dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The 16S rRNA gene sequencing data generated in this study have been deposited in the GenBank database under the BioProject PRJNA810306. The 16S rRNA gene sequence data used in this study are available in the SILVA Living Tree Project LTP (ltp_12_2020)  102  https://imedea. uib-csic. es/mmg/ltp/ltp-2020/; Silva SSU 138 database  105  https://www. arb-silva. de/documentation/release-138/; RDP database (11. 5)  105  http://rdp. cme. msu. edu/index. jsp. Metadata on animal species used in this study are available in EltonTraits database  114  ; Animal Diversity Web https:// animaldiversity. org/.
ASV21 Dominant Animal Gut Thaumarchaeota 3 DSC1 Dominant Soil Archaea 1 112n archaeal lineages in the gut and proposed independent events of adaptation to the gut in the domain Archaea. aDistribution of archaeal 16S rRNA gene sequences in the gut and other environments based on sequences obtained from the Silva database and this study. The archaeal tree is based on Borrel et al. 112enriched with DPANN lineages. Large arrows on the tree indicate five main events of adaptation to the gut environment, small arrows indicate four other possible events of adaptation to the gut. Fully orange triangles indicate that gut adaptation likely occurred at the base of the lineage while blue triangles with an orange spot indicate that gut adaptation occurred within the lineage. a 1 b 0. 8 0. 6 0. 4 0. 2 0.
0 10 12 c r 2 =0. 28 10 10 r 2 =0. 81 10 8 10 6 r 2 =0. 03 10 4 10 5 10 6 10 7 10 8 10 9 10 10 0. 01 0. 91 d 1 Ca Nitrosocosmicus hydrocola G61 (wastewater treatment plant) DSC2 Dominant soil archaea 2 Ca Nitrocosmicus oleophilus MY3 Ca Nitrosocosmicus franklandus NFRAN1 ASV4 Dominant Animal Gut Thaumarchaeota 1 U62814. 1 soil AY887078. 1 human-gut Nitrosocosmicus Nitrososphaeraceae Nitrososphaerales/ 1 HQ269159. 1 soil 0. 83 0. 98 1 Nitrosopumilales 1 0. 99 Nitrosotaleales Fig. 2 The histogram shows the proportion of sequences (from the Silva database) of a given lineage present in either animal digestive tract ("Gut", orange), open natural environment ("Environment", blue) or built environment ("Engineered", grey). Orange circle surface area represents the percentage of reads attributed to each taxon in our study (gut-related samples only).
We present a NNLO QCD accurate event generator for direct photon pair production at hadron colliders, based on the MiNNLO P S formalism, within the Powheg Box Res framework. Despite the presence of the photons requires the use of isolation criteria, our generator is built such that no technical cuts are needed at any stage of the event generation. Therefore, our predictions can be used to simulate kinematic distributions with arbitrary fiducial cuts. Furthermore, we describe a few modifications of the MiNNLO P S formalism in order to allow for a setting of the renormalization and factorization scales more similar to that of a fixed-order computation, thus reducing the numerical impact of higher-order terms beyond the nominal accuracy. Finally, we show several phenomenological distributions of physical interest obtained by showering the generated events with Pythia8, and we compare them with the 13 TeV data from the ATLAS Collaboration.
Theoretical predictions for diphoton direct production at next-to-leading-order (NLO) QCD accuracy, that consistently include also the fragmentation component, were obtained for the first time in ref. [10] , and made publicly available in the DIPHOX software. Next-tonext-to-leading-order (NNLO) QCD corrections to pp → γγ were obtained in refs. [11] [12] [13] [14] , whereas NLO electroweak corrections have been studied in ref. [15]. More recently, even the three-loop q q → γγ amplitudes have been computed  [16]. In order to include all the effects up to α 2 S accuracy, the gluon-induced production of a photon pair through a closed quark loop has to be taken into account as well: such a contribution, that is particularly sizable due to the gluon luminosity, was first computed in ref. [17] , and it is by now known at the next order, both for massless  [18]  and for massive quark loops  [19, 20].
We apply the photon isolation prescription of ref. [9]  to the two final-state photons. For each photon, we compute the angular distance R iγ with respect to the i-th final-state parton. We discard the event unless, for every photon and every R < R c , n i i=1 p i T θ(R -R iγ ) < E max T χ(R), (3. 1) where n i is the number of final-state partons, p i T is the transverse momentum of i with respect to the beam, and χ(R) = 1 -cos R 1 -cos R c n. ( 3. 2) In our analysis, we have set E max T = 4 GeV, R c = 0. 4 , n = 1. (3. 3) In addition, the two photons have to fulfill p Tγ 1 > p min Tγ 1 , p Tγ 2 > p min Tγ 2 , m γγ > m min γγ , (3. 4) where p Tγ 1 and p Tγ 2 are the transverse momenta of the hardest and next-to-hardest photons, m γγ is the diphoton mass, and In addition, the Powheg Box Res code has been run with the flag doublefsr set to 1.
Acknowledgments We would like to thank  S. Alioli ,  A. Broggio  and  J. Lindert  for useful discussions. We also thank  J. P. Guillet ,  P. F. Monni , and  M. Wiesemann  for carefully reading the manuscript and for their useful comments. E. R. thanks  P. F. Monni  and  M. Wiesemann  for several discussions about theoretical aspects related to the MiNNLO PS method. In particular, the relevant aspects of the formulation of the MiNNLO PS method with the scale choice discussed in section 2. 4, and their initial implementation in the code, were developed in collaboration with them. The work of A. G. was supported by the  ERC Starting  Grant  REINVENT-714788.
Tumour location and cartographies A program called Gridmaster was developed in the INTERPHONE study to allow neuroradiologists to define the location of a tumour within the computational head phantom used for the dosimetry on the basis of diagnostic scans  (Cardis et al. , 2011b). The program displayed coronal, sagittal and transverse outlines of the phantom. Since tumour localisation is a complex process, relying heavily on the experience of the neuroradiologist, it is done at centimetre resolution. Thus, the highresolution dosimetry calculations were projected over a superimposed grid of 1 cm 3 resolution. Large advances in phantom models since the development of the Gridmaster allowed the creation of an improved version of the program for this study (XGridmaster), with added anatomical information as a function of age (Fig.
A similar analysis was also performed to compare the level of agreement between SAR cartographies (across phone categories and across ages). That is, if for a given cell, dose in one cartography fell in the same decile of another cartography. This effectively compares the level of agreement of the SAR distribution normalised to its maximum. Pearson correlation was also calculated for the comparison between cartographies, as well as for the comparison between RF and ELF dose as these were quantities on a continuous scale. These statistical analyses were conducted in R programme (version 3. 6. 3) and Matlab  (Cardillo, 2007).
Assuming CSE is the gold standard for RF dose and that data grouping is done in deciles, using only duration of calls and number of calls to estimate dose would lead to an exposure misclassification of over 2 deciles for 20% and 28% of subjects respectively, despite moderate overall agreement. As expected, the level of agreement was heavily dependent on communication systemonly for 2G systems was there moderate to substantial agreement between duration of use of mobile phones and CSE, and for 2G and DECT for CICD. For example, in this study, duration (or number) of calls was a very poor proxy for exposure to RF (and ELF) for subjects who exclusively used CDMA (e. g. one operator in Canada) and lead to an overestimation of dose. These results illustrate the importance of estimating localised dose in the brain rather than using only mobile phone use history in studies of cancer risk from mobile communication devices use.
Uncertainties in SAR and ICD distribution, and in operator data result in non-differential exposure misclassification which adds noise to dose estimates and could result in underestimation of the association, if there is one, but is unlikely to create a spurious association in the absence of one  (Röösli, 2014). With the development of new, more efficient and complex communication systems, EMF dose estimation is likely to be more challenging and subject to larger uncertainties. For example, 5G NR (New Radio) RF exposure is more complex than that of 3G, linked to the enhanced flexibility in how transmission is done. Typical power levels in 5G FR1 (the frequency range currently deployed, 410-7 125 MHz) have been found to be similar to those of 3G systems  (Joshi et al. , 2020) , but the deployment of FR2 (26.
CDMA2000 CDMA2000® represents a family of  IMT-2000 (3G)  standards providing high-quality voice and broadband data services 3. Currently, CDMA2000 includes CDMA2000 1X and CDMA2000 EV-DO standards. CDMA2000 1X, deployed in 2000, is used for circuit-switched voice calls, while CDMA2000 EV-DO is used for data. Maximum transmitted power is 0. 2 W and uplink transmissions are in the 800 MHz and 1900 MHz bands.
Funding Funding for the coordination of the  MOBI-Kids study  was obtained from the  European Commission 's  Seventh Framework Programme  under grant agreements number  226873  and  603794 , and from the  Spanish Ministry of Science and Innovation (MINECO). In Spain , additional funding was obtained from the  Spanish Health Research Fund (FIS)  of the  National Institute for Health Carlos III , and from the  Junta de Andalucía ,  Consejería de Salud. Proyecto PI-0317-2010. ISGlobal also acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through the " Centro de Excelencia Severo Ochoa 2019-2023" Program  ( CEX2018-000806-S ), support from the  Generalitat de Catalunya  through the  CERCA Program  and support from the  Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat of Catalonia through AGAUR (the Catalan Agency for Management of University and Research Grants)  (Project  2017 SGR 1487 ).
Australian participation in MOBI-Kids was supported by the  Australian National Health and Medical Research Council  with a five-year research grant (grant number:  546130 ). Austrian participation in MOBI-Kids was partly supported by a grant from the  Ministry of Science. In Canada , participation in MOBI-Kids was supported by a  university-industry partnership grant  from the  Canadian Institutes of Health Research (CIHR) , reference number  110835 , with the  Canadian Wireless Telecommunications Association (CWTA)  serving as the industrial partner. CWTA provides technical information on wireless telecommunications in Canada and facilitates access to billing records from  Canadian  network operators, but has no involvement in the design, conduct, analysis, or interpretation of the  MOBI-KIDS study.
French participation was also supported by the  French National Agency for Sanitary Safety of Food, Environment and Labour (ANSES , contract  FSRF2008-3  and  2013/2/007 ),  French National Cancer Institute (INCa) ,  Pfizer Foundation  and League against cancer. The German study centre received additional funding from the  Federal Office for Radiation Protection (BfS)  under grant number  3609S30010. In Greece , the study was partially supported by the  Hellenic Society for Social Pediatrics and Health Promotion, ELKE (Special Account for Research Grants of the National and Kapodistrian University of Athens)  and  GGET (General Secretariat for Research and Technology). Mobi-Kids India  was supported by  Board of Research in Nuclear Sciences (BRNS , sanction no:  2013/38/01-BRNS ). Italian participation was partially supported by a  Ministry of Health  grant ( RF-2009-1546284 ).
MOBI-Kids Korea was supported by the  ICT R&D program  ( 2017-0-00961  and  2019-0-00102 ) of  MSIT/IITP ,  Korea. Mobi-Kids Japan  was supported by Research on biological electromagnetic environment (Grant Number:  JPMI10001 ) of  Ministry of Internal Affairs and Communications Japan. New Zealand participation was supported by the  Health Research Council  ( HRC 12/380 ) and  Cure Kids  (grant number  3536 ). The Netherland's participation in MOBI-KIDS was partly supported by The  Netherlands Organisation for Health Research and Development (ZonMw)  within the  program Electromagnetic Fields and Health Research  under grant number  85800001 , and by the ODAS foundation, a private foundation supporting activities in the field of pediatric oncology and visual disabilities. The funding sources had no role in the study design; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit the article for publication.
(x + y, Add 𝑢 1 𝑢 2 ) ← - D 2 {× R } = 𝜆((x, 𝑢 1 ), (y, 𝑢 2 )). (x × y, Add (Scale y 𝑢 1 ) (Scale x 𝑢 2 )) ← - D 2 {R} = R × Delta where we can think of Delta as specifying the difference, or łdeltaž, between two values of type S. We might imagine choosing Delta to be a function of type S → S, that transforms one S value into another nearby one. However, we only need a limited vocabulary of such functions, so it is much nicer to represent deltas by a simple algebraic data type 5  , given in Figure  7. It turns out that four constructors suffice to describe all the deltas we need. This formulation allows, for example, inspection of a Delta and affords the chance for optimisation. This particular definition works only for S = R a (the OneHot constructor does not work for more elaborate choices of S), but we will generalise our final version in Section 9.
A Monadic Translation. The trick is, of course, to build a Delta structure with explicit rather than implicit sharing, and that is achieved by the translation 6 in Figure  9  (defining ← -D, our final answer, with no version number), and its supporting functions in Figure  11. The translation itself is totally unsurprising: it is Moggi's call-by-value monadic translation  [Moggi 1991 ]. First, the Delta data structure is extended with two new constructors: Var DeltaId and Let DeltaId Delta Delta which, as their names suggest, describe explicit sharing. The type DeltaId is the name, or index, of a Delta. In fact, as Figure  11  shows, a DeltaId is just a natural number N. 6 We adopt several conveniences inspired by Haskell: • do-notation allows sequencing of monadic operations by using statements.
Using an Array. The monad M is a state monad, and can therefore be implemented using mutable state: we simply need a mutable structure to which we can add new DeltaBindings as execution proceeds. For example, a dynamically-growable mutable array would work well. The eval function takes and returns a DeltaMap, and uses it entirely linearly, as we have suggested by the linear arrow in its type. So we can readily use a mutable array, indexed by DeltaId (a natural number), rather than a Map data structure, thus type DeltaMap = Array R --Mutable Then addDelta is implemented by an in-place add to the specified slot in the array, while lookup indexes it. The initial array, initialised to zeros, is constructed by emptyMap, and its size can be obtained from runDelta as the final DeltaId allocated. (We omit the code for this little point.
□ We can put these two lemmas together to prove the efficiency of the overall algorithm: Theorem 5 (Translation Efficiency). Suppose we have a program e, and its reverse-mode translation R{e}. Then: (1) The runtime of R{e} is bounded by a constant-factor relative to the runtime of e. (2) The memory usage of R{e} is bounded by the memory usage of e, plus the runtime of e. Proof. The R{e} function calls ← -D{e}, and then builds a Delta named u from the DeltaBinds that ← -D{e} returns using the runDelta function, and then calls eval on that. (1) We know that the size of the DeltaBinds returned from ← -D{e} is linear in the runtime of e, and since runDelta is just a fold that also returns a u linear in the size of the DeltaBinds. Calling eval on that u is also linear in the runtime of e, which means the overall runtime is linear in the runtime of e.
IMPLEMENTATION One well-known advantage of the dual-number approach, which we use for both forward and reverse mode, is that in a language like OCaml or Haskell (and others) we do not need to do any source-to-source transformation at all. In Haskell, all arithmetic is overloaded, so rather than the top-level function having type e : R a → R, it has type e : ∀. Real  ⇒   →  We can instantiate t with type Float to do the primal computation, or with type Dual to do the dual-number computation, where data Dual = Dual Float Delta The instance for Real Dual and Num Dual give the implementations for literals and the floatingpoint operations. This approach is well described by  Karczmarczuk [1998]  x; y = q. y; z = q. z } let dot (p : vec3) (q : vec3) : real = p. x × q. x + p. y × q. y + p. z × q.
Selective Fusion of Delta Traces Suppose our program has a (let-bound) function definition like this: f : R → R f = 𝜆x. x × R sin x Our translation will yield a definition of f :(R × Delta) → M (R × Delta), and every call to  will build a bit of Delta data structure; and the bigger f is the more structure would be built. And yet, if we were to think of f as a primitive function, we could use the approach of Section 8. 2 to define a function that built a constant amount of Delta structure. The transformation is given in Figure  15 , in the special case of expressions of type R a → R. We start from the existing transformation ← -D{e}, and produce a modified transformation ← -D ′ (e) with the same type signature, so we can simply use ← -D{e} instead of ← -D ′ (e) wherever we please. The new transformation runs the wrapper code of Figure  10 , and then wraps that in the impedance-matching code of Figure  14 , using the partial derivatives returned by runDelta.
Protein sequence retrieval and primary analysis The sequences of PhtD (Accession no. AAK99711), PsaA (Accession no. AAF70663), and PspC from group 3 (PspC3, Accession no. EF424119) were fetched from the NCBI protein database (www. ncbi. nlm. nih. gov/protein). TMHMM Server v. 2. 0 was employed to identify the transmembrane helices of proteins (www. cbs. dtu. dk/services/TMHMM-2. 0)  (45). The SignalP 5. 0 server was applied to detect the signal peptide region (http://www. cbs. dtu. dk/services/SignalP/) (46).
Tertiary structure prediction of the selected proteins Depending on the (un) availability of the structural template in the protein data bank (PDB), the template (free)-based modeling methods are used for the prediction of protein tertiary structure. In this study, the tertiary structures of PhtD and PspC were modeled by I-TASSER, a template-based method of modeling (https://zhanglab. ccmb. med. umich. edu/I-TASSER/)  (47). The I-TASSER suite, which automatically models 3D-structure of a given amino acid sequence, provides the model's ranking scores such as con dence score (C-score), template modeling (TM) score, and Root Mean Square Deviation (RMSD). The score of con dence ranges from -5 to +2, with +2 representing the most appropriate model. Template modeling score is in the range of 0-1, with closer to 1 representing the closeness of structure to global topology (48).
The servers were applied at default settings. Likewise, conformational B-cell epitopes were predicted based on the tertiary structures of the considered antigens using the servers Ellipro and DiscoTope (http://tools. iedb. org/discotope/)  (57). The modeled structures using the I-TASSER server and the crystal structure of PsaA (PDB id: 1PSZ) retrieved from RCSB (https://www. rcsb. org/) were used as inputs of these predictor servers. 2. 1. 5. Prediction of MHC-II epitopes T cell epitope prediction tool of the IEDB web server (http://tools. immuneepitope. org/mhcii) (58) and NetMHCIIpan 4. 0 (http://www. cbs. dtu. dk/services/NetMHCIIpan/) (59) were used to discover appropriate MHC-II binding epitopes. In IEDB, each projected epitope has a percentile rank that indicates how well the epitope binds to MHC-II.
The lower percentile rank is the better rank. We chose the eight HLA-DR1 alleles (DRB1*01:01, DRB1*03:01, DRB1*04:01, DRB1*07:01, DRB1*08:01, DRB1*11:01, DRB1*13:01, and DRB1*15:01) as the most common human alleles for MHC-II (60), and also the 3 mouse alleles (H2-IAb, H2-IAd, and H2-IEd). 2. 2. Determination of the potential regions involved in PepO-TLR interaction by molecular docking 2. 2. 1. Sequence retrieval and structure modeling of PepO The sequence of PepO (Accession no. ABJ55398) was fetched from the NCBI protein database. Since the tertiary structure of Pnc endopeptidase O was not available in PDB, the 3D structure of its M13_N domain was modeled and re ned through the I-TASSER and GalaxyRe ne servers, respectively.
2. 1) and the crystal structures of human TLR2 (PDB code: 6NIG) and TLR4 (PDB code: 3FXI) were retrieved from the RCSB site. The best docked complexes of the N-PepO-receptor with the lowest energy were chosen. To display and investigate the considered complexes, the Discovery Studio (DS) Visualizer and DIMPLOT tool from LigPlot+v2. 2. 4 (62) were used.
Reverse translation, codon adaptation, and in silico cloning JCat tool was employed for reverse translation and codon adaptation of the devised vaccine candidate in order to maximal expression of it in E. coli K-12. The vaccine sequence after the reverse translation was 1116 nucleotides long. According to the codon optimization ndings, the CAI value and CG content were 0. 99 and 46. 41%, respectively, which are accepted since they are within the permitted limit. These outcomes con rm a proper expression of the engineered vaccine in E. coli K-12. Ultimately, the ODAC vaccine sequence was successfully cloned into the pET-28a(+) between NdeI and XhoI enzymes by SnapGene software free-trial (Figure  11 ).
An e cient epitope vaccine should be engineered to include B and T cell epitopes capable of providing effective reactions to a speci c infection (103). The B-and helper T-cell epitopes were predicted through various databases and servers and three epitope-rich regions containing high-ranked and shared epitopes, i. e. residues 196-256, 100-187 and 276-363 from PhtD-C, PsaA and PspC, respectively, were chosen (Table  2 ). According to IEBD database, the selected region of PspC was consistent with two signi cant epitopes EDRRNYPSNT and EAKEPRNEEKVKQAK experimentally reported as B cell epitopes by Vadesilho et al. The outcomes of recent experimental study could support our predictions. The physical-chemical features and 3-D structure of the epitopebased vaccines correlate with the nature of the epitopes, linkers, and adjuvants, as well as their place and order in the vaccine construct (104).
Attacks Against Round-Reduced 𝑥 𝐿 𝑥 𝑅 𝑐 𝑖 ⊞ ⊞ 𝑥 𝛼 Figure 1: Round 𝑖 of Feistel-MiMC. Attack description. In order to build a polynomial system representing the CICO problem, we consider an input state ( 0 ,  0 ) = (X, 0). Then we evaluate the round function iteratively, as polynomials in F  [X]: 𝑃 0 = X 𝑄 0 = 0 𝑃 𝑖 = 𝑄 𝑖-1 + (𝑃 𝑖-1 + 𝑐 𝑖 ) 3 𝑄 𝑖 = 𝑃 𝑖-1. The CICO problem becomes   = 0: we just have to find the roots of   =  -1. In practice, we use SageMath to generate the polynomial, and we compute the roots either directly from SageMath, or with an external program using NTL. The corresponding code is given in the Supplementary Material. Complexity Analysis. Since the round function has degree 3, we obtain a univariate polynomial  -1 of degree  = 3 -1 after  -1 rounds. We can estimate the complexity of finding the roots as: 𝑑 log(𝑑) (︀ log(𝑑) + log(𝑝) )︀ log(log(𝑑)) ≈ 3 𝑟-1 × (𝑟 -1) × 1.