dataesr/scientific-paragraphs-categorization huggingface dataset:
This makes this result more universal, motivating us to also indicate the change in the full likelihood L i per energy bin (and to make it available in tabulated form  [178] ). To a reasonable approximation, this can be used to constrain the signal normalisation, at 95% C. L. , of an almost arbitrary smooth DM spectrum dN γ /dE γ , where N γ is the number of photons per annihilation process. Concretely, this corresponds to requiring To guide the eye, the black symbols indicate the value of ∆ log L that corresponds to a 2σ upper limit on such a DM signal, from a single bin. Limits on DM models with arbitrary spectra, taking into account all energy bins, can be approximated as in Eq. (  5. 1). The full likelihood table is available for download at zenodo  [178]. i ∆ ln L i [C χ E i dN/dE γ ] < 2.
Extended dark matter cores Let us now address the impact of the assumed DM density profile. We re-iterate from the discussion in Section 3. 1 that the Galactic DM distribution within the inner few kpc (and even more so for r 1 kpc) is rather uncertain and not very well observationally constrained -but at the same time the distribution is crucial for estimating the overall strength of the annihilation signal. The situation in the GC is, in general, different from the situation in dwarf spheroidal galaxies, where kinematic data allow us to constrain the J-factor sufficiently  [180]  to warrant including them in the likelihood analysis, fully marginalising over the profile parameters  [181] [182] [183]. One of the reasons behind this is that for dSphs we typically observe the  : CTA sensitivity to a DM signal, for the W + W -channel, comparing the case of an Einasto profile without core (solid) to that of an Einasto profile with a 1 kpc core (dashed).
Also in this case the full likelihood table is available for download at zenodo  [178]. Right. Mutual impact of instrumental and GDE model systematic uncertainties on the upper limit on the DM annihilation cross-section. As in Fig. 12  (right panel), but now with respect to the construction configuration. signals in general allows one to test DM annihilation in the most model-independent way, in particular for models where the annihilation spectra deviate from those expected for pure annihilation channels like bb or W + W -. In Fig. 16  (left panel) we show this binned likelihood for the construction configuration; in particular, this allows us to use Eq. (  5. 1) to calculate limits in the same straightforward way as before. Just as for the baseline configuration, we make this likelihood available in tabulated form in the supplemental material.
For the Einasto profile, the sensitivity to a DM signal improves by up to 40 % when taking into account the additional information contained in the assumed energy correlations. For a 1 kpc core, on the other hand, the improvement is much more significant and can be larger than a factor of 2. In the final part of this section we illustrate the uncertainty of the projected sensitivity due to the rather poorly constrained spatial profile of DM in the Galactic centre in a complementary way, this time assuming no energy correlations (and thus returning to our benchmark settings, for better comparison with the results of the main text). In Fig. 23  we show a summary of expected limits for exemplary parameter choices with respect to a  Our parameter choices are motivated by Fig. 4  of a recent Bayesian analysis of MW data  [84].
ON and N k OFF refer to the measured photon events in ON and OFF region for observation k which we prepare as a single Asimov data set from a selection of background source components described in Sec. 3. 2. κ ijk is in general a normalisation factor to account for the different ON/OFF analysis, statistical reach (CR + IEM (Gamma)) Figure  26 : Mean expected CTA upper limits on the DM annihilation cross-section for the bbchannel and an Einasto DM density profile, adopting the default morphology analysis (black) and ON/OFF strategy (red), respectively. The black solid line represents the benchmark situation (including instrumental systematic errors) where we additionally apply a mask to the band b ≤ 0. 3 • to reduce the impact of the IEM along the Galactic plane. All other lines refer to 'statistics-only' limits, where the impact of systematic errors is neglected; dashed (dash-dotted) lines assume that no IE (IE as expected in the Gamma model) is included in the mock data.
S. National Science Foundation ,  U. S. Department of Energy ,  Argonne National Laboratory, Barnard College, University of California, University of Chicago, Columbia University, Georgia Institute of Technology, Institute for Nuclear and Particle Astrophysics (INPAC-MRPI program ),  Iowa State University , the  Smithsonian Institution, Washington University McDonnell Center for the Space Sciences, The University of Wisconsin  and the  Wisconsin Alumni Research Foundation, USA. The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) under grant agreements No 262053 and No  317446. This project is receiving funding from the  European Union 's  Horizon 2020 research and innovation programs  under agreement No  676134.
The amplification was performed through 35 cycles of 95 • C (30 s), 55 • C (30 s) and 72 • C (1 min), with a previous activation start of 95 • C (10 min) and final extension step at 72 • C (10 min). ITS amplified fragments were sequenced at the Eurofins platform (France). Sequence data were edited using Chromas Pro version 1. 34. For identification, fungal ITS rRNA sequences were compared with NCBI (National Centre for Biotechnology Information; http://www. ncbi. nlm. nih. gov) database as previously described  (Giloteaux et al. , 2010). Fungal ITS sequences in this study and reference sequences from Gen-Bank were edited and aligned using CLUSTAL-W  (Thompson et al. , 2003)  as described  (Bruneel et al. , 2008). The aligned sequences were imported into MEGA 3. 1  (Kumar, 2004)  for creating Neighbour-joining (NJ) trees based on pairwise genetic distances.
The quality of the branching patterns for NJ was assessed by bootstrap resampling of the data sets with 1000 replications and rooted to Rhizopus oryzae CBS 112. 07 T (NR 103595. 1) and Trametes versicolor CFMR FP-135156-Sp T (NR 154494. 1). The sequences determined in this study have been submitted to the ITS NCBI database and assigned Accession nos. MT889820 to MT889904.
Acknowledgment We acknowledge the  French/German bilateral program PHC-PRO-COPE  for supporting the project  FungDeg (Elucidating biomarkers and functions of fungal bacterial interactions during the degradation of polycyclic aromatic hydrocarbons at changing environmental conditions ; project no. 42512WL ). We would like to thank all partners of the  FungDeg  project and MELODY group for their useful discussions. JAB received a fellowship from  CONACyT  number  326250/471689  and a mobility grant from the centre of excellence E2S at  Pau University.
Mice The generation and preliminary analysis of Topaz1-null transgenic mouse line has been described previously  (Luangpraseuth-Prosper et al. , 2015). Generation of the 4630493O16Rik-null transgenic mouse line was achieved using CrispR-Cas9 genome editing technology. The RNA mix comprised an mRNA encoding for SpCas9-HF1 nuclease and the four sgRNA (Supplementary Table  1 ) targeting the 4930463o16Rik gene (NC_000076: 84324157-84333540). These sgRNAs were chosen according to CRISPOR software 1 in order to remove the four exons and introns of the 4930463o16Rik gene. Cas9-encoding mRNA and the four sgRNAs were injected at a rate of 100 ng/µL each into one cell fertilized C57Bl/6N mouse eggs  (Henao-Mejia et al. , 2016). The surviving injected eggs were transferred into pseudo-pregnant recipient mice.
5HD assay-brown). Slides were counterstained according to a PAS staining protocol and then observed for visible signals. Hybridization was considered to be positive when at least one dot was observed in a cell. Stained sections were scanned using a 3DHISTECH panoramic scanner at the @Bridge platform (INRAE, Jouy-en-Josas) and analyzed with Case Viewer software (3DHISTECH). In situ hybridization with the control probes was performed using tissue sections of 2-month-old WT testes (Supplementary Figure  1 ). We also used the RNAscope R 2. 5HD assay-red kit in combination with immunofluorescence in order to achieve the simultaneous visualization of RNA and protein on the same slide. The ISH protocol was thus stopped by immersion in water before hematoxylin counterstaining. Instead, the slides were washed in PBS at room temperature.
The Mouse on mouse (M. O. M. ) kit (BMK-2202, Vector laboratories) was used and slides were incubated for 1 h in Blocking Reagent, 5 min in Working solution and 2 h with a primary antibody: DDX4 (ab13840, Abcam) or γH2AX(Ser139) (Merck), diluted at 1:200 in Blocking Reagent. Detection was ensured using secondary antibody conjugated to DyLight 488 (green, KPL). Diluted DAPI (1:1000 in PBS) was then applied to the slides for 8 min. The slides were then mounted with Vectashield Hard Set Mounting Medium for fluorescence H-1400 and images were captured at the MIMA2 platform 2  , 3  using an inverted ZEISS AxioObserver Z1 microscope equipped with an ApoTome slider, a Colibri light source and Axiocam MRm camera. Images were analyzed using Axiovision software 4. 8. 2 (Carl Zeiss, Germany). Total RNA Extraction and Quantitative RT-PCR (RT-qPCR) Total RNAs from post-natal mouse testes or other organs were isolated using Trizol reagent.
Transcriptomic Analysis Sequence libraries were aligned with the Ensembl 95 genome using TopHat  (Trapnell et al. , 2009) , and gene table counts were obtained by applying featureCounts to these alignments  (Liao et al. , 2014). Data normalization and single-gene level analyses of differential expression were performed using DESeq2  (Love et al. , 2014). Since some samples were sequenced several months apart, a batch effect after computation of hierarchical clustering was observed. In order to take this effect into account, we introduced the batch number into the DESeq2 model, as well as the study conditions. Differences were considered to be significant for Benjamini-Hochberg adjusted p-values < 0. 05, and absolute fold changes > 2 (absolute Log2FC > 1)  (Benjamini and Hochberg, 1995). Raw RNA-seq data were deposited via the SRA Submission portal 4  , BioProject ID PRJNA698440.
Biotype Determination of DEGs Data available on the NCBI, MGI 5  and Ensembl 6  websites were used simultaneously to determine the DEG biotypes. For this purpose, information on the mouse genome was obtained by ftp from NCBI 7  ; the annotation BioMart file from Ensembl 8  (Ensembl genes 95, Mouse genes GRCm28. p6) and feature types from MGI 9  (with the protein coding gene, non-coding RNA gene, unclassified gene and pseudogenic region). Only data corresponding to the DEGs were conserved. The files from these three databases were therefore cross-referenced to determine DEG biotypes. When the biotype of a gene differed between databases, the annotation was then listed as genes with a "biotype conflict. ".
Gene Ontology Enrichment The mouse DEGS thus identified were analyzed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway membership with Database performed using the DAVID Bioinformatic Database 6. 8 10. These analyses and pathways were considered to be significant for a Benjaminicorrected enrichment p-value of less than 0. 05. The Mouse Atlas Genome of differentially expressed genes extracted from this study was performed via the Enrichr website 11.
The validation of several DEGs was achieved using RT-qPCR. Two randomly selected genes up-regulated at P16 (B3galt2 and Hp) and three at P18 (B3galt2, Afm, and Cx3cr1), four genes down-regulated at P16 and P18 (Gstt2, 4930463O16Rik, 4921513H07Rik, and Gm21269) and two non-differential genes (Cdc25c and Nop10) were analyzed (Supplementary Figure  3 ). The results confirmed those obtained using RNA-seq. The biotypes of the differential transcripts (protein-coding, non-coding RNAs, etc. ) were determined from the annotations of the NCBI, MGI and Ensembl databases. Two major deregulated groups were highlighted at both stages. The protein-coding gene biotype accounted for half of the deregulated genes (51. 2 and 57. 2% at P16 and P18, respectively) (Figures  1C, D ). A quarter of Topaz1 -/-DEGs; 24.
Pathway and Functional Analysis of DEGs To further understand the biological functions and pathways involved, these DEGs were functionally annotated based on GO terms and KEGG pathway or on InterPro databases through the ontological Database for Annotation, Visualization and Integrated Discovery (DAVID v6. 8, see footnote 10) using the default criteria  (Huang et al. , 2009a,b). At P16, so therefore before the first meiosis division, out of 205 differentially expressed genes, 32% of downregulated and 20% of up-regulated genes corresponded to non-coding RNAs with no GO annotation or no pathway affiliation for the vast majority (Figure  1E ), leading to less powerful functional annotation clustering (Supplementary Table  4 ). Five clusters with an enrichment score > 1. 3 were obtained (an enrichment score > 1.
However, the enrichment scores were higher when only downregulated genes were considered. These clusters include the following GO terms: (i) for cellular components: motile cilium, ciliary part, sperm flagellum, axoneme, acrosomal vesicule; (ii) for biological processes: microtubule-based process, spermatogenesis, germ cell development, spermatid differentiation (Supplementary Table  5 ). Finally, using the InterPro database, four clusters with enrichment scores > 1. 3 were obtained based on downregulated genes (Supplementary Table  5 ) and with upregulated genes, an absence of TOPAZ1 from mouse testes highlighted the biological pathway of the response to external stimulus or the defense response in the testes. Once again, and as for P16, this suggested stressful conditions in these Topaz1 -/-testes.
These three lncRNAs were almost exclusively expressed in testes (Figures  2A, C, E ). These results were in agreement with RNA-seq data available for 4930463O16Rik and Gm21269 on the ReproGenomics viewer 12  (Supplementary Figures  4A, 5A , respectively)  (Darde et al. , 2015 (Darde et al. , , 2019)). Our RNAseq results, summarized using our read density data (bigwig) and the Integrative Genomics Viewer (IGV; 13  ), revealed little or no expression of these three genes in Topaz1 -/-testes (Supplementary Figures  6A-C ). Quantification of these transcripts using qPCR from postnatal to adulthood in WT and Topaz1 -/-testes had previously been reported, as for 4930463O16Rik and 4921513H07Rik (Figure  9  in  Luangpraseuth-Prosper et al. , 2015)  or performed for Gm21269 (Supplementary Figure  7 , also including the postnatal expression of 4930463O16Rik and 4921513H07Rik).
Generation of 4930463O16Rik-Deleted Mice In order to evaluate a potential role in spermatogenesis for the lncRNA with the highest Log2FC at P16, namely 4930463O16Rik (the nuclear expressed gene revealed by HIS), a mouse knockout model was established. The 4930463O16Rik gene (Chr10: 84,488,293-84,497,435 -GRCm38:CM001003. 2) is described in public databases as consisting of four exons spanning approximately 10 kb in an intergenic locus on mouse chromosome 10. Using PCR and sequencing, we confirmed this arrangement (data not shown). In order to understand the role of 4930463O16Rik, a new mouse line depleted of this lncRNA was created using CRISPR/Cas9 technology (Figure  4A ). Briefly, multiple single guide RNAs (sgRNAs) were chosen, two sgRNAs in 5 of exon 1 and two sgRNAs in 3 of exon 4, so as to target the entire length of this gene (Figures  4A, C ) and enhance the efficiency of gene deletion in the mouse  (Han et al.
At P18, 258 genes were differentially expressed (199 downregulated and 59 up-regulated using the same statistical parameters; Supplementary Table  6 ). Among them, 206 were protein-coding genes accounting for 79. 8% of DEGs (Figure  7A ). Thus, P18 DEGs highlighted a direct or indirect relationship between the loss of the 4930463O16Rik lncRNA and proteincoding genes. In addition, loss of this lncRNA also resulted in the deregulation of 37 (14. 3%) other lncRNAs. The validation of several DEGs was performed by RT-qPCR using WT and 4930463O16Rik -/-testicular RNAs at both developmental stages (P16 and P18) (Supplementary Figure  10 ). The qPCR results for the genes thus tested were consistent with those of RNA-seq. The 4930463O16Rik -/-DEGs were also analyzed using the DAVID database (Supplementary Table  7 ).
ACKNOWLEDGMENTS Our warmest thanks go to  Jean-Luc Vilotte  for allowing lncRNA deletion to take place in his laboratory, and also for proofreading this manuscript. We would like to thank the  TACGENE  facility at  U1154-UMR7196 ,  MNHN, Paris , and especially  Anne De Cian  and  Jean-Paul Concordet , for the synthesis of gRNAs and Cas9 mRNA. We are grateful to all members of our mouse experimental unit (IERP,  INRA, Jouy en Josas, France). We would like to thank the  @Bridge  platform for use of the  Agilent Bioanalyzer  and for histology facilities ( UMR 1313  GABI, Jouy-en-Josas, France), and particularly  Marthe Vilotte  for HE staining. We would also like to thank the  MIMA2  platform for providing access to the virtual slide scanner (Panoramic SCAN,  3DHISTECH ). Finally, we are grateful to all members of the " Gonad Differentiation and its Disturbances " team for our scientific and technical discussions on Monday mornings, either in person or by videoconference.
Non-mitochondrial respiration was determined by injecting 1. 5 mM antimycin A and 1. 5 mM rotenone (ETC inhibitors). Data were normalized to lipid content by NileRed or cell number by JanusGreen staining. 2. 8. ELISA TGFb1, TGFb2, and TGFb3 concentrations in cell culture supernatants were determined with DuoSET ELISA kits (R&D Systems, #DY007, #DY008, #DY240, #DY302, and #DY243). Samples were heated for 5 min at 80 C to activate TGFb.
ACKNOWLEDGEMENTS The authors would like to thank  Alexandra Killian  for her excellent technical assistance. We also thank the  Core Facility  " Extracellular Flux Analyzer " at  Ulm University  for excellent help and assistance. This work has been supported by the  Boehringer Ingelheim University Medical Center Ulm Biocenter (BIU). DH was supported by the  International Graduate School in Molecular Medicine at Ulm University  ( GSC270 ). DT is supported by a grant from the  German Research Foundation  (DFG,  TE912/2-2 ). PFP is supported by the  German Research Foundation  (DFG,  Fi1700/5e1 ;  Heisenberg Professorship ,  Fi1700/7e1 ).
AES-128 belongs in this group also, because there are ASIC implementations of it with an area of just 2400 GE  [426]  on 0. 18 µm technology, but it cannot be applied in every scenario. In Table  2. 1, we give a summary of the known lightweight block ciphers, sorted in alphabetical order, with their type, key and block size in bits, number of rounds, used technology and number of GEs if known, and we give the best known attacks in Table  2. 2. KASUMI used in UMTS, GSM, and GPRS mobile communications systems, 3-Way and MANTIS are considered insecure. Additionally, CLEFIA and PRESENT are part of the ISO-29192-2 standard, while HIGHT, MISTY1 and AES are part of the ISO/IEC 18033-3:2010 standard. For fair and consistent evaluation and comparison of software implementations of lightweight block and stream ciphers, one can use a free and open-source benchmarking framework FELICS (Fair Evaluation of Lightweight Cryptographic Systems)  [182].
Message Authentication Codes A message authentication code (MAC) protects the integrity and authenticity of a given message, by generating a tag from the message and a secret key. MAC   schemes can be constructed from block ciphers (e. g. , CBC-MAC (part of the ISO/IEC 9797-1:1999 standard) or OCB-MAC  [504] ), from cryptographic hash functions (e. g. , HMAC (RFC 2104)), etc. Three lightweight security architectures have been proposed for wireless sensor networks: TinySec  [316] , MiniSec  [382]  and SenSec  [370]. TinySec and MiniSec recommend CBC-MAC and the patented OCB-MAC, while SenSec recommends XCBC-MAC, for which there is an existential forgery attack  [238] , and all suggest the use of 32-bit tags. 32-bit security is not enough-the recommended size is at least 64 bits. Design choices for lightweight MACs include shorter tag sizes, simpler key schedules, small hardware and/or software implementations, better performance on very short messages, no use of nonces, and generation from lightweight block ciphers and hash functions.
Speck Similar to Simon the Speck family includes ten variants, differing in their block-and key-sizes. A member is denoted Speck2n/mn where 2n is the block size and mn is the key size. Speck builds on the ARX design paradigm and the cipher is composed of three operations: modular Addition, Rotation, and XOR (hence the name ARX). While efficient in software, ARX operations are known to have slow diffusion. Usually, this slow diffusion mandates employing a large number of rounds (see e. g. ,  [214] ) to be secure. However, as discussed in the sequel, the designers argued that they have a good understanding of the cipher's diffusion and settled for a relatively small number of rounds. To reduce implementation size, the designers reuse Speck's round function for the key schedule, feeding in the round number as the round key and outputting one subkey word in every round.
Remote vs. Local Use The motivation for designing eID systems comes from situations such as automated border control and enabling eGovernment applications. The first option is presenta-tion of an eID to a reader and processing all information locally. The second option is using an eID as a secure token that enables us to create authenticated connections with a remote terminal. An alternative is to use no eID and realize its functions using an ID infrastructure. Each option has its advantages and disadvantages: Option 0: Virtual ID An ID document contains data that may be fetched from an appropriate registry. So one can use a "virtual ID document" containing only a key to a database (e. g. , an ID number printed on a sheet of paper). There are certain advantages of this approach: a negligible technical effort for issuing and delivery of an ID document, and ease of ID document updating and revocation.
• Identity and data verification does not require online access despite strong guarantees originating from the eID issuer. • Interaction with a chip may enable biometric verification without the involvement of any external database. The biometric data obtained by the reader can be directly compared with data stored in the eID. Consequently, any security breach in the system would not expose the biometric data of the whole population. Option 2: Remote Use In this case an eID serves as a secure cryptographic token for remote authentication. The advantages of this approach are as follows: • An eID is involved in the authentication process as a "what you have" and a "what you know" component, since typically an eID requires us to provide the user's activation password. • In remote authentication it is hard to check that the user is really on the other side of the line.
This should include in particular the possibility to: • temporarily block their own eID, • get a record of their former activities. In existing systems, there are limited possibilities to suspend or invalidate an eID based on inserting entries into a central database. This is not enough to prevent lunchtime attacks, where an eID is seized by an adversary for a short time. To keep track of eID activity one could deploy for instance a transaction counter or some more sophisticated solution (see, e. g. ,  [349].