dataesr/scientific-paragraphs-categorization huggingface dataset:
RKYG binding to ssDNA were generated by first aligning the X-ray crystal structure of rhA3G-NTD (PDB ID 5K82  [82] ) onto the X-ray crystal structure of hA3F-CTD (PDB ID 5W2M  [83] ), the latter of which was co-crystallized with ssDNA. Subsequently, the hA3C X-ray crystal structure (PDB ID 3VOW  [68] ) was aligned onto the NTD of rhA3G, which is structurally similar to hA3C. The ssDNA and the interface region of hA3C were subsequently relaxed in the presence of each other using Maestro  [110]. The same program was used to mutate hA3C to obtain the hA3C. RKYG and hA3C. S61P. S188I variants, which were again relaxed in the presence of the ssDNA. Similarly, we obtained hA3C, hA3C. RKYG, and hA3C. S61P. S188I ssDNA binding models based on the ssDNA-binding X-ray crystal structure of hA3A (PDB ID 5SWW  [111] ), a much relevant model similar to 6BUX  [112].
These three DNA complex structures were later used for MD simulations as they include a cytidine residue in the active center. Molecular dynamics simulations. hA3C, hA3C. RKYG, and hA3C. S61P. S188I were subjected to MD simulations. For this, the above-mentioned structures without the DNA were N-and Cterminally capped with ACE and NME, respectively. The three variants were protonated with PROPKA  [113]  according to pH 7. 4, neutralized by adding counter ions, and solvated in an octahedral box of TIP3P water  [114]  with a minimal water shell of 12 Å around the solute. The Amber package of molecular simulation software  [115]  and the ff14SB force field  [116]  was used to perform the MD simulations. For the Zn 2+ -ions the Li-Merz parameters for two-fold positively charged metal ions  [117]  were used.
Phylogenetic inference. In order to study the evolution of the A3-Z2 domains a representative set of 61 primate A3C, A3D, and A3F gene sequences were collected from GenBank (https://www. ncbi. nlm. nih. gov/genbank), as follows: 26 A3C sequences, 12 A3D sequences, and 21 A3F sequences (full list available in Suppl. Table  2 ). The phylogenetic relationships and divergence times among the species used were retrieved from http://www. timetree. org (Suppl. Fig. S8 ). A3 sequences from the northern tree shrew Tupaia belangeri were included as an outgroup to the primate ones. As A3D and A3F sequences contain each two Z2 domains, they were split into the corresponding N-and C-termini. The alignments were performed at the amino acid level using MAFFTv7. 380 (http://mafft. cbrc. jp/alignment/software/) [124].
Statistical analysis. Data were represented as the mean with SD in all bar diagrams. Statistically significant differences between two groups were analyzed using the unpaired Student's t-test with GraphPad Prism version 5 (GraphPad Software, San Diego, CA, USA). A minimum p-value of 0. 05 was considered as statistically significant. Asterisks indicate statistically significant differences relative to the effect of the empty vector on infectivity: ***, p < 0. 0001; ns, not significant. FIGURE CAPTIONS.
ACKNOWLEDGMENTS We thank  Wioletta Hörschken  for excellent technical assistance,  Boris Görg  for microscopy support, and  Wolfgang A. Schulz  for kindly proofreading the manuscript. We thank  Alejandro Moisés Barbero Amezaga  ( University Francisco de Vitoria / Faculty of Experimental Sciences, Madrid, Spain ) for his excellent technical support, and the  University Francisco de Vitoria /Faculty of Experimental Sciences  for financial support of A. M. B. A. We thank Michael.
FUNDING This work was supported by a grant from the research commission of the medical faculty of the  Heinrich-Heine-University Düsseldorf  (grant # 2019-13  to CM and HG). KB is supported by the  German Academic Exchange Service (DAAD). ZZ was supported by  China Scholarship Council (CSC). CK and GGS are supported by the  German Ministry of Health  (grant #  G115F020001 ). CM is supported by the Heinz-Ansmann foundation for AIDS research. The Center for Structural Studies is funded by the  Deutsche Forschungsgemeinschaft (DFG  Grant number  417919780  and  INST 208/761-1 FUGG ).
In addition, to evaluate the specificity of the proposed new electrocardiographic criteria in patients not suspected of having an AMI, we included a control group of consecutive patients with complete LBBB who attended the emergency department of Bellvitge University Hospital for any reason other than acute coronary syndrome or who were referred to this center for electrophysiological study or cardiac pacemaker implantation. The clinical data were retrieved from prospective databases available at each participating hospital. These databases register all patients with STEMI treated within the Codi IAM protocol since the beginning of this.
Areas under the ROC curve were compared using the algorithm proposed by De Long et al. 11  The added value of the new criteria was calculated by the Integrated Discrimination Improvement index and the Net Reclassification Improvement index. 12 The agreement between the 2 cardiologists who interpreted the ECGs was evaluated with the Cohen's kappa coefficient. Differences were considered statistically significant at the 2-sided P<0. 05 level. The statistical analysis was performed with STATA Release 12 software (StataCorp LP, College Station, TX).
Software Implementation in SiReNe SiReNe ("Simulateur de Réseaux de Neurones, " "Neural Network Simulator" in English) software, is a C program that has been developed in our laboratory for several years. Originally, it is a pure time-stepping simulator, but it has been extended with the event-driven updating method presented in this paper. So, it is now able to simulate large neural networks either with a classical time-stepping method, or with the event-driven one. In both approaches, the non-linear differential equations of the HH model are updated with the help of the RK2 integration method. After updating all state variables, it is checked whether the neurons have fired. The potential of the neuron has to cross a threshold during this time-step and the derivatives of the potential at times t and t + t must be respectively positive and negative.
In order to detect the spikes, the time delay of each neuron has to be larger than the time-step, otherwise some spikes may be missed. In the pure time-stepping version, the presynaptic currents are systematically retrieved for every neuron, whereas in the event-driven version, neurons are updated first, then only the post-synaptic connections of the spiking neurons are generated and their currents are updated. Figure  4  shows the general algorithmic scheme of the SiReNe software. In addition, two variants were implemented, with or without storage of the neurons connectivity. The most efficient combination of time-stepping approach and connectivity management, according to execution time as well as memory consumption, is the event-driven (spike-driven) approach with connectivity generation.
Finally, parallel multi-threading has been added via the OpenMP API. The parallel strategy mainly consists in distributing the computations related to the neurons over the threads. In particular, parallelism is exploited in the computation of the derivatives, the spike detections and the synaptic current updates. For the parallel generation of neuron connections, it is mandatory to use a thread-safe random generator. Thus, we have added the required software layer over the random generator described in section 3. 2. 1.
Experimental Context The simulations presented below have been done on a Dell R720 server under Linux Debian 4. 9 amd64 with 2 Intel(R) Xeon(R) CPU E5-2640 v2 @ 2. 00 GHz with 8 cores each, and 128GB RAM. Times are measured with the OpenMP function omp_get_wtime() and the program is compiled with gcc 6. 3. 0 and the -O3 optimization level. All simulations presented in this section are done in sequential (one thread) save for the ones related to the performance evaluation of the parallel version (multiple threads).
In order to stretch the limits induced by this problem, a specific process is under consideration for inclusion in the SiReNe software. Although a complete study is necessary to fully evaluate the gain and interest of such corrective process, preliminary experiments show promising results. A future work will be dedicated to this subject. In this paper, the main features of the event-driven updating and connectivity generation have been studied. The combination of this approach with time-stepping numerical integration of the equations forms a very pertinent solution to efficiently and accurately simulate large neural networks with limited computing resources (single server). However, there is still room for improvements and extensions. As a few examples, the reduction of irregular memory accesses in the multi-threaded version, extensions of the parallel version to support multiple machines and/or GPUs, the inclusion of distance-dependent connectivity, synaptic delays as well as the corrective process of the numerical method when using large time-steps would deserve further studies.
Challenges in lncRNA structural studies Challenges in lncRNA structural studies derive from the unavailability of robust computational pipelines for analyzing lncRNA sequences, from the technical limitations posed by the size of lncRNAs on biochemical and biophysical studies, and from the biological complexity of lncRNA and lncRNA cellular pathways, which are typically difficult to probe at the functional level with highthroughput phenotypic assays (Figure  2 ). Computational analysis relies on available databases that catalog lncRNA sequences and structures. However, sequence databases are not yet comprehensive, because the number of transcriptomics data is still limited. For instance, despite the number of lncRNA genes being significantly higher than that of proteincoding genes (see above), as of September 8th 2020 Rfam counts only 3,024 entries, mostly derived from tRNAs, rRNAs, snoRNAs, or snRNAs  (Kalvari et al.
Especially in the case of multiexonic lncRNAs, this limitation implies that putative homologs need to be identified manually via targeted genomic alignment [i. e. in BLAT  (Kent 2002) ], rather than by high-throughput sequence alignments typically used for proteins [i. e. in BLAST  (Altschul et al. 1990) ]  (Chakraborty et al. 2014; Uroda et al. 2019). There is also no unique database for depositing experimentally mapped secondary structures, which have anyway only been determined for a few targets so far. The low number of annotated lncRNA secondary structures makes ab initio, sequence-based secondary structure calculations challenging, because prediction software does not have access to a sufficient number of sequences for performing comparative analysis, for scoring thermodynamic folding parameters, or for training machine learning algorithms  (Zhao et al.
2015). These limitations are more pronounced when the software is embedded in structural prediction pipelines or when operated via online graphical user interfaces (GUI). For instance, mFold is limited to 1400 nt long sequences in GCG  (Devereux et al. 1984) , while RNAStructure modules are limited to 200-4000-nt-long sequences when operated via the online GUI (https:// rna. urmc. rochester. edu/RNAstructureWeb/Information/ Limitations. html). In these cases, a windowed modeling approach is used by some tools, in which the structure calculations are broken into stages to increase computational efficiency  (Siegfried et al. 2014). If lncRNA secondary structure prediction is challenging in the absence of experimental data, tertiary structure prediction is currently impossible. There is still no experimental high-resolution 3D structure of any full-length lncRNA, and the number of lncRNA motifs of known 3D structure is still extremely limited.
In general, only <0. 01% of the >14 million non-coding RNAs collected in RNAcentral possess experimentally determined 3D structures, and these represent only $3% of all entries annotated in the Protein Data Bank (PDB). Mostly, these structures correspond to short RNAs or RNA fragments of <200 nt in length. Thus, despite ongoing efforts, current de novo RNA 3D structure prediction are accurate only for short segments  (Hajdin et al. 2010; Humphris-Narayanan and Pyle 2012; Magnus et al. 2019; Yesselman et al. 2019; Watkins et al. 2020). Production, purification, and storage of lncRNAs is also challenging. These targets are difficult to produce in a homogeneous conformation and canonical methods used for purification of short RNAs may not be appropriate. For instance, because of their large size, lncRNAs are difficult to purify or analyze using electrophoretic methods and they require the use of specific chromatography matrices, generally not commercially available in prepacked, high-resolution and fast flow format  (Chillon et al.
e. by SV-AUC and SAXS. Several online tools are available for calculating such parameters from the sequence of the lncRNA of interest (i. e. : from https://molbiol-tools. ca). Independent of the tool used, the values obtained in silico are estimates of experimental values, which can later be obtained by mass spectrometry or size exclusion chromatography coupled with multi-angle laser light scattering (SEC-MALLS)  (Uroda et al. 2020 ), but are sufficient to ensure data reproducibility, provided that the same parameters are used consistently, throughout an entire lncRNA structure project. Besides molecular parameters, sequence analysis also informs about SNPs and PTMs. This information can be obtained by data mining existing databases via genome browsers, and is important because SNPs and PTMs often correlate with potentially interesting functional regions, or regions of medical relevance, which are important targets for functional probing by structure-based mutagenesis, once the lncRNA structure has been determined.
Although the medical implications of fungal introns are of a more immediate impact for human health and thus most initial studies targeted the $800-nt long ai5c intron expressed in fungal mitochondria, the first highresolution 3D crystal structures of the intron were obtained using the bacterial homolog from Oceanobacillus iheyensis, which is much shorter, i. e. $400 nt long  (Toor et al. 2008; Pyle 2010). Two approaches can be followed to identify lncRNA sequence conservation. First, one should mine the Rfam database to identify families associated with the lncRNA of interest  (Kalvari et al. 2018 ). Second, using the fulllength sequence of the target, one can use BLAT to identify putatively homologous regions in the genome of other annotated organisms, and extract those putatively similar sequences for pairwise or multiple sequence alignments  (Kent 2002; Uroda et al.
2019). Importantly, before performing experimental probing on these homologs, one needs to confirm experimentally by transcriptomics analysis and RACE or long-read sequencing that these lncRNA are actually expressed, and determine what their exact 5 0 -and 3 0 -boundaries are (see above). Finally, some online tools allow for the identification of known domains, such as PAN-like triple helices or TEderived repeat regions like the Alus (see above), or repeats, which is useful to detect putative intra-molecular interactions. Identification of repeats can be performed with a number of algorithms, such as RepeatMasker (http://www. repeatmasker. org), REPuter  (Kurtz et al. 2001) , the modules palindrome and equicktandem of the EMBOSS suite  (Rice P et al. 2000) , or Dfam, which searches for repeats in genomic DNA  (Hubley et al.
High-resolution methods Despite informing on lncRNA globularity and tertiary structure interactions, chemical probing cannot offer a direct 3D atomic visualization of the target lncRNA, which instead requires structural biology approaches. Of the most frequently-used high-resolution structural methods, NMR and X-ray crystallography have been employed successfully on lncRNAs, but so far only for the characterization of short structural elements (Figure  2 , Table  3 ). NMR has been used to solve the structure of a 14-nt fragment from the $17,000-nt long human XIST [PDB id. 2Y95,  (Duszczyk et al. 2011) ]. However, NMR suffers loss in sensitivity and increased spectral complexity with large (i. e. > 50 kDa) macromolecular targets  (Furtig et al. 2003). For reference, the largest RNA structure determined by NMR so far is the HIV-1 core packaging signal, which counts 155 nt [PDB id 2N1Q,  (Keane et al.
2015) ]. As a consequence, at least in the immediate future, the contribution that NMR will offer to the characterization of lncRNA will remain limited to structure determination of short motifs and their dynamics  (Marusic et al. 2019). By contrast, X-ray crystallography is, in principle, applicable to large macromolecules. However, X-ray crystallography for RNA is more challenging than for proteins, particularly with respect to construct engineering for obtaining well-diffracting crystals  (Wiryaman and Toor 2017; Gomez and Toor 2018)  and to phasing  (Marcia et al. 2013; Marcia 2016). Except for the ribosome, the spliceosome, RNase P, and group I and II introns  (Pyle 2014) , RNA crystal structures are available only for short molecules (<200 nt long). In the lncRNA field, X-ray crystallography has thus only been used so far to solve the structures of the 76-nt long PAN-like triple helix from the $8,000-nt long human MALAT1 [PDB id 4PLX, 3.
1 Å resolution,  (Brown JA et al. 2014) ], of the 12-nt long G4 motif of the $200-10,000-nt long human TERRA [PDB 3IBK, 2. 2 Å resolution,  (Collie et al. 2010) ], and of a 20-nt long duplex from the $4,000-nt long human GAS5 [PDB id 4MCE and 4MCF, 1. 9 Å resolution,  (Hudson et al. 2014) ]. In the future, X-ray crystallography of entire lncRNAs or larger lncRNA domains will be possible, provided that folded lncRNAs forming intramolecular tertiary contacts are identified and a robust, medium-to-high throughput functional assay is developed to assist structural engineering of crystal constructs. Finally, electron microscopy (EM) is another wellestablished technique for high-resolution structural biology, which is gaining momentum, particularly since the development of direct electron detectors  (Kuhlbrandt 2014).
2009; Lavender et al. 2015) , and the secondary structure of the 5 0 -UTR region of the mouse hepatitis virus (MHV)a SARS-CoV-2 related coronavirushas been characterized by in vitro and in virio SHAPE and probed by replication assays  (Yang D et al. 2015). Certain functional segments of viral RNA genomes have also been characterized by structural biology, either at high-resolution, as for the crystal structure of the beet western yellow virus (BWYV) ribosomal frameshifting pseudoknot [PDB id 437 D, 28-nt long, 1. 6 Å resolution,  (Su et al. 1999) ], or at low resolution, i. e. by SAXS and AFM, as in the case of the fulllength HCV internal ribosome entry site (IRES)  (Perard et al. 2013; Garcia-Sacristan et al. 2015). Finally, AFM has been used to characterize non-naturally-occurring RNAs, i.
Funding Our work has partly been funded by the  Agence Nationale de la Recherche  [ ANR-15-CE11-0003-01 ], by the  Agence Nationale de Recherche sur le Sida  et les h epatites virales [ANRS, ECTZ18552], by  ITMO Cancer  [ 18CN047-00 ], and by the  Fondation ARC  pour la recherche sur le cancer [PJA-20191209284]. The  Marcia lab uses the platforms of the Grenoble Instruct Center [ISBG :  UMS 3518 CNRS-CEA-UJF-EMBL ] with support from  FRISBI  [ ANR-10-INSB-05-02] Q2  and  GRAL  [ ANR-10-LABX-49-01 ] within the  Grenoble Partnership for Structural Biology  (PSB).
com/ s/666c9998f22 d661d72f3 RepA (full-length) 1,630 mouse Crosslinking N. A. (Liu F et al. 2017) TERRA (G4) 12 (of 200-10,000) human X-ray crystallography PDB: 3IBK (Collie et al. 2010) XIST (full-length) 19,281 human in vivo PARIS Sequence Read (Lu et al. 2016) Archive: SRP074108 XIST (full-length) 17,946 mouse in vivo PARIS Sequence Read (Lu et al. 2016) Archive: SRP074108 XIST (AUCG 14 (of 19,281) human NMR PDB: 2Y95 (Duszczyk et al. 2011) tetraloop hairpin) N. A. : not available.
Phenomenological models  [116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126]  provide full gravitational waveforms by tuning an extended PN inspiral expansion and separate analytical functions for the merger and ringdown waveforms to NR simulations that have been combined with PN or EOB inspiral waveforms, and then smoothly combining these three regions. Surrogate or reduced order models of NR or EOB waveforms  [127, 128, 129, 130, 111, 131, 132, 133, 134, 135, 136, 137]  can significantly accelerate these waveforms while keeping high accuracy. They have become indispensable tools for GW data analysis over the past several years. Surrogate models fit interpolated decomposed waveform data pieces over the binary parameter space and also compress the waveform in time or frequency. The above modeling approaches rely mostly on traditional polynomial interpolation or fitting techniques for model construction.
Gravitational-wave signal searches Searches for GW signals in ground-based detector data are typically split into four different types, depending on their search strategy. The first type includes CBCs. BBH, BNS and NSBH are the most common type of sources and the only type of source detected to date. The second type, which we refer to as GW "bursts", includes transient sources with an unknown or partially modeled signal morphology, as for example, the ones produced by CCSNe. The third source type includes long-duration, continuous GWs that may be produced by an individual rotating neutron star. The fourth type is a stochastic background of GWs, which could consist of remnant GWs from the Big Bang or distant unresolved CBCs. In this section, we review ML approaches that have been developed to enhance LIGO and Virgo searches for these different types of GW signals.
CBC searches Matched filtering  [18]  is a common approach to search for sources from CBCs in LIGO and Virgo data  [6]. Matched filtering works by first taking as input the raw calibrated strain data. Then a template bank of waveforms is generated spanning a large astrophysical parameter space. The matched filter searches then produce a list of GW triggers by cross-correlating the GW data with the waveforms in the template bank divided by the interferometer's spectral noise density. Signal consistency tests are done on triggers above a certain SNR threshold generated from the matched filtering algorithm in order to determine the time-frequency distribution of power in a trigger. This distribution is compared to what would nominally be expected from the power in the matched filtering waveform.
The method uses 100 different parameterizations of the phenomenological model. cWB pre-processing data is used to prepare images which are fed into the CNN. Red-green-blue (RGB) images are produced to determine the number of detectors where a signal is present: Red (R) for LIGO Hanford, green (G) for LIGO Livingston and blue (B) for Virgo. The algorithm is shown to improve the efficiency of cWB in its standard configuration. A technique to reduce the background of searches for galactic CCSNe in singleinterferometer configurations was developed in Ref. [32]. The method is based on a supervised evolutionary algorithm, Genetic Programming (GP). The procedure assumes that the event time and the distance of the CCSN are known from neutrino and optical observations. The GP algorithm is first trained on off-source data to produce a multivariate expression of the trigger features which is used as a cut to lower the search background.