dataesr/scientific-paragraphs-categorization huggingface dataset:
Results of Successive Iterations of a Fixed Spoofing Attack Since the LabSat and the Spirent cannot communicate with each other, each simulator was automatized separately. The LabSat allows the user to choose the delay between each iteration, but the Spirent does not, so the LabSat had to be set so that they both started their iterations at the same time. This caused a time delay between the two simulators, which increased over the iterations. Three datasets of respectively 170, 192, and 256 iterations were carried out (hereinafter referred to as Datasets 1, 2, and 3). Figure  13  shows the value of the clock bias jump measured for each iteration of Dataset 3. Among the three datasets, the amplitude of clock bias jump ranged from 0. 04 µs to 1. 63 ms, with a good proportion of those values being quite high (95% of them were higher than 25 µs).
In addition, the amplitude of the clock bias jump did not show consistency over the iterations, which indicated that the amplitude of the clock bias jump was random. Our model did not take into account this randomness, and that is why the amplitude of the clock jump of the model was smaller. Therefore, the spoofing attack caused abnormal leaps of the value on the clock bias whose amplitude could not be predicted due to a random phenomenon. However, this phenomenon is an advantage for the detection: the larger the peak is, the easier the detection. Figure  14  shows the value of the clock drift jump measured for each iteration of the three datasets. Regarding the clock drift leap of value, its behavior over the iterations was different depending on the series. On the first dataset, the amplitude of the clock drift jump gradually increased over time, whereas on the other series, the fluctuation had no relevant consequences.
Besides their length, the difference between the three series was the "speed"of the increase of the delay between the LabSat and the Spirent, which came from the adjustments of the repetition delay of the LabSat. Dataset 1 was the one with the highest "speed" of increase, which can explain the difference with the other datasets. However, considering the amplitude of these jumps, 0. 01 to 0. 03 µs/s, a 1 to 3 m/s distance equivalent, it seems difficult to use the variation of clock drift to detect the spoofing attack with these receivers. After these experiments, it appeared that it was possible to use the clock bias against non-synchronized attacks. However, what happens in the case of a synchronized attack? The next section deals with this issue.
Results Study population. This analysis relies on contact data collected by our group in a short-stay geriatric unit (20 beds)  18  of a university hospital counting approximately 1,000 beds, located in Lyon, France. The study period was from Tuesday February 28, 2012 to Friday, March 9, 2012, during the peak of seasonal influenza in France  19. The included data were recorded during the 10 days spanning from the 28th February 2012, at 1:00 a. m. , to the 9th March 2012, at 1. 00 a. m. Individuals were differentiated into 3 groups according to their role in the unit: patients (PAT), nurses (NUR) (nurses, nurses' aides, a social counsellor and paramedical staff) and medical doctors (MD) (physicians and residents). They all received personal electronic badges to wear with an anonymous identification number.
All patients were 77 years old or older and 70% female. Their length of stay was 8. 4 days on average with a median of 7 days. A total of 15 participants (10 PAT and 5 HCP) had confirmed influenza A (H3N2). Six individuals (3 PAT, 1 NUR and 2 MD) were prevalent contagious cases at the beginning of the study period. Four individuals contracted confirmed influenza during their stay or work schedule (3 PAT and 1 NUR). Nurses exhibited a vaccine coverage of 30%, MD 50% and PAT 55%. Contact data. Network contact data were collected using Radio Frequency IDentification (RFID) technology referenced previously  13, 20. Badges were worn individually. The system was tuned so that pairwise face-toface close encounters (less than 1 m) were recorded only when they lasted longer than 20 s (s). This setup was consistent with the real case scenario leading to respiratory virus transmission.
However, in this setting, two people talking next to each other might not end up in a recorded contact. The duration of the contact might be underestimated as 51% of the contacts lasted only 20 s. The distribution of their duration is reported in Fig. 2  and Table  1  of the Supplementary Information page 5. This duration would be multiplied by a factor f β whose values would minimise the prediction quality of our model as explained in supplementary information pages 2 and 3. The starting and ending times of the events were recorded for each pair of established contacts. A total of 17,947 contacts were recorded during the study period with a recorded cumulative duration of 875,300 s (i. e. 243 h or 10. 1 days). More detailed contact data were analysed in previous publications  19, 21. Transitions between states using a SEIR model.
Model parameters. Table  2  summarizes the model parameters. We estimated some parameters from the 10-day observed data and others from contact data. The average daily number of patients in the ward was estimated at 13. A mean of 2 new patients (z 1 ) entered the ward during the study and about 20% of them were already contagious. We considered that only 1/3 (τ ) of the susceptible HCP were on duty at the same time and then susceptible to become infected at the same time (data taken from HCP timetable, not shown here).
Therefore, we chose an event-driven approach to simulate the evolution of an influenza epidemic during the study period. We relied on the Gillespie algorithm for investigating the parameter combinations using the R package GillespieSSA (function ssa) developed by Mario Pineda-Krch  26. In this context, the probabilities of transitioning between states change over time. They depend on the current state of the populations and on the transition rate between states per day. The different transitions (Fig. 2  red lines) and their rates are reported in Table  3. These transition rates were deduced from the underlying differential equations that are reported in Supplementary Information page 7. Gillespie algorithm updates the probabilities of transitioning between states and allows simulating the evolution of the 3 populations.
Method In this section, we explained how the viral data were collected. We detailed the steps that allowed us estimating the matrix of the per capita transmission rates. We discussed the patient susceptibility according to the estimation of the number of transmissions per hour of "contagious" contacts. Virological data. Nasopharyngeal swabs were taken to confirm influenza infection. These swabs were sent to the Southern French National Reference Laboratory for Influenza (Lyon, France) and were analysed for influenza A and B viruses by real-time polymerase chain reaction (PCR; Respiratory Multi-Well system R-gene®, Argene, Verniolle, France)  19. Matrix of the per capita transmission rate β ij. The coefficient β ij is the probability that a contagious individual of population j infects a susceptible individual of population i per day, and is defined as follows  11.
The sample was painted white and then black spots were added to this white painted surface as seen in Figure  7  in order to perform 3D image correlation. The entire experimental testing setup (lights, cameras, etc. ) required to perform digital image correlation is shown in Figure  8. To track the crack onset and propagation, a four-point bending test was performed in conjunction with this DIC approach. During the experiment, two 50-W LED cameras with an 8 mm focal length were utilized to examine the study area, as depicted in Figure  8. During the test, the images were taken every 1 s. The VIC 3D v. 9. 4 software was used for the post-processing of the images taken. An optical, non-contact measurement method called digital image correlation (DIC) is used to quantify surface displacements on an object of interest.
) required to perform digital image correlation is shown in Figure  8. To track the crack onset and propagation, a four-point bending test was performed in conjunction with this DIC approach. During the experiment, two 50-W LED cameras with an 8 mm focal length were utilized to examine the study area, as depicted in Figure  8. During the test, the images were taken every 1 s. The VIC 3D v. 9. 4 software was used for the post-processing of the images taken.
Acknowledgements This work was funded by the projects  GARVM III  ( PID2019-108948RB-I00/AEI/10. 13039/501100011033 ),  ARQUEOSTRA  ( FEDER-UCA18-104415 ),  ARQUEOFISH  ( P18-FR-1483 ,  PAIDI 2020 ), and  GARVMTRANSFER  ( PDC2021-121356-I00 ).
Procedure Ethics approval from all the collaborative universities were obtained prior to data collection. The first page of the questionnaire was the consent form. Participants gave informed consent when answering the question: "Do you consent to participate in this study?" If they chose not to consent, they were redirected to the end of the questionnaire. All the data was obtained in class prior to the first COVID-19 confinement. Participation in this study required answering questions presented in an online questionnaire which took approximately 20 minutes to complete. No compensation was given to the participants, although they had the option of entering a draw to win a CAN $50 Amazon gift card. One gift card for every 50 participants was drawn in January and April 2020.
Planned Analyses After screening the data set for outliers and missing data, the normality of distributions and descriptive statistics were computed to describe the sample. To ease comparisons, all the scale and sub-scale totals were transformed into a score ranging from 0 to 100 by subtracting the minimum possible score for that scale or subscale from the observed score, dividing by the possible range of scores and multiplying by 100. In other words, percentage scores were computed. Next, to determine if the responses from the two continents (North America and Europe) were equivalent, a multiple-group invariance analysis was produced  (Meredith, 1993). To do so, the Lavaan R package was used  (Rosseel, 2012). Based on a Confirmatory Factor Analysis (CFA), this strategy investigates how the total scores of all the scales are related to a single broad construct, here anxiety in quantitative methods.
More critically, it assesses whether the factor structure is equivalent across continents. Invariance between groups will allow the analysis of the continents together instead of separately. Also, because three of the scales were translated into French for the present study and have not been validated in that language in previous studies, CFAs were carried out to examine if the scales have the same factor structure in French as they do in their original language. CFAs were used for scale validation  (Cappelleri et al. , 2014; Levine, 2005). Cronbach's alpha and McDonald's omega are also reported. Lastly, and more central to our investigation, Pearson's correlations were calculated in SPSS between all the scales' scores to examine if, and to what extent, they are associated with one another.
We focused on PDAC as a target tumor entity with high medical need and primary resistance to ICB  (11). We employed MV-NIS, an oncolytic agent currently investigated in clinical trials  (4, 12)  (clinicaltrials. gov NCT03171493, NCT02962167, NCT02068794, NCT02364713, NCT02700230). MV-NIS has also been employed in a preclinical study with PDAC xenografts in combination with 131 I radiotherapy  (13). Our results indicate synergistic effects of MV and ICB combination therapy in PDAC. We detect induction of systemic anti-tumor immunity and remodeling of the local tumor microenvironment. However, the local effects are transient, demonstrating the need for further development of this approach to achieve durable efficacy.
Flow cytometry To assess surface expression of human CD46, murine tumor cells were stained with 1 ml anti-human CD46 APC (clone TRA-2-10, Biolegend) in 100 ml PBS for 30 min on ice in the dark. Viability was assessed by staining with 0. 2 mg/ml DAPI. Samples were acquired on a CytoFLEX flow cytometer (Beckman Coulter) and data were analyzed with FlowJo software (version 10. 8. 1, Tree Star Inc. ). To assess PD-L1 expression after measles virus infection, cells were infected at MOI 3 in 12 well plates and harvested 24 h and 48 h post infection. Patient-derived cells were stained with 1 μl anti-human PD-L1 PE (clone 29E. 2A3, Biolegend) and murine cells were stained with 1 μl anti-mouse PD-L1 PE (clone 10F. 9G2, Biolegend) in 100 μl PBS for 30 min at room temperature in the dark and with 0. 2 mg/ml DAPI.
5 (clone 17A2), 1 μl anti-CD4 APC-Cy7 (clone GK1. 5), 1 μl anti-CD8a APC (clone 53-6. 7) (all from BD Biosciences), 1 μl anti-CD44 PE (clone IM7), 1 μl anti-CD62L FITC (clone MEL-14) (both from Biolegend). After antibody staining for 30 min in the dark, washing, and staining with 0. 2 μg/ml DAPI, acquisition was performed on a BD FACS LSR II (BD Biosciences). Data were analyzed using FlowJo software (version 10. 0. 7r2, Tree Star Inc. ).
T cell receptor profiling DNA was extracted from tumor samples using the DNeasy Blood and Tissue Kit (Qiagen). TCRB survey sequencing was performed by Adaptive Biotechnologies. Data were analyzed using vdjtools  (21). Gini index was calculated using only productive TCRB with ineq (v0. 2-13) package in R and plotted with ggplot2 (v3. 3. 5). Morisita index and heatmap were calculated and visualized using immunarch (v0. 6. 7) package in R (v4. 0. 5).
Gene expression profiling Total RNA was extracted from tumor samples using the RNeasy Mini Kit (Qiagen). NanoString gene expression analysis was performed by the nCounter Core Facility Heidelberg. In short, 25 ng RNA from total RNA samples was subjected to quality control by Agilent Bioanalyzer 2100 and Qubit system, hybridized with the CodeSet Immunology Panel (Mouse; NanoString Technologies), and processed using nCounter SPRINT Profiler. Raw data were normalized to the set of internal reference genes included in the CodeSet panel and data were analyzed using nSolver 4. 0 software including the Advanced Analysis package (NanoString Technologies) for cell profiling and pathway analysis. For pathway analysis, genes differentially expressed between mock and treatment groups (p < 0. 05) were mapped onto pathways using nSolver and log2-fold change expression values were calculated for each gene.
qRT-PCR One microgram of tumor RNA extracted for gene expression profiling was reverse transcribed using Maxima H Minus RT (Thermo Fisher Scientific) with Olidgo(dT) primers. Quantitative PCR was conducted on a CFX96 Real-Time System (BioRad) with 1 ml cDNA or standard, 0. 13 ml forward primer at 33 mM, 0. 13 ml reverse primer at 33 mM, and 10. 5 ml Power SYBR Green PCR Master Mix (Thermo Fisher Scientific) in a total volume of 20 μl. Standards consisted of 10-fold serial dilutions of a plasmid encoding MV N, pCG N (23), starting with 1x10 7 gene copies/ml. The following primers were used: N-241 (5' -TTACCACTCGATCCAGACTTC -3') N-331+ (5' -CCTATTAGTGCCCCTGTTAGTTT -3') The data were analyzed using CFX Manager Software (version 3. 1, BioRad).
Data were acquired on a Bioreader 7000-E instrument (Biosys) and analyzed using EazyReader ® software. Saturated wells were set to 450 spots. Splenocyte samples with an average spot count below 250 in the positive control (ConA) were excluded from analysis. To test for CD46 dependency of anti-tumor immune responses, 1x10 6 splenocytes were cultured with 1x10 5 FC1245 or FC1245-CD46 target cells at an effector to target ratio of 10:1. ELISpots were developed as described above.
Nanostring gene expression profiles revealed a unique expression pattern in mice treated with MV-NIS plus anti-PD-1 at t1, which was lost at t2 (Figure  6 ). Immune pathway analysis using nSolver software (Supplementary Figure  S8 ) pointed towards enhanced adaptive immunity, cytokine secretion, chemokine signaling, cell adhesion, MHC I antigen presentation, T cell receptor signaling, and lymphocyte activation at t1 after combination treatment with MV-NIS and anti-PD-1 compared to the other treatment groups. Of note, also innate immune activation, genes associated with host-pathogen interaction and innate immune signaling were moderately elevated after combination treatment at t1, but not after monotherapy. These differences in gene expression signatures were no longer observed at t2. Immune cell deconvolution analysis with nSolver (Supplementary Figure  S9 ) indicated an increased abundance of total CD45+ cells at t1 after MV-NIS and anti-PD-1 combination treatment.
Further, this combination was shown to promote a broader spectrum of neoantigen-specific CD8+ T cell responses  (30, 38). In most settings, efficacy depends on CD8+ T cells  (41, 44) , but NK cells and type I interferon  (44)  as well as macrophage polarization  (34)  have also been implicated in combined OV and checkpoint blockade therapy. Oncolytic virotherapy combined with PD-1/PD-L1 immune checkpoint antibodies is of significant clinical interest. Treatment of advanced melanoma with talimogene laherparepvec (T-VEC, Imlygic), the oncolytic herpes simplex virus approved by the US Food and Drug Administration (FDA) and European Medicines Agency (EMA), achieved a response rate of 62% in combination with the anti-PD-1 antibody pembrolizumab in a Phase Ib trial  (45). However, the Phase III trial of T-VEC plus pembrolizumab was terminated due to futility (clinicaltrials.
gov, NCT02263508). Nevertheless, trials investigating PD-1 blockade combined with different OVs -including herpes virus, adenovirus, vaccinia virus, alphavirus and reovirus -are currently active (NCT04665362, NCT04787003, NCT04386967, NCT03866525, NCT04616443, NCT03605719, NCT03206073, NCT04735978, NCT04445844, NCT03004183, NCT04755543, NCT03767348, NCT04348916, NCT04725331). For the oncolytic measles virus MV-NIS used in this study, a trial testing the combination with PD-1/PD-L1 blockade with atezolizumab in small cell lung cancer was initiated, but terminated due to low recruitment (NCT02919449). Regarding immunological correlates, response to T-VEC plus pembrolizumab was associated with increased intratumoral CD8+ T cell infiltration, PD-L1 and IFN-g expression after treatment  (45). Based on the finding that reovirus treatment induces PD-L1 expression, a Phase I trial of intravenous reovirus and pembrolizumab in addition to chemotherapy with eleven patients suffering from pancreatic cancer was initiated.
In a randomized trial in glioblastoma, neoadjuvant anti-PD-1 was superior to adjuvant administration, with signs of local and systemic T cell activation in correlative analyses  (74). The concept of neoadjuvant immunotherapy has been extended to oncolytic virotherapy and virotherapy combined with immune checkpoint blockade (36, 40) (NCT03259425). Pre-surgical combined virotherapy and checkpoint blockade may allow for enhanced in situ vaccination and protect patients from relapse by residual disease or micrometases after potentially curative resection. Thus, a neoadjuvant approach may represent a more suitable setting for combination immunovirotherapy. Limitations of our study include the restricted transferability of mouse studies to the clinical setting as well as possible sampling bias. Although the KPC model reflects the biology of human PDAC to considerable extent  (47) , our data show that patient-derived PDAC cultures are somewhat more permissive to MV than a KPC-derived tumor cell line.
Funding This work was funded by the  German National Science Foundation  (DFG, Grant  EN-1119/2-2  to CE), the  Wilhelm Sander Foundation  (Grant  2018. 058. 1  to CE) and the  Else Kröner-Fresenius Foundation  (Grant  2019_EKMS. 02to CE). RV was supported by a scholarship of the Heidelberg School of Oncology. GP-M. received a scholarship from the  Helmholtz International Graduate School for Cancer Research. TS was supported by a  Mildred Scheel Scholarship  of the  German Cancer Aid.
The data from all of these crystals appeared qualitatively similar (Supplementary Fig. 11 ). SXDM maps were collected at beamline ID01 at the European Synchrotron Radiation Facility. The X-ray wavelength was 1. 24 Å (10 keV, 19°angle of incidence), with a beam size of 80 nm, and a flux of 10 9 ph/s, focused using a Fresnel zone plate. A 512 × 512 pixel Maxipix detector at a distance of 1. 6 m was used to collect the nanodiffraction data. Detailed descriptions of the microscope  52  and mapping technique  22  have been described previously. 2D images were collected at 30 angles with a step size of 0. 016°to map the local d-spacing of the reflection across the crystal. This produces a 5-dimensional dataset with each pixel of the 2D image corresponding to a 3D rocking curve. One full 5D scan of a 6 × 6 μm 2 area took ~30 min.
SXDM data analysis Python scripts utilizing the XSOCS and SXDM libraries developed at ESRF (https://kmap. gitlab-pages. esrf. fr/xsocs/; https://gitlab. esrf. fr/ id01-science/id01-sxdm-utils) were used to analyze the data. Conversion of the diffracted intensity to reciprocal space was done by "binning" the diffracted intensity data in each voxel of an array with dimensions of 50 × 100 × 100 and applying 2 × 2 median filter. The 3D fitting of the center of mass of a Bragg peak was used to determine the direction and the length of local Q-space vectors. The images were masked to exclude pixels with less than 20% of the maximum diffraction intensity. An example of the reciprocal space maps generated by SXDM is shown in Supplementary Fig. 1. The top left image shows the integrated intensity of one particle measured during the charge, with a selected pixel marked with a red cross.
Acknowledgements The authors thank  Hamid Djazouli  and  Irina Snigireva  for expert technical support, and  Claire Villevieille  for the donation of the electrolyte. This work was supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme , grant agreement numbers  814106  (TEESMAT) and  818823  (M. I. R. , CARINE). X. Z. acknowledges support from the  National Natural Science Foundation of China  ( 52202210 ). L. W. appreciates the financial support from  Australian Research Council  through its  Linkage  Project ( LP1701000392 ).
Measures The FLBHIS was developed through an iterative process. Informed by current literature and expert opinion, a 100question survey was devised (see Supplementary Data Sheet 1). The survey was adapted from the Lifestyle Barriers for Cognitive Health Questionnaire (LBCHQ) (K. Muhammed 2021, personal communication, 2 September) to be suitable for an Irish population. Additional items relating to other modifiable risk factors for cognitive decline such as sleep issues  (Spira et al. , 2014)  and mental stimulation  (Krell-Roesch et al. , 2019)  were also added to the survey. The survey captured the following information: (1) Sociodemographic factors (gender, age, ethnicity, household income, level of education); (2) Exposure to, as well as knowledge of modifiable risk factors for dementia, namely diet, social interaction, exercise, hypertension, sleep, current low mood/depression, current smoking, alcohol consumption, cognitive stimulation, hearing impairment, diabetes, air pollution, and head injury.