dataesr/scientific-paragraphs-categorization huggingface dataset:
For phylogenetic analysis, the 10 sequences with the highest BLAST scores recovered from the NBCI Gen-Bank nucleotide sequence databases (http://www. ncbi. nlm. nih. gov/nucleotide/, accessed on 17 September 2022) were incorporated in the phylogeny reconstruction, in addition to reference sequences for HEV genotypes  [2]  and the HEV sequence obtained from the present case. Nucleotide alignments were performed using the MUSCLE software (http://www. ebi. ac. uk/Tools/msa/muscle/, accessed on 17 September 2022). The evolutionary history was inferred in MEGA software (http://www. megasoftware. net/, accessed on 17 September 2022) using the Neighbor-Joining method and the Kimura 2-parameter method. The bootstrap test included 1000 replicates. All data were generated as part of the routine work at Assistance Publique-Hôpitaux de Marseille (Marseille university hospitals).
Liver transplantation was not performed due to major hemodynamic instability, despite the administration of norepinephrine, terlipressin, and epinephrine. Two days post-admission, ALT was 4549 IU/L, bilirubinemia was 70 µmol/L, prothrombin index was 11%, and the patient died. The HEV genotype was 3f (GenBank Accession no. OL310924) (Figure  1 ). HAV infection or false-positivity of anti-HAV IgM; no information was obtained regarding prior anti-HAV vaccine immunization. SARS-CoV-2 PCR was negative at symptom onset and upon admission to the ICU. Metformin plasma concentration was 33 µ g/mL, whereas normal values are 1-4 µ g/mL, and the concentration is considered toxic when ≥10 µg/mL. This caused acute renal failure, and led to hemodialysis that successfully fought biguanide intoxication. Liver transplantation was not performed due to major hemodynamic instability, despite the administration of norepinephrine, terlipressin, and epinephrine.
Two days post-admission, ALT was 4549 IU/L, bilirubinemia was 70 µ mol/L, prothrombin index was 11%, and the patient died. The HEV genotype was 3f (GenBank Accession no. OL310924) (Figure  1 ). The HEV sequence obtained by Sanger sequencing from the present case is indicated by a white bold font and a black background. The 10 sequences with the highest BLAST scores recovered from the NBCI GenBank nucleotide sequence databases (http://www. ncbi. nlm. nih. gov/nucleotide/, accessed on 17 September 2022), indicated by bold font, were incorporated in the phylogeny reconstruction, in addition to reference sequences for the HEV genotypes  [2]. Nucleotide alignments were performed using the MUSCLE software (http://www. ebi. ac. uk/Tools/msa/muscle/, accessed on 17 September 2022). The evolutionary history was inferred in the MEGA6 software (http://www.
megasoftware. net/, accessed on 17 September 2022) using the Neighbor-Joining method and the Kimura 2-parameter method. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary dis-  The HEV sequence obtained by Sanger sequencing from the present case is indicated by a white bold font and a black background. The 10 sequences with the highest BLAST scores recovered from the NBCI GenBank nucleotide sequence databases (http://www. ncbi. nlm. nih. gov/nucleotide/, accessed on 17 September 2022), indicated by bold font, were incorporated in the phylogeny reconstruction, in addition to reference sequences for the HEV genotypes  [2].
Nucleotide alignments were performed using the MUSCLE software (http://www. ebi. ac. uk/Tools/msa/muscle/, accessed on 17 September 2022). The evolutionary history was inferred in the MEGA6 software (http://www. megasoftware. net/, accessed on 17 September 2022) using the Neighbor-Joining method and the Kimura 2-parameter method. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree; the scale bars indicate the number of nucleotide substitutions per site. Bootstrap values > 50% are labeled on the tree. The patient had not been questionable upon admission and he lived alone.
Wi-Fi is a popular wireless technology and is continuously extended to keep pace with requirements such as high throughput, real-time communication, dense networks, or resource and energy efficiency. The IEEE 802. 11ax standard, also known as Wi-Fi 6, promises to provide data rates of up to almost 10 Gb/s, lower energy consumption, and higher reliability. Its capabilities go far beyond Wi-Fi 5 (802. 11ac) and novel technical concepts have been introduced for this purpose. As such, the Wi-Fi 6 standard includes Multi-User Orthogonal Frequency Division Multiple Access (MU OFDMA), Multi-User Multiple-Input Multiple-Output (MU MIMO), new mechanisms for Spatial Reuse (SR), new mechanisms for power saving, higher-order modulation, and additional minor improvements. In this paper, we provide a survey of Wi-Fi 6.
Wi-Fi 6 operates on the 2. 4, 5, and 6 GHz frequency bands. Together, these features lead to high data rates of up to 9. 6 Gb/s. The successor to the standard of Wi-Fi 6, IEEE 802. 11be (Wi-  Fi 7) , is currently under study and targets ultra-high-throughput networks. However, it is only in its early stages and is not expected to be introduced before 2024. This survey serves as a basis for future improvements to Wi-Fi 7, making it a helpful research tool.
Orthogonal Frequency Division Multiplexing (OFDM) OFDM is a multicarrier modulation scheme for data transmission in a channel. This means that the channel is subdivided into multiple subcarriers or tones, and data are transmitted in parallel over the subcarriers. The distance between the subcarrier frequencies may be low, e. g. , 312. 5 kHz in Wi-Fi 5. To minimize interference among the subcarriers, the frequencies must be chosen carefully to be orthogonal to each other. Then, each subcarrier is modulated independently. OFDM typically spreads the information of a high-bitrate stream over 2 n subcarriers. To that end, m-bit groups are assigned to each subcarrier and converted to a constellation point using QAM. The entirety of all constellation points forms an OFDM symbol. Conversion to electromagnetic waves turns this OFDM symbol into an OFDM signal.
An OFDM signal transmits one or multiple concatenated OFDM symbols. To avoid interference in time, a Guard Interval (GI) is added after every symbol. It is a short time interval between two consecutive symbols that protects against imperfect synchronization as successive transmissions must never overlap, particularly in the case of clock drifts or multipath delay spread. Symbol duration and GI are visualized in Figure  3a. In Wi-Fi 5, the symbol duration is 3. 2 µs long and the GI takes 0. 4 µs or 0. 8 µs. OFDM in Wi-Fi 2-5 is used for data transmission. A high-bitrate data stream is divided into multiple lower-bitrate data streams. Thus, each subcarrier has a 1  N data rate of the total data rate. As data is transmitted in parallel at a 1  N data rate, the symbol time increases by N compared to the single-tone transmission.
IEEE 802. 11ac (Wi-Fi 5) introduces downlink MU MIMO to serve multiple stations in parallel. Wi-Fi 5 devices support up to eight spatial streams in parallel for downlink transmission. That is, the access point can transmit data to up to four different stations (with two spatial streams per station). MU MIMO leverages a transmit beamforming (TxBF) technique to concentrate the signal in four different directions, i. e. , one direction per station.
Modulation Modulation techniques maximize the number of bits transmitted per subcarrier. Wi-Fi 5 supports up to 256 QAM that modulates 8 bits of information per subcarrier. QAM modulates data in each subcarrier. More bits per OFDM symbol lead to a larger throughput under the condition that the Signal-to-Noise Ratio (SNR) is sufficient. Higher-order modulations are expected to be efficient only under favorable conditions, namely clear environments and short distances. The coding rate identifies the portion of bits that is used for the transmission of data and Forward Error Correction (FEC). The supported coding rates are 1/2, 2/3, 3/4, and 5/6. A coding rate of 5/6 means that 83. 3% (i. e. , 5/6) of the data stream is used to carry data and 16. 7% is used to carry FEC. The combination of modulations namely, Binary Phase Shift Keying (BPSK), Quadrature Phase Shift Keying (QPSK), 16-QAM, 64-QAM, and 256-QAM with coding rates, allow the standard to choose the data rate dynamically according to the radio environment  [5].
Enhanced Distributed Channel Access (EDCA) Enhanced Distributed Channel Access (EDCA) provides QoS for wireless networks by enhancing DCF in legacy Wi-Fi. It introduces prioritized traffic classes, so-called access categories (ACs), and gives prioritized access to higher-priority packets. The nodes label the data packets in the upper layers. EDCA defines four different ACs: video, voice, best effort, and background traffic. AC-specific inter-frame spaces, called Arbitrary InterFrame Space (AIFS), help to handle packets differently. A high-priority station waits for a shorter AIFS time before decrementing its backoff value. Moreover, EDCA enforces AC-specific min/max values for contention windows. EDCA achieves proportional QoS: higherpriority flows obtain more bandwidth than lower-priority flows.
The model was based on the backoff counter value and the number of available RUs. It then calculated the transmission and collision probability. Bellalta et al. [98]  focused on the AP-initiated OFDMA and MU MIMO features. In particular, the different A-MPDU must be aligned when several stations transmit in parallel through different sub-channels. Matlab simulations validated this analytical model. Network simulations have also been conducted to measure the performance of Wi-Fi 6. The authors of [99] validated OFDMA performance using NS-3 simulations. Naik et al. [100]  analyzed the performance of uplink multi-user OFDMA. They introduced the BSR delivery rate metric to measure a network's performance in dense deployments. They concluded that the throughput depends on the distribution of random access and scheduled access.
Then, with respect to the channel quality, the station selects the best-fitted MCS to achieve higher reliability and performance. Hussien et al. [181]  proposed a deep learning approach to select the best MCS. Link adaptation was modeled as a multi-label multi-class classification and predicted the performance of the system for both the optimal MCS and suboptimal MCS. They applied a deep convolutional neural network to predict the performance with a dataset generated with Matlab. The loss function gives a larger penalty for false positives; the MCS should not have been selected since it jeopardizes the reliability. An access point may have precise knowledge of the CSI of each station and should allocate an RU and MCS to each of them. This scenario corresponds to an optimization problem. Ha et al.
In this way, a controller may be able to control a heterogeneous set of Wi-Fi 6 access points. Researchers may also exploit this API to plug novel algorithms or evaluate performance in complex deployments. Wireless access points need a lightweight operating system to be executed on resourceconstrained hardware. OpenWrt (https://openwrt. org/, accessed on 1 September 2022) is a well-known Linux operating system for managing network devices including access points. It can be installed on any writable file system and provides more freedom for tuning the default configurations from vendors. OpenWrt is open source and can be modified to integrate novel software features. The operating system is not sufficient since some Wi-Fi features are implemented in the hardware. The firmware is a specific software installed in a small memory chip on the hardware and is in charge of the communication between the operating system and the NIC! (NIC!).
It provides the instructions to control specific hardware. Open-source firmware is beneficial for tuning some parameters. We identified three Linux firmwares: ath11k (https://wireless. wiki. kernel. org/en/users/drivers/ath11k, accessed on 1 September 2022): This firmware is designed for Qualcomm Technologies' Wi-Fi 6 chipset. Its source code is freely available (https://github. com/kvalo/ath11k-firmware, accessed on1 September 2022). MT7915 (https://github. com/openwrt/mt76/tree/master/mt7915, accessed on 1 September 2022): This firmware is designed for the Mediatek MT7915 chipsets, which consist of Wi-Fi 6 and Bluetooth 5 combo chipsets. MT7915 supports the Wave 1+ features listed in Table  7  and mostly targets routers, repeaters, and mesh networking equipment. It also supports EasyMesh features to create a meshed wireless topology of access points.
MT7921 (https://github. com/openwrt/mt76/tree/master/mt7921, accessed on 1 September 2022): This firmware is designed for the Mediatek MT7921 chipsets and mostly targets notebooks and routers. MT7921 supports Wi-Fi 6 with a 2 × 2 dual antenna. However, the firmware provides often very limited access to Wi-Fi 6 features (or even none for the non-open-source versions). Many features are implemented in the hardware and can only be executed as a blackbox.
The prototype is based on an FPGA and includes the software drivers used with Linux. Both the hardware and software implementations are freely available. It supports multiple different SDR platforms. Currently, Wi-Fi 6 features are under development but openwifi does not yet support the high bitrate of Wi-Fi 6. Xia et al. [190]  implemented a cross-technology communication scheme between Wi-Fi 6 and LoRa. They demonstrated the feasibility of their proposition by implementing a prototype in the USRP N210 hardware. However, the focus was on implementing the LoRa mechanisms and most Wi-Fi 6 features are not supported.
Multi-Access Point Cooperation To mitigate interference in overlapping BSSs, Wi-Fi 7 focuses on coordination between BSSs regarding channel access, scheduling, and joint transmission of a single data flow. Multi-access point coordination improves performance in two aspects: OFDMA and SR. Coordination among access points with regard to OFDMA means that different access points may assign the same RU to different stations if these stations do not interfere with each other. Otherwise, they need to assign different RUs to them in order to avoid interference. Coordination among access points with regard to SR means that access points exchange CSI to adjust their transmission power; therefore, they can reuse the spectrum more efficiently. This mechanism additionally provides modifications in coordinated beamforming.
The search was followed with manual curation of lower-scoring candidates (true positives were recognized by the pattern of conserved residues, compatible with known DJR MCPs, and by the absence of other conserved domains recognizable with the NCBI conserved domain database, https://www. ncbi. nlm. nih. gov/Structure/cdd/wrpsb. cgi), which resulted in the identification of 226 DJR MCPs encoded in 218 contigs (Supplementary File S1). The initial DJR profiles, nucleotide sequences and predicted proteins from the detected DJR MCP-encoding gut contigs are available at https://ftp. ncbi. nlm. nih. gov/pub/yutinn/Vinavirales_2022.
Classification of DJR MCP-Encoding Gut Metagenome Contigs Homologs of human gut DJR MCPs were identified by running BLASTP against the NCBI non-redundant protein sequence database (NR)  [30]  with an e-value threshold of 10 -6. The entire set of the DJR MCP sequences was clustered using MMSEQS2 [31], with a similarity threshold 0. 5; the resulting protein clusters were aligned using MUSCLE5  [32]  and iteratively compared to each other using HHSEARCH to establish the relationships among the clusters. Groups of related sequences were aligned using MUSCLE5  [32] ; alignments were compared to the rest of the set, again using HHSEARCH. The approximate maximum likelihood phylogenetic tree was reconstructed using FastTree with a WAG evolutionary model and Gamma-distributed site rates  [33]  (Supplementary File S2).
Abundance Calculation for the Major Capsid Protein Gene To estimate the abundance of HK97 and DJR MCP genes in human metagenomes, 3995 datasets of Illumina human gut metagenome reads, available at the Sequence Read Archive (https://www. ncbi. nlm. nih. gov/sra) as of December 10, 2021, were used. The reads from each dataset were mapped with Kraken2  [34]  to the HK97 MCP genes  [24]  and to the DJR MCP genes identified in this work (Supplementary File S1).
Host Assignment for Members of Vinavirales from the Human Gut Two approaches were used to infer the hosts for members of Vinavirales from the human gut. The first approach leverages the fact that most of these viruses are either found as prophages in the human gut metagenome or that related viruses have been sequenced as prophages along with their host genomes. For all DJR MCP-containing contigs, all predicted proteins were searched using BLASTP against a database consisting of proteins encoded in 24,757 high-quality complete genome assemblies from RefSeq and GenBank (https://ftp. ncbi. nlm. nih. gov/genomes/ASSEMBLY_REPORTS/), downloaded in November 20, 2021. Best hits for each protein from each MCP-encoding contig were recorded; the lowest-level taxon that accounted for at least half of the hits was taken as the likely source taxon for the prophage (Supplementary File S6).
Phylogenetic Analysis of Vinavirales MCP Using Vinavirales MCPs detected in human gut metagenomes as queries, homologous protein sequences were extracted from GenBank; previously detected PM2 group MCPs  [8]  were added to the sequence set. The protein sequences were clustered at 80% identity and aligned using MUSCLE5; a phylogenetic tree was constructed from this alignment using IQ-TREE (LG + F + R5 model chosen according to BIC by built-in model finder)  [36]. The alignment, the tree, and the IQ-TREE output are available at https://ftp. ncbi. nlm. nih. gov/ pub/yutinn/Vinavirales_2022.
Gene Composition and Protein Function Prediction for Members of Vinavirales A set of 54 (pro)phage genome sequences was selected to represent the MCP sequence diversity with the preference for known phages and contigs that included extended DNA sequences upstream and downstream of the MCP gene. For the purpose of genome annotation, ORFs were predicted in contigs using Prodigal in the metagenomic mode. Amino acid sequences were initially clustered using MMSEQS2 with the similarity threshold 0. 5; the resulting protein clusters were aligned using MUSCLE5 and iteratively compared to each other using HHSEARCH. Highly similar clusters (alignment footprint coverage threshold 0. 5; relative sequence similarity threshold 0. 05) were progressively aligned to each other using HHALIGN. Cluster alignments were compared to publicly available profile databases (DB_mmCIF70_21_Mar, Pfam-A_v35, Uniprot-SwissProt-viral70_3_Nov_2021, and NCBI_Conserved_Domains (CD)_v3.
Protein Structure Prediction with Alphafold2 and Protein Structure Comparison In order to predict protein structures from the alignments, a local version of ColabFold v. 1. 2  [37]  (Mirdita et al. , Cambridge, USA; alphafold2_batch) was downloaded on 15 December 2021. ColabFold was run with default settings with the following modifications: input was a precomputed alignment generated as described above, and -num-model 1 and -num-recycle 3 were set to 1 and 3, in order to minimize the computational cost. Structures retrieved with ColabFold were compared against reference protein structure databases using DALI  [38]  and foldseek  [39]. For DALI, a mirror of the pdb database as of December 2021 was dereplicated at 70% identity and transformed into a DALI compatible database. Apart from using the 70% dereplicated database, DALI was run with default parameters.
DJR MCP-Encoding Contigs in Gut Metagenomes We screened 23,119 EMBL-assembled human gut metagenomes for DJR MCP by searching the set of open reading frames (ORFs) from metagenome contigs using HH-SEARCH, with a collection of DJR MCP profiles representing different groups of viruses within Varidnaviria used as queries (see Methods). This search and subsequent manual curation of lower-scoring candidates yielded 226 DJR MCP sequences encoded in 218 contigs (Supplementary File S1). This set of DJR MCPs was augmented with closely related homologs, that were retrieved by running BLASTP against the NCBI non-redundant protein sequence database and clustered using MMSEQS2. The resulting protein clusters were aligned and compared to each other using HHSEARCH. This analysis revealed five distinct clusters of DJR MCPs (Figure  1 ; Supplementary Files S1 and S2).
Ten closely similar MCPs on 10 contigs belong to fish polintoviruses; these sequences group with fish, reptile, and four 'adintovirus' MCP sequences from GenBank, so their native association with human gut commensals is questionable, at best. Finally, six MCPs on six contigs belong to the 'Bam35 group' of tectiviruses  [8, 43]. As well as the MCP, these contigs encode the FtsK-like packaging ATPase, peptidase M23, family B DNA polymerase and a lysozyme; their closest relatives among identified phages are Bacillus tectiviruses Wip1, AP50, and V Ba and the Thermus phage phiKo  [44]  (AYJ74688; Supplementary File S2).
DJR MCP-Encoding Contigs in Gut Metagenomes We screened 23,119 EMBL-assembled human gut metagenomes for DJR MCP by searching the set of open reading frames (ORFs) from metagenome contigs using HHSEARCH, with a collection of DJR MCP profiles representing different groups of viruses within Varidnaviria used as queries (see Methods). This search and subsequent manual curation of lower-scoring candidates yielded 226 DJR MCP sequences encoded in 218 contigs (Supplementary File S1). This set of DJR MCPs was augmented with closely related homologs, that were retrieved by running BLASTP against the NCBI non-redundant protein sequence database and clustered using MMSEQS2. The resulting protein clusters were aligned and compared to each other using HHSEARCH. This analysis revealed five distinct clusters of DJR MCPs (Figure  1 ; Supplementary Files S1 and S2).
DJR MCP genes were detected in 7% of the datasets Viruses 2022, 14, 1842 6 of 16 (294 of the 3995), as compared to the 96% of the datasets (3851 out of 3995) in which HK97 MCP genes were detected. This stark contrast reaffirms that members of Varidnaviria are a minor component of the human gut virome. While members of the order Crassvirales, the most abundant phages of the realm Duplodnaviria in the human gut, accounted for 28% of the HK97 MCP reads (6,346,752 of the 22,722,487), the Vinavirales-like group of DJR MCPs dominated the varidnavirus fraction of the gut virome in terms of both contig number and read counts (Figure  1A ). Indeed, 92% of the reads that mapped to DJR MCP-encoding contigs belonged to this group (13,832 of 15101). Of the 176 contigs that represent Vinavirales in the analyzed human gut metagenomes, 142 were identified as prophages, as attested by the presence of host flanking genes (see Section 2), and four probably originated from virus particles, whereas the remaining 30 could not be identified as either phage or prophage.