in the source-on and source-off spectra from a follow-up observation, in the CRAFTS data they are mixed with the bandpass fluctuations induced by standing wave and chronically present RFI, making it difficult to distinguish the Hi emission from the bandpass ripples.
Correction for standing waves There have been attempts to suppress the standing waves in radio data. Kern et al. (2019)  described techniques for modelling and removing the reflections and standing waves in the Hydrogen Epoch of Reionisation Array (HERA) data  (Kern et al. 2020). They showed that by combining reflection calibration and crosscoupling subtraction, standing waves could be suppressed down to the integrated noise floor. Li et al. (2021)  recognised the standing waves in the Tianlai cylinder array and found the oscillating modulations in the frequency spectrum could be mitigated by removing the corresponding components in the Fourier space. Tests are also being carried out to correct the FAST standing waves. By fitting or extracting peaks in Fourier space caused by standing wave components with the period of ∼1.
Information from neighbouring beams With so many spurious signals originating from the bandpass fluctuations and RFI being selected as preliminary candidates, it is of vital importance to find a valid and easy-to-use method to distinguish the intrinsic Hi absorption from man-made signals. Fortunately, the 19 beams of the L-band receiver and the driftscan survey strategy provide an effective solution (described in Sect. 3. 4). The unique design of FAST makes it a powerful instrument for Hi absorption searching. Considering that only approximately half of the frequency band from a small part of CRAFTS observable sky has been searched, we expect that a larger number of Hi absorbers could be detected with the twopass full survey. In the blind search presented in this work, we ran our pipeline in the data from every single beam separately.
0), the ALFALFA absorption pilot survey  (Darling et al. 2011) , and model fits to Hi emission-line observations with the Westerbork Synthesis Radio Telescope (WSRT) at z = 0  (Zwaan et al. 2005)  and DLA systems in SDSS-DR7 at z ∼ 3  (Noterdaeme et al. 2009 ). The predicted f (N Hi , X) for the Hi absorption searching in map-making data from the full coverage of the FAST observable sky is comparable with the FLASH f (N Hi , X) upper-limit prediction in super-DLA (N Hi 2 × 10 20 cm -2 ) regions. We should note that, limited by the RFIs, computing resources and authorised follow-up observation time, only the 1300-1450 MHz band is used for absorption line searches and sensitivity A40, page 16 of 18 calculations. With more comprehensive data processing methods and more frequency bands (1000-1300 MHz) and a larger sky searched in the near future, a much larger comoving absorption path and higher sensitivity to N Hi frequency distribution function could be obtained with CRAFTS.
Summary In this paper, we present the early science results of a purely blind search for extragalactic Hi 21 cm absorption lines in the CRAFTS drift-scan data from FAST. A nearly 643. 8 h and 3155 deg 2 survey (covers 44827 radio sources with a flux density greater than 12 mJy) from the Commensal Radio Astronomy FAST Survey (CRAFTS) was searched for Hi absorbers. The total comoving absorption path length spanned by our data for intervening and associated absorption is ∆X inv = 1. 05 × 10 4 (∆z inv = 9. 80 × 10 3 ) and ∆X asc = 1. 63 × 10 1 (∆z asc = 1. 51 × 10 1 , assuming T s /c f = 100 K). The completeness correction, determined using mock absorption signals with FWHM = 30 km s -1 , has been applied to the estimates of a comoving absorption path. All polarisation data of all 19 beams were processed.
Due to the RFI, only the relatively clean data in the frequency range of 1. 3 GHz to 1. 45 GHz were investigated. We used a matched-filtering approach to detect the Hi absorption profiles at each time point of the drift scan. By cross-correlating information from different beams, we effectively eliminated spurious signals and identified three previously known Hi absorbers (UGC 00613, 3C 293, and 4C +27. 14) as well as two newly discovered ones (NVSS J231240-052547 and NVSS J053118+315412). We fitted the Hi profiles with multi-component Gaussian functions and calculate the redshift, FWHM, flux density, optical depth, and Hi column densities for each source. The newly discovered Hi absorption feature in NVSS J053118+315412 displays associated absorption with both blueshifted and redshifted wings, as well as redshifted components.
We found optical and infrared counterparts in data archives along the line of sight where the two absorptions were detected, and a follow-up observation with redshift measurement will determine whether NVSS J231240-052547 is associated with the Hi absorbers and the nature of the counterparts. Foundation of China (Grants No. 11929301), Thoth Technology Inc, Alexander von Humboldt Foundation, and the National Science and Technology Council (NSTC) (111-2123-M-001-008-, and 111-2811-M-001-040-). Computations were performed on the SOSCIP Consortium's [Blue Gene/Q, Cloud Data Analytics, Agile and/or Large Memory System] computing platform(s). SOSCIP is funded by the Federal Economic Development Agency of Southern Ontario, the Province of Ontario, IBM Canada Ltd. , Ontario Centres of Excellence, Mitacs and 15 Ontario academic member institutions.
Acknowledgements. We acknowledge the support of the  National SKA Program of China , No. 2022SKA0110100. This work is supported by the  National Key R&D Program   2017YFA0402603 , the  Ministry of Science and Technology (MoST)  inter-government cooperation program China-South  Africa Cooperation Flagship  project  2018YFE0120800 , the  MoST grant   2016YFE0100300 , the  National Natural Science Foundation of China (NSFC)  key project grant  11633004 , the  Chinese Academy of Sciences (CAS) Frontier Science Key Project QYZDJ-SSW-SLH017  and the  CAS Interdisciplinary Innovation Team  grant ( JCTD-2019-05 ), the  NSFC-ISF  joint research program No. 11761141012 , the  CAS   Strategic Priority Research Program   XDA15020200 , and the  NSFC  grant  11773034. Wenkai Hu  and  Guilaine Lagache  are supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project  CONCERTO , grant agreement No.
788212 ) and from the  Excellence Initiative of Aix-Marseille University-A*Midex , a  French "Investissements d'Avenir" programme. Zheng Zheng  is supported by  NSFC  grant Nos. 11988101 ,  U1931110  and  12041302. Zheng Zheng  is also supported by  CAS Interdisciplinary Innovation Team  ( JCTD-2019-05 ). The authors thank  Snežana Stanimirović ,  Elizabeth K. Mahony ,  Stephen J. Curran  and  James R. Allison  for helpful discussion. Ue-Li Pen  receives support from  Ontario Research Fund-Research Excellence Program (ORF-RE) ,  Natural Sciences and Engineering Research Council of Canada (NSERC)  [funding reference number  RGPIN-2019-067 ,  CRD 523638-18 ,  555585-20 ],  Canadian Institute for Advanced Research (CIFAR) , the  National Science.
Indeed, this communication model suggests that each station subscribe to a certain number of topics, thus defining its interests and hence the types of information it wish to receive. Stations can then be notified when information belonging to one of their interests is available. Implementation of a MASN over Android devices. We also propose in this paper an implementation of a MASN based on our approach, by prototyping an Android application for the dissemination of information among participants of a scientific conference. We used a software architecture designed for topic-based publish/subscribe communication among mobile devices and the Google's Nearby Connections API  [19]  to handle device-to-device connectivity among these devices. The rest of this paper is organised as follows: section II presents similar works found in the literature, section III briefly presents the information dissemination protocol we used; we present the architecture and the implementation we propose in sections IV and V; the results we obtained are presented and discussed in section VI; section VII concludes this paper.