dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomes for B. thailandensis, C. violaceum, and P. syringae are available at the National Center for Biotechnology Information (NCBI) under accession numbers NC_007651 (Chromosome I)/NC_007650 (Chromosome II), NZ_PKBZ01000001, and NC_004578 (Chromosome)/NC_004633 (Plasmid A)/NC_004632 (Plasmid B), respectively. An improved annotated draft genome of C. violaceum is available under NCBI BioProject accession number PRJNA402426 (GenBank accession number PKBZ00000000). Data for resequencing efforts for B. thailandensis and P. syringae are under NCBI BioProject accession numbers PRJNA402425 and PRJNA402424, respectively. Metabolomics data and transcriptomics data are also available at the JGI Genome Portal (Nordberg et al. , 2014) under JGI proposal identifier 502921. MZmine XML parameter files for all.
Filter plates were incubated at 27°C with gentle shaking (~0. 32 rcf). For each plate, a custom R script (RandomArray. R [see script at https://github. com/ShadeLab/PAPER_Chodkowski_mSystems_2017/blob/master/R_an alysis/RandomArray. R]) was used to randomize wells for each organism assigned to RNA extraction (16 wells) and flow cytometry (5 wells). The following procedure was performed for each organism when a transwell plate was destructively sampled: i) wells containing spent culture assigned to RNA extraction were pooled into a 1. 5 mL microcentrifuge tube and flash frozen in liquid nitrogen and stored at -80 until further processing. ii) 20 μL from wells assigned for flow cytometry were diluted into 180 μL Tris-buffered saline (TBS; 20 mM Tris, 0. 8% NaCl [pH 7. 4]). In plate arrangements where P.
Four final feature data sets from polar and nonpolar analyses in both ionization modes were analyzed in MetaboAnalyst 5. 0 (Pang et al. , 2021). Features were normalized by an ITSD reference feature (see Dataset 5 at https://github. com/ShadeLab/Paper_Chodkowski_MonocultureExometabolites_2020/t ree/master/Datasets) and cube root transformed. Reference features for polar analyses in positive ([ 13 C, 15 N]proline) and negative ([ 13 C, 15 N]alanine) modes were determined by the ITSD with the lowest CV value across all samples. The reference feature for nonpolar data sets was the ITSD ABMBA. Heat maps were generated in MetaboAnalyst using Ward's clustering algorithm with Euclidean distances from Z-scored data. Data for each sample are the averages from independent time point replicates (n = 2 to 4).
, 2020). These strategies have been applied in recent land plants to identify HGT candidates For instance, the genomes of two fern species, Azolla filiculoides and Salvinia cucullata, were sequenced and screened for HGT by  Li et al. (F. -W. Li et al. , 2018) ). A fern-specific insect resistance gene identified in A. filiculoides appears to have originated from bacteria via HGT based on phylogenetic analyses showing clustering of the fern gene with bacterial orthologs. The authors confirmed candidates by analyzing flanking genes and performing RT-PCR. A similar methodology was used in a large analysis of HGT across land plants, comparing candidates to "donor" and "recipient" databases  (Ma et al. , 2022). Physcomitrellopsis africana inhabits transitional zones between grassland and forests in coastal habitats in South Africa.
Quality control of genomic and transcriptomic reads The genomic short reads were first assessed with FASTQC v. 0. 11. 7  (Andrews, 2010). In preparation for assembly with Haslr and Wengan, Sickle v. 1. 33 with a minimum quality score threshold of 30 (-q) and a minimum length of 50 bp (-l) was employed for reads trimming. Nanoplot v. 1. 21. 0 was used to quantify and assess the quality of the Oxford Nanopore PromethION long-read sequences. To detect potential contamination, the long reads were aligned against the provided bacterial, human and viral databases with Centrifuge v. 1. 0. 4-beta (p+h+v; min-hit length was increased to 50 bp)  (Kim et al. , 2016). Reads aligning to the target loci in the databases were removed. The quality of the transcriptomic short reads was assessed with FASTQC v.
Transcriptome assembly The transcriptome was independently assembled to provide protein-level evidence for the structural annotation of the genome, using Trinity v2. 6. 6  (Grabherr et al. , 2011) , with a minimum contig length of 300bp. Contigs with minimal read support, post assembly, were removed (FPKM > 0. 5) with RSEM v1. 3. 0 (B. Li & Dewey, 2011). Transdecoder v3. 0. 1  (Haas et al. , 2016)  was used to translate the remaining contigs into Open Reading Frames (ORFs) and remove sequences without a viable frame. To aid Transdecoder in the identification of ORFs, searches against the Pfam database were performed with HMMER v3. 1b2 (Z. Zhang & Wood, 2003). Transdecoder annotated the putative transcripts as complete, partial, and internal. Those without a defined start and stop codon (defined as internals) were removed (split_frames.
py). The final set of peptide sequences were functionally annotated with EnTAP v0. 8. 0  (Hart et al. , 2020)  against NCBI's nr protein database and the UniProt/Swiss-prot reference databases. EnTAP was run with contaminant filters that included bacteria, archaea, fungi, and insecta. Transcripts with high confidence alignments to these organisms were removed (contam_removal. py).
Genome assembly Hybrid genome assembly, integrating both long and short-read data, was conducted with MaSuRCA v. 4. 0. 3  (Zimin et al. , 2013 ), Wengan v. 0. 2 (Di Genova et al. , 2021) , and Haslr v. 0. 8a1  (Haghshenas et al. , 2020). Additionally, a separate assembly using only long-reads as input was conducted with Flye v. 2. 5 (with three polishing iterations)  (Kolmogorov et al. , 2019). The Flye, Wengan, and Haslr assemblies were polished following long-read alignment with Medaka v1. 3. 2 (github. com/nanoporetech/medaka). Post assembly with Wengan, the assembly was filtered to remove scaffolds less than 3 Kb. To further improve the accuracy of the assemblies, the hybrid assembly generated by MaSuRCA was polished with the Illumina short reads using Pilon v. 1. 24  (Walker et al. , 2014).
This filter was applied with Interproscan v. 5. 35-74. 0  (Jones et al. , 2014)  using the Pfam database v32. 0  (Finn et al. , 2014). It is worth noting that mono-exonic genes can be the result of fragmented annotations and the target metric of 0. 2 (mono:multi-exon gene ratio) is often achieved through protein domain filters  (Vuruputoor et al. , 2022). Metrics for the gene predictions were generated with gFACs v1. 1. 3  (Caballero & Wegrzyn, 2019)  and BUSCO. After assessment, the filtered BRAKER gene predictions were selected for functional annotation with EnTAP v. 0. 10. 8  (Hart et al. , 2020). Functional annotation reports from EnTAP (both sequence similarity search and EggNog taxonomy scope classifications) allowed for the identification of non-target species scaffolds in the assembly  (Huerta-Cepas et al.
Horizontal gene transfer candidate identification To identify candidate HGTs in Physcomitrellopsis africana, protein sequence similarity searches were conducted with Diamond v2. 1. 8  (Buchfink et al. , 2021). The protein sequences of Physcomitrellopsis africana were aligned against "donor" databases, which included sequences from bacteria, fungi, archaea, and metazoa from NCBI's nr database. Additionally, the same proteins were aligned to "recipient" databases containing sequences from Streptophyta, Tracheophyta, Embryophyta, Viridiplantae, and Spermatophyta  (Ma et al. , 2022). Although these categories are not fully exclusive, each database was utilized separately to systematically assess presence across plants at different evolutionary divergence points. To identify candidate genes representing putative horizontal gene transfer (HGT) events unique to Physcomitrellopsis africana, the following criteria were utilized: genes were required to have between one and four significant sequence alignments (E-value <1e-5) to microbial donor databases, while exhibiting no significant sequence similarity to plant recipient databases.
This range of one to four microbial alignments was selected to capture potential HGTs while avoiding ubiquitous domains shared across many microbes. The lack of hits to plant databases was intended to enrich for Physcomitrellopsis africana-specific sequences, rather than those conserved across plants through vertical inheritance. At this stage, any scaffolds containing only bacterial or fungal genes, without any plant-related genes, were removed from the assembly. To assess the validity of the two proposed HGT candidates, the genes were visualized on the Integrated Genome Viewer (IGV). The alignments of the HGT candidates from the 'nr' database, as well as the transcripts assembled with StringTie2  (Kovaka et al. , 2019) , were visualized.
Transcriptome assembly A total of 36. 58M Illumina short reads were generated. Following quality filtering using Trimmomatic, the dataset was reduced to 31. 84M reads. The de novo assembly process with Trinity yielded 143,150 contigs. After expression filtering via RSEM, the number of contigs was reduced to 123,373. Identifying open reading frames (ORFs) in the contigs resulted in 110,852 successfully translated transcripts. The average sequence length of these translated ORFs was 803 bp, with an N50 value of 1,038 bp. To enhance the quality of the transcriptome assembly, internal sequences and putative contaminants were removed, resulting in 74,997 total transcripts. After removing internal sequences and contamination, the total unique sequences with an alignment is 51,982 (69. 3%; File S1).
8%[D:13. 6%], with a mono:multi ratio of 0. 08. Reciprocal BLAST conducted by EnTAP produced an annotation rate of 78%, of which 93. 6% aligned to Physcomitrium patens (Table  1; File S3 ). This contrasts with the annotation rate of 50% in F. hygrometrica  (Kirbis et al. , 2022) , likely attributed to significant divergence time from the model Physcomitrium patens, and the lack of closely related species in public genomic databases  (Kirbis et al. , 2020; Rahmatpour et al. , 2021). The higher annotation rate in this study suggests that many genes found in Physcomitrium patens but not F. hygrometrica may have been acquired in the ancestor of the Physcomitrellopsis-Entosthodon-Physcomitrium clade  (Medina et al. , 2019).
Separately, the annotated gene space was aligned to a set of "donor" and "recipient" databases. This identified 31 potential horizontal gene transfer (HGT) events. Following the set of best practices outlined by  François et al. (2020) , an additional set of 10 scaffolds were found to be contaminated as the flanking genes of the potential HGT candidates were not of plant origin. This resulted in their removal (HGT filter). Two unique HGT candidates survived these filters (Fig. 4 ). The first HGT candidate, Pa1_22336. 1 (alignment to Paracoccaceae bacterium), contains a methyltransferase domain. A recent phylogenetic analysis conducted among bryophytes revealed that DNA methyltransferases identified in Marchantia polymorpha, Physcomitrium patens, and Anthoceros angustus form clades with their bacterial homologs.
S4 ). Parameters used for removing a set of contaminated scaffolds from the draft genome based on functional characterization of the annotated gene space. Scaffolds with a length of 10 kb or less, and those with 40% or more of their total genes classified as archaea, bacteria, or fungi were removed. Additionally, scaffolds with a length greater than or equal to 10 Kb and having 55% or more genes classified as archaea, bacteria, or fungi were also excluded. In total, 831 scaffolds were removed as a result of this filtering process. B Identification of HGT candidates via sequence similarity comparisons of Physcomitrellopsis africana proteins against both "donor" databases (archaea, bacteria, fungal, and metazoan) and "recipient" databases (Streptophyta, Viridiplantae, Tracheophyta, and Spermatophyta).
Proteins with >1 and <4 hits against all of the donor databases, and no hit against recipient databases were labeled as HGT candidates. This analysis was conducted on the contaminated scaffolds removed in A, confirming their contamination status. On the post-filtered scaffolds, there were some putative HGTs that were flanked by contaminants. These scaffolds were also removed from the assembly (additional 10), resulting in the retention of two HGT candidates in the final analysis.
Physcomitrellopsis africana was sampled in October 2010 from a coastal forest in the Eastern Cape Province of South Africa (Dwesa National Park, along trail to chalets behind campground, coordinates 32 18. 222' S 28 49. 666', at ± sea level). The voucher specimens collected by Goffinet (collection numbers 10326 and 10329, with T Hedderson and N Wilding) are deposited in the CONN herbarium under accession numbers CONN00235389 and CONN00235388, respectively. Specimen 10326 (culture and long read library DNA #5074) provided DNA for genome sequencing and assembly, while 10329 (RNA and Illumina DNA #5075) provided RNA and DNA for Illumina sequencing. Sterile cultures were first established on Knop medium using spores from a single operculate capsule. The gametophytes were harvested, ground and spread on a rich sandy loam soil in PlantCon tissue culture containers (MP Biomedicals, Solon, OH, USA), and maintained in a growth chamber under 16 h of daylight at about 24 C.
Acknowledgements This research was supported by the project  PRESupply  funded by the  Walloon Region of Belgium  (Convention # 7566 ). Computational resources were provided by the  Consortium des Équipements de Calcul Intensif (C ÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11.
Minimum Purchase Commitment with carrier e at period t (e) Q l Datasets and indices Set Description Index T Planning horizon t, n E Candidate carriers e I Cross-docking facilities i K Customer areas k Ie Network of cross-docking facilities operated by carrier e i I k Network of cross-docking facilities that can serve customer area k i K i Customer areas that can be served from cross-docking facility i k E k Carriers that can serve customer area k e L Capacity levels of cross-docking facilities l Input parameters Symbol Description N Number of periods in the planning horizon (N = |T |) He Duration of contracts with carrier e (number of periods) M t e.
I. Introduction W IRELESS networks today have more subscribers than wired connections for many features, the most important of which are mobility and ease of use of mobile applications. Mobile users are looking forward to having a high data connection to improve the usage of their applications and browse the internet easily. Therefore, advanced technologies such as multiple-input-multiple-output (MIMO), millimetre wave (mmWave) and expected new technologies in the future support massive high data rate in 5th generation (5G) and 6th generation (6G). Consequently, there is a need to improve wireless networks performance  [1]. This especially occurs in blind areas where there is no line of sight (LOS) between users and the base station (BS). The advent of intelligent reflecting surface (IRS) eliminated the effect of the blind zones.
IRS is considered a revolutionary technology which is able to improve the performance of wireless data transmission systems  [2]. IRS consists of meta surface of massive number of reflecting elements of configurable phase shift to serve a terminal device in the far-field and near-field regions  [3]. Moreover, reduction of energy consumption in communications which eliminate unnecessary electromagnetic (EM) waves are an incontestable trend today. Hence, some cellular transceivers which could be replaced by IRSs can contribute to support this trend  [4]. Comparing IRS to the traditional active antenna arrays, it acts as a passive reflective antenna array that improves the achievable spectral efficiency and hence data rate in a cost-effective. Moreover, it improves the energy-efficient manner and constitutes a self-sustaining energy solution  [5].
Several works have studied the performance improvements due to the use of IRS, a little of them studied the impact of the IRS positions which will be mentioned in the next section of related work. As it is known, the main benefit of mobile phones is the mobility while using them. Inspired by this, IRSs positions are expected to be more effective if they follow the same behavior of mobility as mobile phone users. Moreover, in this work, we contribute to have mobile IRSs selected based on its positions. As has been suggested in all work to date, the IRS used to be of a fixed position and its selection is based on various criteria based on the proposals of the authors of previously published works. The goal itself is to improve the signal-to-noise ratio (SNR) to support wireless communications for high data rates and to provide coverage for blind areas.
Since this resulting neural network architecture is not really used for time series prediction but to predict future positions of IRSs, and since it is using what is generally called tabular data (contrarily to timeseries), we call it in the rest of the paper as our decision neural network (DNN). Optimizing performance parameters for a dynamic scenario, such as the number of handovers per mobile session or how long a mobile phone does not change its IRS is very complex with this approach. Of course, it can be adapted but this will add one more dimension which is time. Instead of optimizing every time (t) and independently of each other, the time dimension must be considered globally in the optimization problem. Thus, we propose another approach based on the Markovian Decision Process (MDP).
This approach has been validated in  [21]. MILP will be used to get out the optimal results and then DNN will be used to learn these obtained results. The MILP process is shown in the first part of Fig. 3 , it is based on the positions databases of buses and taxis in the city of Rome, the output is the selected IRS-bus dataset. This output data set will be fed to DNN for learning as will be described later in the performance evaluation section.
C. Encoder & Decoder Recurrent Neural Network(LSTM) The DNN consists of an encoder and a decoder network as shown in Fig. 3. This approach is intended for natural language processing, but we have adapted its inputs to match our MILP results as validated in a previous work  [22]. These two LSTM networks work in symbiosis to compress the input data and to extend the output again. The encoder has the same MILP input which is the positions (latitude and longitude) of taxis and buses. The encoder output is fed to the decoder along with the shifted labelled data. The output corresponds to the optimally computed IRS-bus list for MILP shifted by one. The final labels consist of the IRSbus's list. The training process uses MILP inputs and MILP results. Here numbers of optimal IRSs results from MILP vary each timestep and hence we need a more sofisticated LSTM architecture using the encoder decoder one.
VI. Performance Evaluation A. DNN with LSTM Evaluation In this subsection, the performance of the DNN according to its architecture of Fig. 3  will be validated. As mentioned before, the input of the DNN is the taxis and buses positions and the output is the optimal IRS-buses positions dataset. The encoders and decoders family has the ability to handle inputs and outputs of different sizes and with different semantics as well as handle complex tasks. Therefore, it is proposed to be used in the learning process over normal LSTM as it gives higher learning speed and performance. It should be noted that, in our case, the output is completely decorrelated from the input (taxi positions versus IRS ones). Additionally, the input size is different from the output size. The performance of the DNN architecture is studied for different sizes of neural networks and meta parameters.
The learning time and mean square error (MSE) are also studied based on using the LSTM and gated recurrent unit (GRU). The simulation code is built on Python (3. 8) and two PCs of CPUs i7-10700 CPU @ 2. 90GHz and 64GB RAM and i7-10750H CPU @ 2. 60GHz and 32GB RAM. Dateset used of mobile taxis and public transit buses of Rome city (Italy)  [6]  and  [7]  are used for validating the performance of the proposed models. These traces are constrained to the city center without losing the generality of the proposed models. The duration of taxi dataset is 60 days with GPS latitude and longitude coordinates recorded along with time. We have a large number of records for each taxi with a time-varying accuracy starting from one second time period. According to the taxi data set, taxi activity throughout the day was studied to determine the best time period for the simulation.
The performance of DNN is studied in terms of Mean Square Error (MSE). The batch size changes in steps 3, 6, 9 and 12 which corresponds to number of samples. In addition, since a DNN can consist of a different number of neurons, we studied its performance based on changing the number of DNN used neurons in steps 64, 128, and 256. As shown in Fig. 6 , Fig. 7 , and Fig. 8 , the MSE changes with the change in the number of neurons. It is obvious that MSE decreases as neurons of the same batch size increase. Furthermore, the effect on MSE is shown for both 100 and 200 IRS for better a conclusion. In order to compare the effect of the training dataset length on the MSE, Fig. 9  shows that 4, 10, and 16 days dataset are not enough to determine the effect of the training dataset. Therefore, there is a need to train the model using several larger datasets to determine its effect but with a penalty of very long processing time that can last for days/weeks and requires superior server hardware specifications.
for the same number of neurons, the MSE changes without a specific pattern. Therefore, we can conclude that there is no obvious impact on the MSE due to batch size change. Conversely, when looking at the subfigures vertically of the three columns (a, d, g), (b, e, h), and (c, f, i) the MSE improves as the number of neurons increases. CPLEX evaluation time is a very critical parameter. Table  2  shows the difference simulation times of 10 days dataset for using both CPLEX and DNN. As shown in the table, the time taken by DNN is much less than the time taken by the CPLEX. As it can be seen, MILP optimization of 10 days dataset takes simulation run time of several days on the mentioned computer. Thus, this is a main repressive issue to use MILP for such dynamic problems. According to the high processing time difference between both methods, DNN is preferred.
B. Markov Decision Process-Based Model Evaluation To validate our MDP algorithm, we tested it on the same real vehicles mobility dataset  [6]  and buses dataset provided by Rome city  [7]  as in the previous section. Before beginning the discussion of model evaluation, let us recall that the term platoon will be used to refer to continuity of contact with the IRS-bus. show the average platoon duration in function of the coverage radius ρ, with a perfect predictor and with the neural network based one. As expected, the strategy Q is the worst one: the taxis have the IRS they are connected to which change almost all the time. Strategy Q'A is better but still bad, although it is of course better in the case of the perfect predictor compared to the neural one. It is interesting to observe that both strategies Q'B and Q" behave more or less the same.