dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular dynamics (MD) simulations. The simulations were carried out with AMBER 18 package  (Case et al. , 2018)  implemented with ff14SB  (Maier et al. , 2015)  and GAFF  (Wang et al. , 2004)  force fields. The structure of CRM197 was obtained from the protein data bank web site (PDB id: 4AE0). The parameters and charges for the unnatural Tn and the corresponding linker were generated with the antechamber module of AMBER, using GAFF force field and AM1-BCC method for charges  (Jakalian et al. , 2002). The conjugated (or compound 1) was immersed in a water box with a 10 Å buffer of TIP3P water molecules  (Kiyohara et al. , 1998). If required, the system was neutralized by adding explicit counter ions (Na + ). A two-stage geometry optimization approach was performed. The first stage minimizes only the positions of solvent molecules and the second stage is an unrestrained minimization of all the atoms.
1 M ethanolamine-HCl (pH 8. 0). The levels of immobilization were in the range of 3000-5000 resonance units (RU). Flow cell 1 treated as a flow cell 2 (amine coupling procedure) without protein was used as a reference. Prior to use, 50 mM stock solutions of mime[4]CRM were diluted to the final concentration in the running buffer. Typically, a series of different compounds was injected onto the sensor chip a flow rate of 30 µl/min for a period of 1min followed by a dissociation period of 1 min. No regeneration was needed. The concentrations used for affinity measurements were in the range of 0. 05-1. 5 mM. Sensogram data were double-referenced using the Biaevaluation X-100 software (Biacore, GE). The experimental data of affinity measurements were fitted to a one site-specific model binding using Prism software (see also Fig.
DCs phenotyping. For the phenotype analysis both active and control DCs were incubated with mAbs for 30 min and washed with PBS containing 0. 1% bovine serum albumin (BSA) and 0. 1% sodium azide. DCs were labeled with mAbs for CD80 FITC, CD83 PE, CD86 APC (BD Pharmingen, San Jose, CA, USA). Samples were analyzed by FACS Canto II cytometer (Becton-Dickinson, San Jose, CA, USA) using the DIVA software. The error bars are from triplicates of the analysis for one donor (SpSS version 24 sofware).
For tumor growth curve, the total flux of photons (photons per sec) within each region of interest (ROI) from bioluminescence image were quantified using the Living Images Software Package 3. 2 (Xenogen-Perkin Elmer) (see also Table  S1 ). Five-week old female BALB/c mice were purchased from Envigo (Italy). Mice were housed in individually ventilated cages in an experimental room. The animals were maintained under standard conditions of light (12 h-light/12 h-dark), temperature (23°-25°C), humidity (50 to 60%) and rodent chow and water ad libitum. Survival analysis was performed using the Kaplan.
Statistical analyses for quantitative Immunofluorescences. Cell counting on immunofluorescence staining was performed using ImageJ software (version 1. 52r, 26 October 2019; https://imagej. nih. gov/ij/index. html). More in details, we used the Cell Counter tool (from ImageJ software) to score the number of DAPI positive cells (blue nuclear staining) from three different images per tissue sample previously acquired with Leica TCS SP5 Confocal Laser Scanning Microscope with the 63× oil immersion objective. Then, the same Cell Counter tool was used to score (i) CD4 positive cells (green plasma membrane staining), (ii) FOXP3 positive cells (red nuclear staining), (iii) TCR positive cells (red plasma membrane staining), (iv) CD11c positive cells (red staining plasma membrane staining), (v) PD1 positive cells (red plasma membrane staining), (vi) CD68 positive cells (red plasma membrane staining), (vii) CD163 positive cells (red or green plasma membrane staining), F4/80 positive cells (green plasma membrane staining) per slice (by clicking on the marker feature in the image).
ACKNOWLEDGMENTS Authors thank  Dr. F. P. Pennino  ( University of Naples ) for his initial contribution to the animal settings,  Dr. X. Ferhati ,  G. Salerno , and  Ms. A. Mazzara  ( University of Florence ) for initial contribution to CRM glycosylation. We thank  MIUR-Italy (Progetto Dipartimenti di Eccellenza   2018-2022  allocated to Dept. of Chemistry and Prin 2015),  CISM  ( University of Florence ) for  MALDI-MS, CEINGE  for supporting the use of mice facility and animal regulation study, PON  01-02388/1   2007-2013 , and  POR Rete delle Tecnologie in Campania Movie. I. A. B. thanks the Asociacion Espan ˜ola Contra el Cancer en  La Rioja  for a grant. F. C. was supported by  Ministerio de Ciencia, Innovacion y Universidades  (project  RTI2018-099592-B-C21 ).
75 U Biotaq DNA polymerase (Bioline). In a second step, a unique combination of tagged primers (forward and reverse) was added to each sample. Finally, 1 µL of template DNA (25 ng µL -1 ) was added. A negative control was included and the PCR program was as follows: 95 • C -2 min, 30 × (94 • C -30 s, 58 • C -30 s, 72 • C -30 s), with a final extension step at 72 • C for 5 min. Agarose gel analysis was performed for verification of the PCR products. When samples showed a non-specific band (approximately 550 bp) close to the target band (approximately 450 bp), the target bands were then captured using Pippin prep system (sage science) following the manufacturer's instructions. Then these captured DNA were checked with TapeStation (Agilent 2200) system for size and quality assessment following the manufacturer's instructions.
Bioinformatics Pipeline Two files of raw data (5 -3 and 3 -5 ) in fastq format were received from the sequencing platform. Files were processed using the pipeline developed by Frederic Mahé known as "Fred's metabarcoding pipeline" found at https://github. com/fredericmahe/swarm/wiki/Fred's-metabarcoding-pipeline. It combines programs such as Vsearch  (Rognes et al. , 2016) , Cutadapt  (Martin, 2011) , and Swarm  (Mahé et al. , 2015). All default parameters of the pipeline were left unchanged except when mentioned. Briefly, the pipeline starts with merging reads using Vsearch. Next, Cutadapt is used to demultiplex the sequences. Here, each sequence is assigned to its sample in an individual file by its tag. Primers are also removed and sequence containing ambiguous bases are discarded. In each file, sequences are dereplicated using Vsearch.
Then all files are assembled as one file and dereplicated in the process. The Swarm algorithm was then used to cluster the sequences. In this step, the "d" parameter (i. e. , the number of different nucleotides between sequences) was changed from 1 to 13 in order to be close to the identity threshold of 97% which define same species  (Schloss and Handelsman, 2005). Next, de novo chimera detection was applied to representative sequences of each cluster using Vsearch. The representative sequences were taxonomically assigned to a reference database downloaded from arb-SILVA (release number 132;  Quast et al. , 2013)  and prepared as required. Next, a python script from the pipeline was used to build the final OTU table. The OTU table was then filtered with three different filters. The first one discarded all sequences flagged as chimera.
Statistical Analysis Statistical analyses and plots were performed using R, version 3. 5. 0 (R Core Team, 2018) and ggplot2  (Wickham et al. , 2018). The OTU table was transformed to relative abundance using the "decostand" function from the vegan package  (Oksanen et al. , 2019). Alpha diversity indices (i. e. , Shannon and Pielou) were calculated with the OTUtable package  (Linz, 2018). Simpson and inverse Simpson were calculated using the vegan package  (Oksanen et al. , 2019). The indexes comparison for each condition (Site, Month, Depth, and Filter) was performed using the Kruskal-Wallis test. When the p-value of Kruskal-Wallis test was inferior to 0. 05 (alpha), a Dunn test was performed with Bonferroni correction  (Dinno, 2017). The richness plot found in the Supplementary Figure  2  was made using the "rarecurve" function in vegan.
An NMDS to illustrate beta diversity was computed using the "metaMDS" function from vegan and also the goeveg package  (Goral and Schellenberg, 2017). A Simper test was also performed with vegan to access the contribution of each OTU to the observed dissimilarity between samples. Regarding environmental variables, they were only available for pt4 (SHL2). OTUs found here were extracted and if an OTU had no read in a site, we removed it from the final OTU table. The data was also transformed as log(1 + x) in order to stabilize the OTU dataset. Then, another NMDS with environmental variables and a CCA were performed on that table following the online tutorial of Umer Zeeshan Ijaz  (Torondel et al. , 2016)  found at http://userweb. eng. gla. ac. uk/umer. ijaz/ bioinformatics/ecological. html. Co-occurrence network analysis was performed on all sites following Ju Feng's R and python scripts  (Ju et al.
, 2014; Ju and Zhang, 2015; Hu et al. , 2017)  found at https://github. com/RichieJu520/Co-occurrence_ Network_Analysis. A bacterial co-occurrence network was built with bacterial orders including Bdellovibrionales and Bacteriovoracales. The network was composed of 92 nodes (bacterial orders) and 461 undirected edges or connections. The bacterial OTU table used for the network was assigned and curated to the order level. Only positive interactions between community members were considered. The Spearman correlation and p-value cutoffs were set to 0. 6 and 0. 01 in the script. As for the C-score of the network, it was performed with the R package EcoSimR  (Gotelli et al. , 2015)  based on a presence/absence OTU matrix. The visualization and customization of the network was done with Gephi software  (Bastian et al.
Phylogeny On one hand, 18 BALOs reference sequences (including type species) of the 16S rRNA (6 for the genus Bdellovibrio, 5 for Peredibacter, 5 for Bacteriovorax, and 2 for Micavibrio) were downloaded from NCBI  (Benson et al. , 2013 , Supplementary Table  5 ). On the other hand, the assigned OTU sequences of BALOs we found (31 for the family Bdellovibrionaceae, 8 for Peredibacteraceae, 6 for Bacteriovoracaceae, and 9 for the order Micavibrionales) were used to construct the phylogenetic tree. All sequences were aligned using the program MUSCLE alignment  (Edgar, 2004)  in MEGA7  (Kumar et al. , 2016). The ends of all sequences were trimmed at 5 and 3 , making reference and OTU sequences of equal length. Next, the alignment was improved using Gblocks 0. 91b  (Castresana, 2000)  with "Minimum Length of a block" set to 5.
ModelGenerator v. 85  (Keane et al. , 2006)  was used to select the best nucleotide substitution model (GTR + I + G) under corrected Akaike information criterion (AICc)  (Akaike, 1973)  with 4 discrete gamma categories. The tree was built using the Maximum likelihood method with PhyML 3. 1  (Guindon et al. , 2010)  with 100 bootstrap replicates. A Bayesian tree was also constructed using MrBayes 3. 2. 7a (Ronquist et al. , 2012)  program with 500,000 generations and a burn-in value of 25%.
It is worth mentioning  that the median percentage of identity between representative sequences and database (arb-SILVA) sequences for taxonomic assignment was about 97. 4%. The lowest and highest identity percentages were 50. 3 and 100% respectively, with an average value of 94. 8%. Among (facultative and obligate) predatory bacteria, BALOs constituted the group with the largest number of reads, representing only 0. 61% of total bacteria reads. However, the Myxococcales order had the largest number of OTUs, i. e. , 240 OTUs, representing 3% of total bacterial OTUs. BALOs were in second position with 130 OTUs, representing 1. 63% of total bacteria OTUs (Table  1 ). It is noteworthy here that the detailed analysis of the OTUs revealed the presence of Halobacteriovorax (i. e. , a marine group) but after a more in-depth inspection of the sequences using NCBI-BLAST, it was clear that these sequences were wrongly assigned.
DATA AVAILABILITY STATEMENT Bdellovibrio and like organisms generated sequences used to construct the phylogenetic tree can be found under NCBI accession numbers: MN617094, MN617095, MN617096, MN617097, MN617098, MN617099, MN617100, MN617101, MN617102, MN617103, MN617104, MN617105, MN617106, MN617107, MN617108, MN617109, MN617110, MN617111, MN617112, MN617113, MN617114, MN617115, MN617116, MN617117, MN617118, MN617119, MN617120,.
In CAPRISA 012B, CAP256V2LS will be assessed alone and in combination with VRC07-523LS and/or PGT121. VRC07-523LS targets the HIV-1 Envelope CD4 binding site and PGT121 targets the V3 glycan-dependent epitope region of the HIV Envelope protein. Given the vast genetic diversity of HIV-1, the use of multiple bNAbs may be required to ensure adequate coverage of circulating strains. Recent clinical trial data demonstrate that VRC07-523LS is safe with a half-life of 38 days after IV administration and 33 days after SC administration. 30  Open access clinical trial data of PGT121 also demonstrate safety with a half-life of 22 days. 31 his trial is also one of the first to assess the use of a recombinant human hyaluronidase (rHuPH20) together with an anti-HIV monoclonal antibody. 32 rHuPH20 is the active ingredient of the investigational ENHANZE Drug Product (EDP) and optimises the SC delivery of coadministered therapeutics by depolymerising hyaluronan in the extracellular matrix of the SC space that normally serves to restrict increased flow volumes.
Sample size calculation The main objective of this study is to assess the safety, tolerability and PK of CAP256V2LS; hence, the ability of the study to detect SAEs is key. The probability of detecting no SAE, at least one, or at least two SAEs at a specified Open access true event rate will be calculated. These probabilities highlight the likelihood of the study to detect either rare or common AEs. In addition, the 95% CI for the true event rate was calculated. Currently, limited safety data are available to guide the estimation of the true event rate that might be observed in the study. In the absence of available AE rates for CAP256V2LS alone or in combination with other bNAbs, a range of hypothesised event rates to calculate the probability of observing no events, at least one event, or at least two events were used.
Randomisation An unblinded statistician who is not involved in study conduct will be responsible for generating the randomisation sequence (for groups 1 c, 1d, 3a to 3 c and 4a) using SAS V. 9. 4 software or latest. Participants will be assigned to unique participant identification numbers stratified by HIV status and group number. Sequentially numbered, sealed, opaque envelopes containing the group number and envelope number or treatment code (for use by the unblinded pharmacist only) will be provided to the study coordinator, to be opened once a participant has been deemed eligible and is ready to be enrolled into the study. The pharmacist will store envelopes in a secured location within the research pharmacy with access restricted to delegated study pharmacists only. The study pharmacist will also receive a randomisation list consisting of the unique three-digit envelope number and study group with the corresponding study drug or placebo and relevant dosage from the unblinded statistician.
Enrolment Enrolment will take place within 56 days of screening. After the informed consent is obtained and eligibility criteria are met, the participant will be allocated to a study group (table  2 ). At this visit, vital signs will be recorded, a targeted physical examination will be conducted and all laboratory results from the screening visit will be reviewed. For women of childbearing potential, a negative pregnancy test on that day must be obtained prior to product administration. Prior to the infusion/injection, the study team must ensure that the participant is eligible to receive study product. All study product administrations will be completed according to the assigned group and may be via an IV infusion or SC injection. Once safety has been established in the first participants, enrolment into the next groups as well as dose escalation will take place in a sequential manner, following review at each step of safety data from the preceding groups.
Safety monitoring Safety monitoring includes internal monitoring by the study team, the protocol safety review team (PSRT) and the data safety monitoring board (DSMB), as well as external monitoring including audits. Safety reporting of SAEs, AEs and other important reportable events will be the responsibility of the entire study team. In addition, the study statistician will prepare routine study safety progress reports, which include reports of AEs experienced by study participants (blinded to treatment assignment), for review by the PSRT. The PSRT will review the clinical safety data on a weekly basis via electronic distribution of reports and will have face-to-face meetings as required. The PSRT will be responsible for decisions related to participant safety. In addition, an independent DSMB will meet in-person and/or via teleconference semiannually and review the study data and study conduct.
Tolerability evaluation will be mostly descriptive and consist of solicited AEs that occur within 1 hour following study product administration and reasons for any withdrawal or discontinuation based on participant discomfort. This early assessment of tolerability of the monoclonal antibodies will inform which parameters should be solicited or routinely assessed to further characterise the tolerability profile in a larger number of participants. Where appropriate, some of the data will be presented graphically. The analysis will be carried out using either SAS V. 9. 4 or higher or R. PK analysis PK disposition of CAP256V2LS alone and in combination with VRC07-523LS and PGT121 will be evaluated in this study and PK will be assessed via dose subgroups and route of administration. Sixteen participants will receive CAP256V2LS by the IV route.
The two lower dose levels will be administered to HIV-negative participants and the highest dose level will be administered to HIV-positive participants. These PK data will serve to characterise dose effects on CAP256V2LS clearance, volumes of distribution and elimination half-life. The PK data across group 1 will also enable a preliminary evaluation of the potential impact of HIV status on CAP256V2LS PK. The PK results from the IV administration groups will be compared with the data from the SC administration groups in order to estimate bioavailability. A total of 24 participants will receive CAP256V2LS by the SC route, either as a single dose or a repeat dose. A subset of participants will receive CAP256V2LS with SC hyaluronidase to allow the larger doses of antibody to be administered SC.
These data will allow concentration profiles, bioavailability and absorption patterns to be established. The impact of repeat CAP256V2LS SC administration on PK will also be determined. The PK data from the combination groups (groups 3 and 4) will be compared with those from group 2 to determine if coadministration of VRC07-523LS or PGT121 affects CAP256V2LS PK. In addition, the VRC07-523LS and PGT121 PK results will be compared with findings from PK studies in CAPRISA 012A and reported in the literature to establish whether CAP256V2LS impacts the PK of VRC07-523LS and PGT121.
Data management Data for the CAPRISA 012B study will be collected on case report forms (CRFs) designed specifically to address the protocol requirements. Data will be managed by the CAPRISA Data Management department, using DFdiscover (DF/Net Research, Inc), software specifically designed for clinical trial data management. The site will record data on paper CRFs that will be directly captured onto the DFdiscover system and validated by the data management staff. All source documents will be kept in the participants' study files and medical charts at the clinical research site. All original CRFs and study-related documents will be securely stored at the site, during the study and after study completion.
Validation of results by PCR and DNA sequencing Microfluidic real-time PCR results were confirmed through conventional and nested PCR using different primers (see Additional file 1: Table  S1 ) than those of the BioMark TM system. Amplicons were sequenced by Eurofins MWG Operon (Ebersberg, Germany) and assembled using the Geneious Prime software (Biomatters Ltd, Auckland, New Zealand). An online BLAST (National Center for Biotechnology Information) was used to identify the sequenced organisms.
Phylogenetic analyses The partial nucleotide sequences of microorganisms obtained for 18S rRNA, 16S rRNA and citrate synthase (gltA) genes were used to assess the genetic relationships with those of species of Babesia/Theileria, Ehrlichia and Rickettsia, respectively. Reference sequences were downloaded for each pathogen from GenBank and aligned separately (Babesia/Theileria over 582 bp; Ehrlichia over 618 bp; Rickettsia over 375 bp) with sequences obtained in this study, using ClustalW  [44]  in the software MEGA v7. 00  [45]. The best-fit evolutionary models for each dataset were selected based on Corrected Akaikeʼs information criterion (cAIC) and Bayesian information criterion (BIC) using MEGA. Phylogenetic trees were constructed using the Neighbour-joining (NJ) and Maximum Likelihood (ML) methods in MEGA and Bayesian Inference (BI) method using Mr Bayes in Geneious Prime  [46].
Each Bayesian analysis was run over 20,000,00 generations (ngen = 20,000,00) with two runs and every 400 th tree was saved (samplefreq = 400). For the NJ tree estimations, evolutionary distances were computed using the p-distance method whereas for the ML method, initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the Maximum Composite Likelihood (MCL) approach, and then selecting the topology with superior log-likelihood value. All positions containing gaps and missing data were eliminated. Bootstrapping method (10,000 replicates) was used to assess the reliability of internal branches and all trees were visualized and edited using MEGA. Plasmodium falciparum (GenBank: M19172), A.
Statistical analyses Chi-square and Fisher's exact tests were used to evaluate the overall prevalence of microorganisms and to compare their prevalence in different AEZs. Data were analyzed using GraphPad 5 Prism program (GraphPad Software Inc. , La Jolla, CA, USA). The multiple correspondence analysis (MCA) was used to analyze the pattern of the association between infections (co-infections) and their distribution across provinces, districts and bovine host species. The inertia values were calculated by standard 'Burt matrix' method. The analyses were performed using the software, Statgraphics Centurion v. 16. 1. 03 (StatPoint technologies Inc, Warrenton, VA).
Genetic relationship of DNA sequences of selected microorganisms Three different phylogenetic methods (BI, ML and NJ) were used to analyze (separately) genetic relationships of 16S rRNA gene sequences of Ehrlichia spp. , 18S rRNA gene sequences of Babesia and Theileria spp. and gltA sequences of Rickettsia spp. with those of respective previously published sequences. The topologies of all three trees for each target organism were similar; hence, only NJ trees are provided herein (Figs. 4, 5, 6). Two unique 16S rRNA sequences of Ehrlichia spp. (GenBank: MN726921 and MN726922) detected herein grouped with those previously published sequences from Thailand (GenBank:AF497581), Tibet, China (GenBank:AF414399) and Multan, Pakistan (GenBank:MH250197) (Fig. 4 ), B (t = 1) C (t = 25) B (t = 11) C (t = 3) B (t = 12) C (t = 13) Borrelia spp.