dataesr/scientific-paragraphs-categorization huggingface dataset:
We use three scenarios; each scenario contains different number of TCP sender nodes: scenario A with 12 TCP sender nodes; scenario B contains 25 TCP sender nodes; scenario C with 50 nodes source of TCP sender. Three data packet lengths of 1000, 2000 and 2500 bytes were analyzed in each scenario. The execution time of the simulations is 300 s. Table  2 , describes general simulation parameters. Table  3  describes the simulation parameters of U-NewReno protocol.
Secondary metabolite analysis For targeted and untargeted analysis of bioactive secondary metabolites, GA1 and CMR12a were cultured in root exudate-mimicking medium or in casamino acids medium, as described above, and on tomato plantlet roots (see the section ''in planta competition" below). Untargeted analyses of metabolites produced by GA1 (presented on Figs. 1a, S1, S2, S4, and S11) and CMR12a were performed using UPLC MS (Agilent 1290 Infinity II) coupled Jet Stream ESI-Q-TOF 6530 mass spectrometer used in positive mode with the parameters described in Supplementary Methods. Data were additionally processed with Mzmine 2  [37]  as explained in Supplementary Methods. Targeted analysis of metabolites produced by GA1 (presented on Figs. 1b, 4 , S5, and S12) were made by UPLC MS with UPLC (Acquity H-class, Waters) coupled to a single quadrupole mass spectrometer (SQD mass analyzer, Waters), using a C18 column (Acquity UPLC BEH C18 2.
Data are calculated as indicated in a. Mean values were calculated from data obtained in three repeats from three independent experiments (n = 9). Statistical significance was calculated using Mann-Whitney test where "****" represents a significant difference at p < 0. 0001. c Overproduced BSMs structures with variable parts in red, explaining the natural coproduction of variants for most of the compounds (1: (dihydro)bacillaene, 2: surfactins; 3: bacillibactin; 4: (oxy)difficidin). dilutions were prepared and 200 µL of each were plated onto solid LB medium using glass plating beads. After 24 h of incubation at 30 °C for CMR12a and at 42 °C for GA1, colonies were counted. The colonization results were log-transformed and further statistically analyzed. In addition, detached bacteria were used to investigate acnA, srfaA, dfnA, and baeJ gene expression by RT-qPCR, as described in Supplementary material.
Distribution of sequence homologs of the PchE protein in bacterial genera To look for sequence homologs of the amino-acid sequence of the PchE protein, we constructed a database of BLASTp results (n = 1000). Sequences extracted from the database were clustered at 95% identity using the program CD-HIT  [38]  and representative sequences were selected for each cluster to remove redundancy (n = 289). We further filtered the selected sequences to share >40% identity with the original PchE protein sequence from P. sessilinigenes and cover >90% of the BLAST query (n = 253). A ClustalW multiple sequence alignment was performed on the resulting sequences with the R package msa  [39]. Distance matrix and construction of the Neighbor-Joining phylogenetic tree were constructed with the R package ape  [40].
Statistical analysis For statistical analyses, software GraphPad PRISM 8 with Mann-Whitney or Student paired T-test was performed. Further, the RStudio 1. 1. 423 statistical software environment (R language version 4. 03)  [42] was used for multiple comparisons where one-way ANOVA and Tukey's Honest Significant Distance (HSD) tests were performed. The groups that differed significantly from each other, at α = 0. 05, were labeled with different letters.
We evaluated the potential of GA1 to modulate secondary metabolite production upon interaction with CMR12a under conditions avoiding possible interferences due to diffusion constraints in a semi-solid matrix or due to the formation of impermeable biofilm structures. Assays were thus performed by growing GA1 in agitated liquid medium supplemented or not with Pseudomonas cell-free supernatant (referred here below as crude extract, 2% (v/v)) containing metabolites produced by CMR12a in casamino acids medium. We first employed an untargeted UPLC-MS approach to compare metabolites secreted by GA1 in the two conditions in samples collected at three different time points corresponding to the beginning and the end of the exponential phase or the stationary phase. Data processing with MZmine 2 allowed us to detect features and the corresponding natural products differentially produced upon interaction.
sessilinigenes CMR12a trigger of secondary metabolite boost in B. velezensis GA1. a The CMR12a cellfree supernatant (CFS) was processed through the solid-phase extraction on C18 cartridges and compounds were stepwise eluted according to their hydrophobicity with increasing acetonitrile-water ratio, expressed in ACN % (v/v). All fractions were analyzed by UPLC-MS and those containing substantial amounts of pyoverdine (PVD) (1: PVD structure) and/or enantio-pyochelin (E-PCH) (2: E-PCH structure), labeled as dashed and solid lines, respectively. Fold changes were calculated based on relative quantification of the compounds by UPLC-MS (peak area) per OD600 in treated cultures compared to un-supplemented controls (fold change = 1, red line). Data are from one representative experiment showing mean and ± SD calculated from three technical replicates.
FUNDING This work was supported by the  EU Interreg V France-Wallonie-Vlaanderen portfolio SmartBiocontrol (Bioscreen and Bioprotect projects, avec le soutien du Fonds européen de développement régional -Met steun van het Europees Fonds voor Regionale Ontwikkeling) , the  European Union   Horizon 2020 research and innovation program  under grant agreement No. 731077  and by the  EOS  project  ID 30650620  from the  FWO/F. R. S. -FNRS. AR and GB are recipient of a F. R. I. A. fellowship ( F. R. S. -FNRS, National Funds for Scientific Research in Belgium ) and MO is Research Director at the F. R. S. -FNRS.
The EPICA Dome C (EDC) ice core provides the longest continuous climatic record covering the last 800 000 years (800 kyrs). Obtaining homogeneous high resolution measurements and accounting for diffusion provide a unique opportunity to study decadal to millennial variability within the past glacial and interglacial periods. We present here a compilation of high resolution (11 cm) water isotopic records with 27,000 δ 18 O measurements and 7,920 δD measurements (covering respectively 94 % and 27 % of the whole EDC record), including both published and new measurements (2,900 for both δ 18 O and δD) over the last 800 kyrs on the EDC ice core. We show that overlapping time series performed over multiple depth ranges over the past 20 years, using different analytical methods and in different laboratories, are consistent within analytical uncertainty, and therefore can be combined to provide a homogeneous data set.
Measurements techniques and coherency of the dataset Over the last 20 years, several techniques have been applied to measure δD and δ 18 O of the EDC ice core (Tables  1  and 2 ). The first measurements on the first EDC ice core (DC-96) were performed using the uranium reduction method for δD  (Vaughn et al. , 1998)  or the CO2 -H2O equilibration method for δ 18 O  (Meyer et al. , 2000). The latest measurements were performed on the EDC2 ice core using a method based on cavity ring down spectroscopy (CRDS)  (Kerstel and Gianfrani, 2008; Busch and Busch, 1999). The precisions obtained for the different methods are comparable, i. e. 2 σ values between 1 and 1. 4 ‰ for D and between 0. 1 and 0. 4 ‰ for  18 O (Table  2 ). Figure  1  displays the full high resolution (11 cm) datasets for δ 18 O and δD of water over the EDC ice core (figure  1 ).
The EPICA ice core A discrete wavelet analysis is used to identify the contribution to the overall isotopic variability from signals of different periodicities (i. e. corresponding to decadal to multi-millennial signal variability). With this aim, we produced a multi resolution analysis (MRA) using R software with the waveslim wavelet package  (Whitcher, 2020)  containing the MRA function with a Daubechies orthonormal wavelet filter. MRA is a mathematical analysis tool which decomposes a signal at different resolution levels. An important feature of MRA is its ability to capture temporally localised changes at its nearest neighbour. A low (high) resolution level corresponds to a coarse (detailed/high frequency) component of the original signal. Each MRA level can thus be used to interpret the temporal variability within a frequency range.
Recorded multi-decadal to multi-millenial isotopic variability over the last 800 kyrs The compiled high resolution water isotope datasets on the EDC ice core is presented in Figure  1. For δD, 5 interglacial periods have been analyzed at high resolution. For δ 18 O, we have a profile almost complete except MIS 7 and part of MIS 11. We use these times series to study the multi-decadal to millennial variability over the last 800 kyrs, extending the results of  Pol et al. , (2011 Pol et al. , ( , 2014)) , which focused on the evolution of the multi-decadal and multi-centennial variability during the Holocene, MIS 5 and MIS 11. We applied the MRA decomposition on each of the 6 selected intervals (see Methods) and present decadal to multi-millennial variability across the last 800 000 years (Fig.
checked the coherency of the different datasets in two different ways: 1/ comparison of the same samples measured by different techniques on different periods and 2/ comparison of the low resolution measurements (55 cm resolution) with a 5 points average of the high resolution measurements (11 cm resolution). First, we used the new CRDS technique in 2019-2020 to measure two sets of samples already analysed within the period 2004-2010 by uranium reduction for δD on MIS 5. 5 (1670-1693 m) and by H2O-CO2 equilibration for δ 18 O (1670-1793 m). Figures 2 and 3 provide two examples of analyses performed on all overlapping intervals. Additional comparisons of new vs old data are also presented in the supplementary material sections (Figure S5 and S6.
Reporting and sharing An iEEG dataset consists of complex and often heterogeneous data, including information from the clinical team, pre-and post-operative neuroimaging data, results of neuropsychological assessments, electrical cortical mapping, assessments of patient participation in the experimental tasks, the actual neurophysiological and behavioral data recorded during the experimental tasks and associated annotations ( Fig. 2  ). The Brain Imaging Data Structure (BIDS) provides a template for organizing neuroimaging data of various types in a standardized directory, filename and file structure that now covers multiple imaging modalities (for MRI, iEEG, MEG and EEG, see, respectively,  ( Gorgolewski et al. , 2016 ; Holdgraf et al. , 2019 ; Niso et al. , 2018 ; Pernet et al. , 2019 ) ), with work continuing to incorporate additional modalities such as single unit activity.
An iEEG dataset includes anatomical imaging data, location of intracranial electrodes, iEEG data in relation to the events in the experimental task, and behavioral measures (see Fig. 2  ). As iEEG is acquired in clinical rather than lab settings, this introduces sources of variability across acquisition sessions and medical centers, so special attention is needed to document metadata. Minimally, those metadata shall document the experimental environment (e. g. , hardware, software, equipment wiring diagram), information concerning additional equipment, participant behavioral status, and the experimental protocol and/or deviations from the protocol due to unforeseeable reasons such as medical interventions during testing. Specific pre-processing is required for the raw dataset both from the clinical and research perspective (e.
This will improve efficiency, quality and reproducibility of science. For an example of iEEG datasets in the BIDS format, see  4. While a standardized format offers the most universal application of data, some researchers share valuable data in a lab-specific manner. In that case, the interoperable (I) and reusable (R) criteria of FAIR 5  should be considered  ( Wilkinson et al. , 2016 ). Specifically, i) the format of physiological and behavioral data should be sufficiently simple, and variables corresponding to each measurement should be explicitly described in companion documents, ii) co-registered anatomy in standardized coordinates should be integrated into a provided workflow, and allow plots to be easily made from analyses of time series data, and iii) a clearly written workflow and all code (fully and extensively commented) to reproduce every element of published work should be provided.
For examples of this approach, see recent libraries of publiclyavailable, open-source, ECoG data and code 6  ,  ( Miller, 2019 ) , or multimodal iEEG-fMRI dataset  ( Berezutskaya et al. , 2022 ). If the patient data is to be shared publicly according to Open Science principles, additional constraints will apply. For example, to support a paper with primary research findings, or alternatively the form of a data descriptor paper, it is crucial to consider the legal (e. g. , GDPR or HIPAA) and ethical considerations regarding patient privacy. Anatomical imaging data that are shared with the published dataset must be defaced or skull-stripped to remove facial features, but that does not guarantee that it is impossible for the participant to be re-identified  ( Abramian and Eklund, 2019 ; Schwarz et al.
Challenges, recommendations and reporting advice • Sharing iEEG data, or any other data from human research participants, requires written informed consent that explicitly specifies which data can be shared (e. g. , iEEG signal, neuroimaging data, video), to which audience (e. g. , other institute(s), public space) and what measures are undertaken to warrant data anonymization or deidentification to protect participant privacy (  ( Bannier et al. , 2021 ) , see also  7  ). In addition, one needs to consider the safety of the tools used to share the data, as well as the necessity of setting up a data sharing agreement. • Visualization of the raw signals and their power spectra permit the evaluation of data quality (if permitted by the acquisition software, the sanity check of power spectra should be performed during the actual recording, otherwise it must be performed offline, see section 3.
3. 1 ). More broadly, once registered in a normalized space any individual data (e. g. , MRIs, PET as with the iELVis toolbox  ( Groppe et al. , 2017 ) ) can be projected on the corresponding brain template to perform group-level analyses or comparisons with prior studies or databases. The second goal of registering the patient's brain to a template is to make use of various anatomical or functional information from atlas(es) matched to this template. While defined in space relative to a template, an atlas should not be equated with a brain template, rather an atlas is the projection of information on a template. Similar to an atlas of the world being defined by features over space (e. g. , elevation, climate, social, geopolitical) projected on a given map, a brain atlas consists of labeled brain parcels with anatomical or functional information.
See the iEEG-BIDS specification and Lead-DBS website for a thorough review of the different "MNI spaces " (see 14 and 15 ). Since the reference space and the transformations estimated are specific to each program, the exact reference template space (e. g. , MNI152NLin2009Asym) should be mentioned, together with the software used for the normalization of electrode locations to the MNI space. Brain lesions pose a challenge for registration algorithms that would try to match the locally abnormal imaging intensity to the template. As epileptic activity can originate from lesioned tissue, it is actually common to have electrodes surrounding lesions. The position of these electrodes in the template is therefore more susceptible to imprecision. We recommend being particularly vigilant regarding electrode position in a normalized space for patients with a lesion, and we advise to visually assess the quality of the registration procedure and the respective electrode positions around the brain lesion.
Excluding trials due to artifacts should be done blind to the condition to avoid selection biases. Demeaning (a. k. a DC-offset correction) corresponds to centering the signal around zero. Detrending consists of correcting for a linear drift. The signal features that we aim to remove with demeaning or detrending generally originate from the acquisition equipment and are not interesting. These corrections can be applied on an entire dataset or on single epochs depending on the planned analysis. For long trials, a high-pass filter at low frequency could be prefered (e. g. , at 0. 01 Hz). They facilitate signal visualization such that the average signal value during a predefined baseline epoch is zero. Frequency filtering refers to the removal of specific spectral components, such as the ambient electrical noise (i.
We also recommend performing the time-frequency decomposition for phase analysis twice: once with a local reference scheme and once with a monopolar reference, to assert if the electrode(s) used as reference is/are 'neutral' phase wise or, in contrast, if it/they show(s) some phase angle consistency (see panel E in Fig. 12  ). The reference and the estimate of signal magnitude/power. Signal magnitude is impacted by the reference montage, as it represents the magnitude difference between a given electrode and the reference electrode(s) in the frequency domain  ( Shirhatti et al. , 2016 ). Also, it is critical to make sure that the signal(s) used as reference is neither artifacted or modulated by experimental factor(s), which would then be mixed with the signal(s) of interest. We recommend looking at the same dataset with different reference montages to verify that the electrode(s) used as reference do(es) not contaminate the signal, enabling one to understand the spatial features of the referenced signal(s) (see Fig.
, 2004 ). Compared to functional connectivity analysis, effective connectivity analysis leads to more complete models of the large-scale cognitive networks, with detailed causal relationships and information spreads between brain areas. We advise iEEG researchers to test the validity of the methods on simulated data prior to their application on acquired datasets. We also recommend comparing the differences between methods' outputs and interpret the results in light of the literature related to the structural organization of the network of interest (including its hierarchical organization). Especially, the benefit remains to be explicit for hypothesis driven research and sometimes more risky for exploratory research.
The types of research conducted Scientific research is based on a fundamental dissociation between exploratory research (a. k. a postdiction) and hypothesis-driven research (a. k. a. prediction). Discoveries stem from data-driven exploratory research that makes few, if any, prior assumptions. The investigation aims to make observations, build a model from these observations, and generate new questions and hypotheses. Next, the hypotheses or models have to be empirically tested according to a dedicated protocol. Here comes the hypothesis-driven approach, where the investigation is planned prior to observing a newly acquired dataset. The hypothesis/model that was stated beforehand is validated (or not) based on the data, and can be accepted or (partially) rejected and modified (see Fig. 14  ).
At the larger scale, science advances through the alternation of exploratory and hypothesis-driven research; something that can be ex- ploited within a single study using a cross-validation procedure that dissociates the two research types. Using a first (pilot) dataset that is already acquired, its exploratory analysis provides a hypothesis that is subsequently tested on a second dataset that remains to be recorded, or that is recorded but not yet analyzed (here the pilot dataset is not to be included in the subsequent analysis). Analyzing pilot data prior to the acquisition of the remaining dataset allows some time to get a similar electrode placement for the remaining dataset. As such, the reported choice of predefined ROI prevents circular analysis. The second dataset validates the hypothesis generated by the exploration of the first dataset.
This procedure can be adapted when the dataset for an entire group is already available by simply splitting participants into an exploratory and validation group. Then, data from the validation cohort is held out from the initial analysis (performed on the exploratory cohort) and is used solely to validate the hypothesis/model. This dichotomy, or perspective, is fundamental considering the general context of iEEG research, which is prone to selection bias (see Sections 1. 1. 5 and 1. 2. 3 ). The limited recording time with the participant and the richness of the data often motivate the use of anatomical and/or functional criteria to select participants and/or electrodes. Typically, this choice can stem from the anatomical location of the electrodes and/or the working hypothesis guided by independent data collected from the same patients (e.
2. 2 and 4. 2. 3 ). • When an iEEG study is published, we recommend sharing analysis code and making data available (conditional on the participants' consent, see Section 1. 3. 8). The reuse of data is valuable for replication purposes and for extending previously reported findings. It should be acknowledged that data sharing might come with some hidden risks: the multiplication of statistical tests and double dipping / circular reasoning. When the same data is re-analyzed, some tests are repeated without correction for multiple comparisons and with an inflated risk of false positives. Knowing what to look for can influence analysis processes in order to find again the initially postulated effect. To reduce these risks, we recommend in follow-up studies to take previous analyses into account.
, 2021 ))  or the wish to focus on the applicability/relevance of a series of methods applied on the human brain  ( Brovelli et al. , 2005 ; Moses et al. , 2021 ). From the perspective of cognitive neuroscience studies, the analysis is performed on multiple trials and eventually validated through a test-retest procedure across two distinct datasets (for a discussion on inter-individual variability see  ( Jeffery et al. , 2018 ) ). While supported at the individual level by iEEG sensitivity (i. e. , SNR), the main limitation of single case studies is the difficulty to generalize the result to the population and the significance of a single case study is reached when converging evidence from multiple patients (with various etiologies or foci) assure some generalisability. Yet, a thorough investigation of a single case permits to reach a high level of qualitative description, potentially leading to a "black swan " effect: an observation made for the first time can lead to a breakthrough.
Yet, one should keep in mind that the validity of the simplification depends on the granularity of the anatomical parcellation that is used. This can be compared to smoothing of data along other dimensions: too little smoothing highlights the interindividual variability which then might be interpreted as noise, whereas too much smoothing washes out the effects. The right level of smoothness is an empirical question: establishing the relationship between anatomy and function with iEEG implies a detailed participant-level anatomical investigation, and to go back-and-forth between group-level and individual analysis (even when many participants have been recorded). Because the electrode positions are variable across patients, recording at sufficiently similar locations in multiple participants requires enough participants and hence time to collect the complete dataset.