dataesr/scientific-paragraphs-categorization huggingface dataset:
The input data is available in Table  S1. Then, three machine learning approaches (LDA, QDA and MLP) were developed using an arbitrary threshold of 1 µM, 10 µM and 100 µM to separate between active and inactive CES1 ligands. The performances of the models on a training set, cross validation and test sets are described in Table  1  and Figure  4. In general, all the models show a better AUC than the docking score only (between 10% and 20% higher). The AUC remains relatively high on the test set, demonstrating the robustness of the models. The best model was the neural network (MLP), followed by the QDA and LDA with a quite similar performance profile. In addition to cross-validation, to check for overfitting, the possibility of an outsized effect of liminal ligands was investigated by computing the performances of the classifiers while removing compounds with Ki within 5 µM of the 10 µM threshold from the dataset (i.
e. , removing compounds between 5 µM and 15 µM). For both procedures, there were minimal changes in the evaluation metrics (Figure  S3 ), confirming a limited bias of compounds around the threshold in the models developed. It confirms the benefit of combining machine learning approaches with ensemble models to improve the detection of CES1 ligands. Finally, we developed a MLP classification model using the entire set of 294 CES1 ligands and a threshold of 10 µM in order to investigate the CES activity of drugs. With a Matthews coefficient correlation of 0. 74, the model shows a good performance and looks reliable for prediction. followed by the QDA and LDA with a quite similar performance profile. In addition to crossvalidation, to check for overfitting, the possibility of an outsized effect of liminal ligands was investigated by computing the performances of the classifiers while removing compounds with Ki within 5 µM of the 10 µM threshold from the dataset (i.
Significant interindividual variability of pharmacokinetic and pharmacodynamic profiles of these medications, often associated with unforeseen ADRs and therapeutic failure, has been frequently observed in clinical practice. The main underlying causes are genetic variation and DDIs  [38]. Previously published studies  [35, 52]  have identified some classes of routinely prescribed medications as potential inhibitors of CES1, however, none of these studies performed a systematic screening of all clinically-used drugs. Hence, in the present study, we sought to focus on potential DDI resulting from CES1-mediated metabolism inhibition by clinically used drugs, using a methodology combining ensemble docking and machine learning approaches. This protocol allows for an evaluation of large databases of compounds from which only a limited number of compounds can be selected and experimentally evaluated.
Collection and Curation of the Chemical Library One thousand one hundred and fourteen FDA-approved, non-nutraceutical drugs were collected from Drugbank 4. 0  [55]  and a reference dataset of 294 ligands with biological activity on CES1 (documented Ki) was gathered from Chembl  [56]. Conformers for those molecules were either retrieved through PubChem [57], returning the experimental structure conformer if available, or generated using the open source cheminformatics library, RDKit  [58]. Ligands were converted to PDBQT format using Autodock Tools (Scripps Research Institute), assigning Gasteiger charges to the atoms at the same time  [59]. pH-appropriate protonation was conducted using OpenBabel (pH set at 7. 4)  [60]. The molecules were then randomly assigned to a training set of 230 compounds (80%) and a test set of 64 compounds (20%) for use in the development and validation of the predictive models.
Machine Learning Models Using the python library SciKit-Learn and Keras [64] (using TensorFlow as backend), three types of machine learning models i. e. , Linear Discriminant Analysis (LDA), Quadratic Linear Discriminant analysis (QDA) and a MultiLayer Perceptron (MLP) were trained to classify active and inactive ligands using the ChEMBL data. Three thresholds (1 µM, 10 µM, and 100 µM) were used to discriminate between the two classes (active and inactive ligands). The input data on the models consisted of the score and five docking terms from the Vina's score_only mode (gauss1, gauss2, repulsion, hydrophobic, and hydrogen interactions), all extracted from each pose of the ChEMBL data. One aspect of the score, namely the entropic term related to the number of rotatable bonds, was not extractable using score_only mode.
Section 3 adresses the impact and mitigation of the ATC radar interference. Section 4 briefly discusses the antenna array signal model for an arbitrary interference waveform, describing an interference detection algorithm and a signal direction of arrival (DOA) estimation method. Section 5 proposes a maximum likelihood (ML) estimation-based triangulation algorithm for interference location, using a set of DOA estimations. The implementation of a prototype based on software-defined radio (SDR) technology is described in Section 6, including synchronization challenges in low-cost devices. Section 7 shows the algorithm implementation validation by simulations, and Section 8 shows the results obtained in a real-life measurement campaign to detect and localize the ATC radar-pulsed interference.
IMPACT AND MITIGATION OF THE ATC RADAR INTERFERENCE IN SDR GNSS RECEIVERS As already stated, when using SDR GNSS receivers in the L1/E1 band with general purpose front-ends, the ATC  radar interference in the E6 band may appear as an out-of-band interference. This is clear from the top plot in Figure  9  for a Galileo example, where the out-of-band pulses are leaking from E6 to E1. These radar pulses cause spurious correlation spikes at the acquisition and tracking stages of the receiver. To assess the impact of the real ATC radar interference into an SDR GNSS receiver, we consider the use of an open source software defined GNSS receiver. 11 We show the tracking results for 53 seconds of signal in Figure  10. In this case, the receiver is not able to correctly track the signal (continuous loss-of-lock) because the pulses cause spikes in the early-prompt-late correlators.
The top plots in Figure  10  show that the navigation data bits (BPSK) are not decoded, thus the receiver is not able to provide a PVT solution due to the radar interference, thus a complete GNSS service denial results. An easy countermeasure for such interference is to use a pulse blanking algorithm. 9 An example of the blanked signal is shown in the bottom plot in Figure  9 , where the pulses have been effectively filtered. Using the blanking filter considerably improves the signal quality. The tracking results considering a real-time pulse blanking filter with a false alarm of 0. 04 are shown in Figure  11. In this case, the navigation message is correctly decoded (see the BPSK data bits in the top plots) and the receiver provides a PVT solution, with a time to first fix of 42 seconds. Only one pulse at second 32 is not blanked, but this does not break the correct receiver operation.
PROTOTYPE IMPLEMENTATION The idea of using extremely cheap RF front-ends to receive GNSS signals was already explored in Arribas et al,  17  where the goal was to use a digital television front-end USB dongle to successfully obtain a position fix by adapting the open-source GNSS-SDR. The so-called RTLSDR receiver with a retail cost below US $25 is based on Taiwan's Realtek RTL2832U DVB-T receiver chipset, sold in the form of USB dongles that allow users to watch over-the-air DVB-T European broadcast television on their personal computers (see Figure  12 ). Those devices send partially-decoded MPEG transport frames over the USB, but by exploiting an undocumented mode of operation of the demodulator chip, the user is able to obtain raw I&Q samples, stream them through USB to a personal computer and then apply the GNSS-SDR software processing.
17 t that time, in September 2013, Juha Vierinen, a researcher involved in the Kilpisjärvi Atmospheric Imaging Receiver Array (KAIRA), Finland, did an experiment with two RTLSDR's dongles, modified for sharing the same reference clock source. 18  The hardware modification consisted of removing the 28. 8 MHz XTAL in one of the dongles (see Figure  12 ) and connecting the clock input directly to the other dongle XTAL signal output. Apparently, the two front-ends are synchronized in both the downconversion chain and in the baseband sampling process. However, due t the independent USB transport streams and different sampling start times, both sample streams are not synchronized as seen from the SDR software running in the CPU, resulting in a time shift between channels, constant in time during the capture or processing session but random over sessions.
A regular laptop attached to a USB 3. 0 hub drives the dongles with the SDR software components, described hereafter. For the sake of simplicity in this experiment, the calibration signal source is obtained using a USRP B210 and a two-port splitter which is manually connected to the RTLSDR inputs in each operation. In a fully automatic  estimation algorithms and thresholds in the same signal, we captured the array baseband samples in files for post-processing. Figure  15  shows the GNU Radio Companion block diagram for DOA estimation in post processing. From left to right, the samples are loaded from file sources (skipping the first 45 seconds to exclude the calibration signal transitory). The array snapshots are fed to the DOA estimation block. Its output ports are connected to QT time sinks for visualization.
Finally, the triangulation algorithm was implemented in a MATLAB script that reads the DOA estimation files and  uses the measurement point coordinates to perform the steepest-descent solver described in Equation  17. The initial interference position estimation p0 was set to an arbitrary value located in the surrounding measurement points. The number of steps was set to 10000 and the step size was set to  = 0. 0001. Figure  25  shows the evolution of the position estimation for each step. The final position estimation error is below 60 m.
Funding information Generalitat de Catalunya , Grant/Award Number:  2017-SGR-1479 ;  Ministerio de Economía y Competitividad , Grant/Award Number:  TEC2015-69868-C2-2-R (ADVENTURE) ;  National Science Foundation (NSF)  under Grant/Award Numbers:  CNS-1815349  and  ECCS-1845833  of other, legitimate, radiolocation services.
16S rRNA sequences were submitted to NCBI to obtain accession numbers. Polymerase chain reaction was used to amplify PAI related genes including txtAB, nec1, and tomA  (Bukhalid et al. , 1998; Wanner, 2006). PCR reaction conditions were same as above except the annealing temperature was adjusted at 60 • C, 55 • C and 48 • C for nec1, tomA, and txtAB genes, respectively.
, separately. The tubes were sealed with a stopper and head space was filled with 2% oxygen. About 10% head space was exchanged with equal amount of acetylene. The tubes were placed in an incubator at 28 • C for 2 weeks. Reduction of acetylene to ethylene was measured by gas chromatography (Agilent technologies 7890A GC system), which was equipped with flame ionizing detector and Agilent CP7348 column (25m × 0. 25mm). As a positive control, two bacterial strains belonged Bacillus amyloliquefaciens (ZM2; accession number JX185642) and Pseudomonas aerouginosa (ZS24; accession number JQ990311) were used (kindly provided as positive control by Dr. ZL, University of the Punjab, Pakistan).
Greenhouse Assay Pathogenicity assay on tubers was performed in greenhouse assay  (Wanner, 2006). From eighteen isolates of pathogenic Streptomyces spp. , two isolates namely S. scabies strain AJ-7 (Accession number MG725948. 1) and AJ-10 were selected whereas S. violaceusniger strain AC12AB (Accession number MH388022. 1) was used as antagonistic and plant growth promoter strain. As a positive control PCS pathogenic S. scabies strain AC-46 (Accession number KU560917. 1) was also used. To observe normal growth pattern, tubers were also inoculated without any bacterial inoculation. The greenhouse assay was performed twice at greenhouse facility, University of Maine, Orono, United States during 2016-2017. Pathogenic S. scabies AJ-7, AJ-10, AC-46, and antagonistic S. violaceusniger AC12AB were cultivated separately in YME broth for 3-5 days at 28 • C in incubator with shaking at 150 rpm, until they attained 10 6 CFU mL -1 conc.
The purified DNA was used in qPCR reactions to amplify promoter regions with specific primers listed in Table  S1. Relative enrichment was calculated by dividing the antibody immunoprecipitation signals with the no-antibody signals. Three independent experiments were performed. RNA extraction and Q-RT-PCR Total RNA was extracted from whole plant tissues using RNeasy Mini Kit (Qiagen), cDNA synthesis was carried out on 1 µg RNA with Maxima First Strand cDNA synthesis kit with dsDNase (Thermo Scientific). For the qPCR reactions, Sybr Green JumpStart Taq ReadyMix (Sigma) was used and the reactions were performed using Rotor-Gene Q (Qiagen). We used actin as a reference gene in all Q-RT-PCR experiments as described in  (Horvath et al. , 2017). Raw data were analysed with the delta-delta CT method.
Phenotype analyses of γ-tubulin RNAi plants Immunofluorescence labelling and fluorescence microscopy: Slide preparation of squashed Arabidopsis seedlings and immunolabelling was performed as described in  (Horvath et al. , 2017)  using anti-α-tubulin (Abcam, 1:1,000) and anti-mouse Alexa Fluor 488 (1:600) antibodies. Chromatin was counterstained with DAPI. Images were obtained by confocal imaging system Olympus IX-81 FV-1000 as decribed in  (Kohoutova et al. , 2015). Images were analyses and prepared using FV10 ASW2. 0 (Olympus) and Adobe Photoshop/Illustrator CS4 (Adobe System), respectively. Cell counting was performed on immunolabelled mesophyll cells of WT and γ-tubulin RNAi leaves. Cells were counted manually in randomly selected areas defined by 1,000 µm x 1,000 µm square using ImageJ software.
8C ). The degree of endoreduplication was quantified by endoreduplication index (EI; the number of endoreduplication cycles per cell). There was an almost 2-fold increase of endoreduplication index in leaves with reduced γtubulin levels compared to WT and 1. 2-fold increase of EI in cotyledons (Fig. 8D ). To prove that the higher DNA content in plants with silenced -tubulin is due to endoreduplication and not polyploidy, we counted chromocentra in WT and γ-tubulin RNAi plants. Distribution of cells with the given chromocentra number showed a distinct peak corresponding to the diploid number of chromosomes and was similar for WT and -tubulin RNAi leaves (Fig. 8E ). Thus, the increased ploidy found upon γ-tubulin silencing is not due to endomitosis, but endoreduplication. The increase in the endoreduplication level shown by microscopical and Altogether, our data showed that γ-tubulin silencing results in overproliferation in some cells while enhanced endoreduplication in others, a phenotype that is reminiscent when E2FA together with DPA is overexpressed  (De Veylder et al.
Acknowledgements This work was supported by grant  P501 15-11657S  from  Grant Agency of the Czech Republic  to  P. Binarová  and by the  Institutional Research Concept   RVO 61388971. The research was partially supported by  Programme  for research and mobility support of young researchers MSM200201901 from  Czech Acad. Sci. to  H. Kourová , by the  Biological and Biotechnology Research Council  to  L. Bögre  ( BB/M025047/1 ), by  EFOP-3. 6. 3-VEKOP-16-2017-00009  to  B. M. Kállai , and  OTKA-NN-111085  to  T. Mészáros , by the grant  GINOP-2. 3. 2-15-2016-00001  from the  Ministry of National Economy (Hungary)  to  Z. Magyar , and by the  Czech-BioImaging  large  RI  project  LM2015062   MEYS CR  and  OP RDE CZ. 02. 1. 01/0. 0/0. 0/16_013/0001775. We thank  Barbora Gallová  and  Anna Kuchařová  for help with cloning and selection of -tubulin RNAi plants,  Gabriela Kočárová  for technical assistance, and the  CSIRO Plant Industry, Australia  for vector pHANNIBAL (Wesley et al.
K. ) and examined on an Aquasem (Tescan, Brno, Czech Republic) scanning electron microscope at 15 kV in high vacuum mode. a n u s c r i p t Accession numbers: TubG1, At3g61650; E2FA, At2g36010. 3; E2FB, At5g2220. 2; E2FC, At1g47870. 1; DPA, At5g02470. 1; DPB, At5g03415. 1; MYB66, At5g14750. 1; CDKB1;1, M At3g54180; CYCD3;1, At4g34160; CCS52A2, At4g11920; PCNA1, At1g07370; RBR, A c c e p t e d At3g12280; ORC2, At2g37560. DQ- 19, Sigma-Aldrich) in PBTB containing 0. 2% cold water fish skin gelatin for 1 h, washed in 10 0. 005% Tween 20 in PBS, pH 7. 4 (PBT), and incubated for 1 h with 6-nm gold-conjugated donkey anti-chicken IgG and 12-nm gold-conjugated goat anti-rabbit IgG (Jackson ImmunoResearch Laboratories Inc. , West Grove, PA, USA), diluted each 1:30 in PBTB,.
11, 13, 14 ACEi and ARB treatment can attenuate the development of lung fibrosis in animal models. 12, 16, 19, 20 Together, these results suggest that IPF outcomes may be positively affected by either ACEi or ARB treatment. The phase 3 ASCEND (Study 016; NCT01366209) and CAPACITY (Studies 004 and 006; NCT00287716 and NCT00287729) trials established pirfenidone as an antifibrotic therapy to treat IPF. 21, 22 2] [23]  Patients enrolled in these trials frequently received concomitant medications to treat cardiovascular comorbidities, including hypertension. 24 e goal of this post hoc analysis was to evaluate associations of angiotensin modulators with the clinical course of IPF. Patients randomized to receive placebo in the phase 3 trials were categorized by baseline antihypertensive treatments, including ACEi, ARB, and neither ACEi nor ARB (non-ACEi/ARB), and assessed for IPF disease progression and all-cause mortality.
Source Data and Study Population All patients randomized to receive placebo in the ASCEND or CAPACITY phase 3 trials were included in the study population. A second pooled analysis included patients who received placebo in ASCEND or CAPACITY and all patients randomized to receive placebo or interferon gamma-1b in the INSPIRE (Effect of Interferon Gamma-1b on Survival in Patients with Idiopathic Pulmonary Fibrosis) trial (NCT00075998). 25 he trials included in this analysis were conducted with informed written consent from participating patients and with the approval of the ethics committee/institutional review board for each participating study site (additional details in e-Appendix 1). Complete eligibility criteria for these studies were previously described. 21, 22, 25 In ASCEND and CAPACITY, patients were aged 40 to 80 years with an IPF diagnosis, baseline % predicted FVC $ 50% and # 90% in ASCEND or $ 50% in CAPACITY, baseline % predicted diffusing capacity of the lung for carbon monoxide (DLCO) $ 30% and # 90% in ASCEND or $ 35% in CAPACITY, baseline 6-min walk distance (6MWD) $ 50 m, and no clinical or pulmonary function improvement in the preceding year.
26, 27 The ACEi and ARB groups were also compared with the CCB/BB group. As a supportive analysis, the primary analysis was repeated in pooled data from ASCEND, CAPACITY, and INSPIRE. Only observed data were analyzed; no imputation was performed for missing values. Patients were censored at the time of lung transplant, loss to follow-up, or the end of the 52-week follow-up period, whichever occurred first. For models of change in % predicted FVC, declines in 6MWD, and all-cause hospitalization, death was treated as a competing risk. SAS version 9. 3 for Windows (SAS Institute) was used for all statistical analyses.
Antihypertensive medication groups were determined by baseline medication use; patients may have discontinued, switched, or added to their baseline medication during the trials. Available data limited the duration of the analysis to 52 weeks; trends over longer durations were not evaluated. Confounding from differences in baseline conditions, including pulmonary hypertension and cardiovascular disease, and the severity and possible misclassification of these conditions may have influenced observed differences in mortality as well as other outcomes. Notably, a history of cardiovascular disease was more prevalent in the ACEi group than in other groups, creating an expectation for greater cardiovascular mortality; slightly greater all-cause mortality was observed in the ACEi group in an unadjusted comparison of outcomes with the non-ACEi/ARB group, but this difference was not statistically significant in multivariable analyses.
Acknowledgments Author contributions: M. K. takes responsibility for the content of the manuscript, including the data and the analysis. M. K. , D. J. L. , M. M. -M. , I. N. , C. V. , L. F. , D. W. , M. A. , K. -U. K. , and V. C. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis; and contributed substantially to the study design, data analysis, and interpretation and writing of the manuscript. Data sharing: Qualified researchers may request access to individual patient-level data through the clinical study data request platform (https://www. clinicalstudydatarequest. com). Further details on  Roche 's criteria for eligible studies are available (https://clinicalstudydatarequest. com /Study-Sponsors/Study-Sponsors-Roche.
). Color groups denote separate experiments, with each data point representing a biological replicate. Circles show results from experiments using A. castellanii Neff, whereas experiments conducted with Acanthamoeba sp. strain ML are represented by diamonds. Data points filled with color were obtained using L. pneumophila Paris, and data points with black dots were obtained using L. pneumophila Lp02-T. Horizontal solid lines indicate medians, taking into account all data points. Dotted lines show medians only considering the infections with symbionts from Fig. 3  (pink and orange diamond shapes). Error bars denote interquartile ranges. No individual experiment yielded a statistically significant difference between the presence and absence of the symbiont or between naive and recovered amoebae (P Ͼ 0.
To test this hypothesis, we quantified L. pneumophila Paris released from Acanthamoeba sp. strain ML into the supernatant at late infection stages and also determined their infectivity. We chose 96 hpi for collecting the supernatants, because light microscopic inspection of infected cultures indicated massive release of L. pneumophila from symbiont-free amoebae at this time point. By plating the supernatants, we indeed recorded a marked (4-fold) decrease in the number of L. pneumophila organisms released in the presence of the symbiont at this time point during infection (Fig. 5A ), a ratio that could be confirmed when counting L. pneumophila via filtration and 4=,6-diamidino-2-phenylindole (DAPI) staining (data not shown). Based on L. pneumophila numbers determined by DAPI staining, symbiontfree amoebae were subsequently infected with equal numbers of L.
After an hour of incubation, gentamicin was removed by two washing steps with PYG medium. At 2, 24, 48, and 120 hpi at 20°C, cocultures were harvested to quantify amoebae and L. pneumophila. Quantification of amoebae, L. pneumophila, and L. pneumophila infection level. To determine amoeba numbers per volume, amoebae were harvested by physically detaching amoebae from the culture surface at the indicated time points and directly counting cells using a Neubauer counting chamber. Amoeba growth is expressed as the difference between starting and final cell concentration (net growth). Amoeba cell sizes were determined using FISH images and the open-source image analysis software ImageJ  (86). To monitor numbers of viable L. pneumophila, amoeba cocultures were harvested at different times postinfection, cells were collected by centrifugation (6,800 ϫ g, 8 min, room temperature), pellets were resuspended in infection buffer, and amoebae were lysed by one freeze-thaw cycle (Ϫ20°C/48°C), followed by five passages through 26-gauge injection needles (B.