dataesr/scientific-paragraphs-categorization huggingface dataset:
The results obtained for the larger groups are homogeneous across the different models (Figure  S11 ). For Firmicutes, which is the only phylum with some architectural diversity in our dataset, two of the five models (E and H2) do not completely settle on an LCA monoderm with a thick peptidoglycan, and instead do not dismiss an LCA without peptidoglycan (17. 6% and 30. 1%, respectively; Table  1 ). Finally, a comparison of the fit of the five models using Bayes Factors (Table  2 ) showed that model R1 was the best, followed by models R2, H1, E, and finally H2. Therefore, the two models that do not fully agree with the others about the peptidoglycan trait are also those that are deemed less fit by Bayes Factors (E and H2). 1  featuring the cell-wall architecture inferred for selected last common ancestors among Bacteria.
Data Availability Statement: The datasets generated and analysed for this study can be found in the FigShare repository available here: https://doi. org/10. 6084/m9. figshare. 14932386. v2 (16  February 2022). Similarly, the database schema and corresponding table dump are available at: https://doi. org/10. 6084/m9. figshare. 17102651. v1 (16 February 2022).
The resulting signal is conveyed to the receiver by cable in order to have a precise tuning of the LOS/NLOS communication paths in terms of amplitude and phase. The second method consists in the implementation of an over-theair transmission scenario in the indoor environment. In order to vary the characteristics of the propagation channel, electromagnetic absorbers are inserted between the emitting and the receiving antennas to modify the power level of the LOS signal. A channel sounding method is also applied to evaluate the characteristics of the propagation channel. The performance of the SM system in the practical implementation is compared to the simulation results. A comprehensive presentation of the simulation framework used by the authors is given in  [21]. The main contributions in this work are summarized as follows: • We conduct experiments on SM systems based on the National Instruments (NI) vector signal transceiver (VST) and LabVIEW programming environment, with the construction of the complete experimental platform and the synchronization of each PXI VST module.
II. TRANSMITTING AND RECEIVING PROCESSES OF THE SM SYSTEM The experiment setup can be divided into software and hardware parts, from simulation level and from physical level, respectively. The block functions and the transmission chain are given in FIGURE  1. The SM system is implemented by using four NI PXIe-5646 VST modules  [27]  which are programmed by using NI LabVIEW framework. The VST as a vector signal transceiver is able to transmit and to receive at the same time and at the same frequency. Here, to realize a 2 × 2 SM system, two VST modules are supposed to work for the emission and the remaining two modules are employed for the reception. The four VST modules are integrated into a PXIe chassis which can guarantee the symbol synchronization by configuring the synchronization function.
The VST modules are driven by LabVIEW which is coupled with Matlab software. This latter software is employed to implement the complex signal processing algorithms (i. e. maximum likelihood (ML) optimal detection algorithm, least-square (LS) algorithm) in the step of the SM modulation, channel estimation and SM demodulation. A. TRANSMITTING PART FIGURE 2 outlines the step-by-step configuration of the transmitting part. The input data is a random bitstream which will be processed by the SM mapping algorithm. In order to configure the parameters (carrier frequency, modulation type, power level, etc. ) of the VST modules, NI-RFSG libraries provided by LabVIEW software are employed. More precisely, NI-RFSG is an instrument driver which is able to configure and operate hardware, to program and generate signals, and to perform modulation tasks using Lab-VIEW VIs functions  [28].
1) CONFIGURATION Once the NI-RFSG session is opened, the carrier frequency and the output power level are configured. The carrier frequency is set up at 868 MHz (inside one of the European ISM band), which is chosen in order to comply with our hardware constraints (VST platform, frequency synthesizer, phase coherent coaxial cables, antennas, etc. ) and simulation capabilities. The output power level can be adjusted as required in the limit range of the VST and the regulations which will be introduced in the following sections. We then configure the generation mode and the reference clock source. The RF signal generator has also been configured to be able to generate an arbitrary waveform. The reference clock source must be external to the device and we apply the sampled reference clock (SRC) for the synchronization of four VST modules.
4) SM MODULATION As mentioned before, the input will be processed by the SM mapping algorithm which is given in FIGURE  3. In our case, we have two transmitting antennas (N t = 2) and QPSK (M = 4) is chosen as the modulation method. As there are four possible symbols for the QPSK modulation, each possible 3-digit (log 2 (N t ) + log 2 (M ) = 3) binary represents one possible transmitting situation of the SM system. Moreover, P (P = N t M = 8) is given to denote the number of possible configurations of the SM. As a header, additional 8 (P = N t M = 8) frames of data are added before the random input sequence. This header is employed in order to estimate the propagation channel characteristics (i. e. CSI), which is used at the receiver side for SM demodulation. As shown in the right part of FIGURE  2 , the input signals for the two transmitting VST modules need to be prepared.
4) QPSK DEMODULATION The demodulation as the core step in the RFSA module, Matlab software is introduced to perform the LS algorithm and the ML algorithm. The LS algorithm is a traditional and effective method for CSI estimation, which has already been demonstrated in  [21]  for the simulation implementation of the SM system. The constellation of the received symbols is shown in FIGURE  5. We suppose here Tx1 and Tx2 as the two transmitting antennas. One can remark from the FIGURE  5  that the two QPSK constellations of the signal transmitted by Tx1 and Tx2 are different in terms of amplitude, and a slight rotation between the two constellations is also visible, since the transmission channels between the two transmitting antennas and the receiving ones are quite different. IQ data waveform for the constellation at the receiving end is shown in FIGURE  6.
III. HARDWARE SETUP AND PROPAGATION ENVIRONMENT A. HARDWARE SETUP In general, NI PXI platform is designed for measurement and automation applications. It supports high-performance, modular instruments and I/O modules that enable specialized synchronization and critical software functions for test and measurement applications, from hardware validation to production self-testing. As shown in FIGURE  1 , the PXI chassis as the hardware assumes the role of transmitting the modulated signals and receiving the signals that have passed though the propagation environment. For our experiments, the NI-PXIe-1085 chassis is equipped with four VST modules of the type PXIe-5646R. FIGURE  9  shows a photograph of the PXIe-5646R modules fitting into the NI-PXIe-1085 chassis for the application of our SM system.
IV. PERFORMANCE EVALUATION OF THE SM SYSTEM In this section, we discuss the BER performance of the mentioned SM system under the LOS/NLOS scenarios. The two propagation environment scenarios previously described are considered. One is the controlled propagation environment and the other is the realistic channel. Due to the hardware limitation, a simple 2×2 SM scenario has been implemented. To compare with the experimental approaches, a simulator of the SM scheme was built in  [21]  with the help of the Keysight ADS and Matlab software. Similar to the experimental implementation, in the simulation setup, a time varying  CSI detector is applied to estimate the simulated propagation environment, and 10 6 input binary values are transmitted in order to estimate the BER. The parameters of the experimental system are gathered in TABLE  2.
V. CONCLUSION In this paper, the performance of a SM system has been analyzed, for the first time, under mixed LOS/NLOS conditions with the help of an experimental framework based on NI VST modules. This study compensated for the lack of experiments on SM systems in realistic propagation channels. The software framework for the generation and the analysis of the SM signals has been presented. In particular, a CSI detector for the channel estimation was designed instead of assuming the availability of the perfect CSI at the receiver. The index of transmitting antenna and symbol are detected respectively by employing a low complexity ML detection method. Moreover, an extensive analysis of the experimental setup was presented and two models of the propagation environments (controlled one and wireless one) were proposed.
1C ). Whereas in PilB the extra module corresponds to a vWA domain (IPR036465)  6  , in PilC it belongs to the concanavalin A-like lectin/glucanase domain superfamily (SSF49899). This superfamily is composed of very diverse proteins in the three domains of life, ranging from legume lectins (e. g. , carbohydrate-binding proteins, but they differ in their ligand specificities. These observations raise the possibility that PilC is a modular pilin with a lectin module promoting T4P-mediated adhesion of S. sanguinis to glycans. PilA is structurally similar to a widely conserved minor pilin from a complex of minor pilins found at the tip of many T4F To better understand the role of PilA, we solved its 3D structure by X-ray crystallography. We produced a 15. 7 kDa recombinant protein in Escherichia coli, in which the N-terminal 32 residues of PilA (encompassing the leader peptide and hydrophobic α1N) (Fig.
1B ) were replaced by a hexahistidine tag (6His). α1N truncation promotes pilin solubility without having a structural impact on the rest of the protein  37. We purified soluble 6His-PilA using a combination of affinity and sizeexclusion chromatography (SEC). The protein crystallised readily, and after optimising the best diffracting crystals, we collected a complete dataset in the space group P43212 (Table  1 ). After phase determination, using crystals produced in the presence of seleno-methionine (SeMet), we solved a 1. 77 Å resolution structure. As can be seen in Fig. 2A , PilA exhibits a typical pilin architecture, with a globular head consisting of a long N-terminal α1-helix packed against a β-sheet composed of five anti-parallel β-strands. Modelling the full-length PilA protein using AlphaFold 38 reveals a canonical lollipop shape  4  where the globular head is mounted onto a long α1-helix "stick" (Fig.
Crystal structures of PilC reveal a modular pilin To confirm the prediction that PilC might be a modular pilin, we solved its 3D structure by X-ray crystallography. Due to the instability of its pilin moiety (Fig. 5 ), we produced a 41. 4 kDa recombinant 6His-PilCΔpilin protein in E. coli, in which the pilin moiety was missing (Fig. 1C ). This protein crystallised readily, and we collected a complete dataset in the space group P3221 (Table  1 ). After phase determination using SeMet crystals, we solved a high-resolution structure at 1. 45 Å. Strikingly, while bioinformatics only predicted that the C-terminus of PilC corresponds to a lectin module, our structure reveals two distinct modules (Fig. 6A ). The first module adopts an Ig-like fold, consisting of two β-sheets packed against each other (Fig.
PilClectin and Gal-7, which display no sequence homology, superpose with an RMSD of 1. 95 Å (Fig. 6C ). This similarity is interesting because the ligand-binding ability of galectins is well-characterised. In the Gal-7 structure in complex with galactose (Fig. 6C ), the carbohydrate-recognition domain (CRD) forms a patch on the concave side of the protein  41. Since there is good structural homology in that part of PilClectin (Fig. 6C ), we speculate that the CRD in PilC might be on the concave side of PilClectin. To try to determine a PilC structure encompassing the pilin moiety as well, we expressed and crystallised the corresponding protein from another S. sanguinis strain SK36 (PilC SK36 ), in which the N-terminal 33 residues were replaced by 6His. We collected a complete dataset in the space group P212121 (Table  1 ) and solved a 1.
PilC binds two types of glycans prevalent in the human glycome The presence of a lectin domain suggests that PilC might be an adhesin recognising host glycans. We therefore investigated the glycan binding ability and specificity of PilC using a glycan microarray with oligosaccharide probes immobilised using the neoglycolipid (NGL) technology  42. Microarray analysis with 6His-PilC revealed that it oligosaccharide probes (Supplementary dataset 1) -corroborating its lectin activity (Fig. 7A ). Two major types of negatively charged glycans were bound: sialylated glycans and sulphated glycosaminoglycans (GAG). Most of the sialylated glycan ligands display a α2-3-sialyl linkage and terminate with 3′-sialyllactose (3'-SL, NeuAc(α2-3)Gal(β1-4)Glc) or 3′-sialyl-N-acetyllactosamine (3'-SLN, NeuAc(α2-3)Gal(β1-4)GlcNAc), which are similar trisaccharides.
9C ). We then modelled the full-length PilACB complex using AlphaFold. Since the prediction for full-length PilC is of poor quality, especially for the Ig fold module, we produced a full-length PilC model by pasting the predicted PilCpilin onto our crystal structure where the Ig fold module is well resolved. The model suggests that the two modular pilins PilB and PilC can be accommodated in the same complex despite their bulky extra domains (Fig. 9D ). Some level of movement for the extra modules in PilB and PilC is expected because the short loops that connect them to their respective pilin moieties are likely to be flexible. Although PilA is predicted to be added first, the extra modules in PilB and PilC are capping the complex and look like "open wings". Due to this peculiar architecture, the PilACB complex could only be accommodated at the tip of the T4P, as previously suggested for HIJK  25.
PilA thus acts as an intra-pilus chaperone, protecting PilC from proteolysis by perhaps participating in its proper folding. In addition, it is likely that PilA also participates in the proper localisation of PilC at the pilus tip. We previously showed that PilB displays a modular architecture with a bulky functional module grafted onto a pilin moiety  6. The vWA (IPR036465) module in PilB makes it a bona fide adhesin, playing a key role in S. sanguinis adhesion to host cells by binding protein ligands such as fibronectin and fibrinogen 6. Since S. sanguinis T4P are adhesion being involved in IE. Here, we show that PilC is also a modular pilin with two extra modules, an Ig-like fold (not revealed by bioinformatics) and a ConA-like lectin/glucanase module (SSF49899). While the function of the Ig-like module remains unclear, we showed that the ConA-like module in PilC is a bona fide lectin.
SEC-MALS SEC-MALS was performed using an ÄKTA Prime system with a S200 10/300 GL column (GE Healthcare) and a MALS detector (Wyatt). The column and the system were equilibrated in buffer (50 mM HEPES pH 7. 5, 150 mM NaCl) for 48-72 h to minimise and stabilise the light scattering and background noise. Protein samples of 150 µl (45 µM PilA, 50 µM PilC, 40 µM PilA-PilC) were loaded onto the column at 0. 2 ml/min. The UV, light scattering and refractive index signals were analysed with the ASTRA chromatography software.
ITC ITC was used to study the interaction between the minor pilins PilA and PilC, as well as the binding of PilCΔpilin to sugar ligands 3'-SL and 3'-SLN. The experiments were performed on a MicroCal ITC200 Malvern machine at 20°C. Each experiment was repeated three times. Data analysis was performed on the Origin ITC200 software. Statistical analyses were performed with Prism (GraphPad Software). Comparisons were done by one-way ANOVA, followed by Dunnett's multiple comparison tests with 99 % confidence interval. An adjusted P value <0. 01 was considered statistically For measuring the affinity of the PilA-PilC interaction, the two purified proteins were buffer-exchanged into the same buffer (20 mM HEPES pH 7. 5, 150 mM NaCl). The sample cell was loaded with 20 µM PilC, while the syringe was filled with 200 µM PilA.
Glycan microarrays The binding specificities of 6His-PilC, 6His-PilCΔpilin and 6His-PilC SK36 -purified and concentrated to 1 mg/ml in 10 mM HEPES pH 7. 5, 150 mM NaCl, 5 mM CaCl2were analysed using a NGL-based microarray system  42. A previously described broad-spectrum microarray of 672 sequence-defined lipid-linked glycan probes was used  63. The list of glycan probes is given in the Supplementary dataset 1. Details of the preparation of the glycan probes and the generation of the microarrays are listed in Table  S3  in accordance with the MIRAGE guidelines (Minimum Information Required for A Glycomics Experiment)  64. The microarray analyses were performed essentially as described  65. In brief, after blocking of the slides for 1 h with HBS buffer (10 mM HEPES pH 7. 4, 150 mM NaCl) containing 1 % (w/v) BSA (Sigma), 0.
Bioinformatics and modelling Protein sequences were routinely analysed using DNA Strider 66. Prediction of protein domains was done by interrogating the InterPro database with InterProScan 67. Molecular visualisation of 3D structures was done using PyMOL (Schrödinger), which was used for generating the figures in this manuscript. The DALI server was used for comparing protein structures in 3D  68. Protein 3D structures were downloaded from the RCSB PDB server. The 3d-SS  69  server was used to superpose 3D protein structures with the STAMP algorithm  70. PDBePISA 71 was used for the exploration of macromolecular interfaces. Modelling was done using AlphaFold  38  and AlphaFold-Multimer 72 on Google Colab with default parameters. Arrowheads indicate the proteins that were produced and purified in this study, with/without the pilin moiety (black and green arrowheads, respectively).
Acknowledgements This work was funded by the  Medical Research Council  ( MR/P022197/1 ). The glycan microarray studies were performed in the Carbohydrate Microarray Facility at the  Glycosciences Laboratory , which is supported by  Wellcome Trust biomedical  resource grants ( 099197/Z/12/Z ,  108430/Z/15/Z , and  218304/Z/19/Z ) and partially by the  March of Dimes Prematurity research centre  grant ( 22-FY18-82 ). The sequencedefined glycan microarrays contain many saccharides provided by collaborators whom we thank, as well as members of the  Glycosciences Laboratory  for their contribution in the establishment of the NGL-based microarray system. We acknowledge the use of the crystallisation facility at  Imperial College London , which is supported by the  Biotechnology and Biological Sciences Research Council  ( BB/D524840/1 ) and  Wellcome Trust  ( 202926/Z/16/Z ).
Gene transcription levels were computed from aligned reads and statistically compared using the Cuffdiff (v0. 0. 7) tool  (Trapnell et al. , 2012)  implemented in a Galaxy pipeline (http://galaxyproject. org). Transcription level variations between exposed and nonexposed larvae and between adult females from exposed and nonexposed larvae were computed. Differential transcription analysis was performed on the genes showing a transcription level > 0. 5 FPKM in each condition pair across all replicates. Genes were considered significantly differentially transcribed between exposed and controls when the adjusted t-test p-value was lower than 0. 01 after multiple testing correction using the Benjamini-Hochberg method  (Benjamini and Hochberg, 1995; Trapnell et al. , 2013). For each condition, differentially transcribed genes were subjected to a Kegg pathway and Gene Ontology term (GO-term) enrichment analysis using the functional annotation tool DAVID (Database for Annotation, Visualization and Integrated Discovery, http://david.
, 2012). This software can be used to highlight homology and evolutionary relationships between sequences and thus, to select rRNA molecule regions specific to the digestive symbiont lineages. Literature data  (Behrens et al. , 2003)  were used to select 16S rRNA molecule regions known to be accessible for probe hybridization. First, the 2D conformation of the rRNA was used to select potential zones for probe hybridization and to assess the potential for co-hybridization between two probes. The potential accessible regions are chosen to avoid 3D in situ conformations of the rRNA molecule or regions showing interactions with ribosomal proteins. Then, the physical properties of the designed probes were estimated to avoid hairpin structure formation, cross-hybridization, self-dimer formation, and to check their melting temperatures (T M ), using the Geneious Primer Design tool and the Oligo Calc software  (Kibbe, 2007).
The clinical efficacy of NBTXR3 was demonstrated in patients with locally advanced Soft Tissue Sarcoma in the randomized controlled Phase II/ III Act. in. Sarc study (NCT02379845)  [18]. We also demonstrated the capacity of radiotherapy-activated NBTXR3 (NBTXR3 + RT) to significantly improve cGAS-STING pathway activation in the human colon cancer cell line HCT116  [19] , a cellular response important for the activation of antitumor immunity  [20]. In mice bearing mouse colon cancer CT26. WT tumors, NBTXR3 + RT triggered a potent anti-tumor CD8 + T cell immune response as well as a significant abscopal effect, drastically improving tumor control and survival  [21]. Recently, it has been demonstrated that NBTXR3 + RT combined with anti-PD-1 significantly enhanced local tumor control, abscopal effect, and survival of mice bearing lung tumors resistant to anti-PD-1 treatment  [22].
Statistical analysis Normality distribution of values was assessed by Shapiro-Wilk normality test. Immunohistochemistry (IHC) analyses were analyzed by Mann-Whitney test. For TCR repertoire, analysis with normal distribution was analyzed by one-way ANOVA while non-normal distribution was analyzed by Mann-Whitney test. A p value < 0. 05 was considered statistically significant. The GraphPad Prism 8 v. 8. 2. 1 software was used for graph plotting and biostatistics.
The detector was switched into excitation 260 nm and emission 315 nm at 25 min to test proline. Two solvents were utilized in this method: solvent A (0. 01 mol/L Na 2 HPO 4 with 0. 8% THF, adjusted to pH 7. 5 with H 3 PO 4 ) and solvent B (20% solvent A, 40% methanol, 40% acetonitrile). Gradient: 0 min, 0% B; 14 min, 40% B; 22 min, 55% B; 27 min, 100% B; 35 min, 100% B; 36 min, 0% B; with a flow rate of 1 mL/min. Chemstation (Agilent) chromatography data system was used for data analysis.
Statistical Analysis Statistical analysis was undertaken using IBM SPSS Statistics 22. The data were subjected to two-factor analyses (ANOVA) to partition the variance into the main effects and the interaction among them. In the case of significant interactions among factors, treatments were compared using the least significant difference (LSD) at the 5% level (p < 0. 05). The principal component analysis (PCA) was performed through MiniTAB16 (Minitab, LLC, State College, PA, USA).
Subject Recruitment Patients with biopsy proven PCa were recruited from the Winship Cancer Institute at Emory University from 18 April 2018 to 16 May 2019. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The recruitment protocol was approved by the Institutional Review Board (IRB) and complied with the Health Insurance Portability and Accountability Act (HIPAA). The data was collected as part of a feasibility trial for neuroendocrine prostate cancer imaging under the IRB title 'Molecular Imaging with [ 68 Ga]Ga-DOTATATE PET to Investigate Neuroendocrine Differentiation in Prostate Cancer Patients (IRB#99167)'.