dataesr/scientific-paragraphs-categorization huggingface dataset:
To access the specifically enriched mRNAs in the Ard1p, Nat3p, or Mak3p sel-TRAP experiments, the measured log2(IP/input) ratios were corrected by subtracting the corresponding value obtained from the Rpl16Ap immunoprecipitation. Average specific enrichment values were then calculated from two independent experiments. The obtained data were filtered according to the mRNA enrichment values using a threshold value increasing in 0. 1 steps from -3 to +4. From the lists of mRNAs detected above these thresholds, the evolution of the proportions of the 20 amino acids at position 2 of the corresponding proteins was analyzed to identify any possible enrichment related to this position. HGT scores were also calculated for each threshold value to validate the statistical significance of the detected enrichments.
After centrifugation (5000rpm, 15 min, 4˚C), cells were resuspended in 20ml extraction buffer (0. 7M Sorbitol, 50mM Tris-HCl pH7. 5, 0. 2mM EDTA, 1× complete EDTA-free Protease Inhibitor from Roche) by 10 up and down pipetting cycles. The total input sample was obtained by mixing 20μl of spheroplast extracts with 20μl of 2X Laemmli buffer. Cell remnants and nucleus were removed by lowspeed centrifugation (3500rpm, 5 min, 4˚C) and mitochondrial and cytosolic fractions were separated by high-speed centrifugation (11,000 rpm, 30 min, 4˚C). Mitochondria were washed once before finally being resuspended in 100μl of extraction buffer. proteins and maximizing the coverage of the original dataset (Steps 1 and 2 in B panel), (3) complementation of bestN GO terms with one or more smaller BestFGO terms displaying the highest position 2 frequency biases terms (Steps 3 and 4 in B panel).
Scripts used to perform analyses of amino acid usage bias are available at https://tinyurl. com/mr2r54x8. Mass spectrometry proteomic data were deposited on the ProteomeXchange Consortium via the PRIDE partner repository with dataset ID PXD034922 (http://www. ebi. ac. uk/pride/archive/projects/ PXD034922). The microarray data and the related protocols are available at the ArrayExpress website with the dataset identifiers E-MTAB-11772 (https:// www. ebi.
(B) Several protein groups defined by their annotation in the Gene Ontology database have a specific Nterminal signature. (B1) GO term selection procedure illustrated on the particular case of N-terminal preferences for leucine. Twenty-eight GO terms describing components were extracted on the basis of their significant overrepresentation of leucine at position 2 assessed by hypergeometric test (HGT score > 4). After several filters, 7 of them were finally retained, including a single GO BestN term and 6 GO BestF terms (see more details in S1 Supplementary Methods and S2 Fig). (B2) Heatmap of HGT scores for the 6 BestN GO terms that best describe the GO terms selected in the "components" classification when applying this strategy to the 20 amino acids. Stars indicate significant HGT scores (*HGT>3, **HGT>4).
Bottom panel: Agarose gel electrophoresis and ethidium bromide detection of 28S and 18S rRNA in immunoprecipitated RNA fractions. (C) Comparison of the composition of NatA (Ard1p), NatB (Nat3p) and NatC (Mak3p)-associated ribosomes by mass spectrometry analysis of immunoprecipitations protein fractions. Venn Diagram represents the overlap of the three different sets of coimmunoprecipitated protein partners: for each sector the number and the name of interactants reproducibly detected in two independent experiments are indicated (S4 Table). Partners indicated in bold correspond to the known subunits of NatA, NatB and NatC complexes. The pie chart shows the functional distribution of proteins for the shared partners in the three datasets. (D) Microarray analysis of mRNA contents from total extract (Input) and immunoprecipitated fractions (IP) identified mRNAs specifically enriched after sel-TRAP of NatA, NatB and NatC.
com/), located 50 km from the BGW. The timestep of the dataset was 3 hours. During the monitoring campaign of summers 2018 and 2019, the evapotranspiration chamber was placed over the ground of the BGW for two minutes every ten minutes to monitor the variation of absolute humidity, carbon dioxide, net radiation, and temperature. It was accompanied by 2 thermocouples (one inside the chamber, one outside) and by a radiometer 380 placed inside the chamber. These sensors were placed about 20 cm above the ground surface.
Data processing The energy balance measurements from the LAS MKI, CNR4 and thermocouples were collected with a CR3000 Datalogger (from Campbell Scientific®). A program was created through the software LoggerNet (i. e. , from the same Datalogger manufacturer) to communicate 385 with the sensors, and to collect and store the data. The LAS MKI manufacturer provides the software EVATION  (Kipp & Zonen B. V, 2015)  to users. It performs estimations of 𝑄ℎ by using the monitored data and those collected from Météo-France, and applying the iterative procedure presented previously. The resulting data are synthetized in a unique file (YYYY_MM_DD_Data_SEB. csv). Each line corresponds to a time step of 10 minutes for 390 which every information is recorded (the values are separated by a comma). The different columns are detailed in Annex.
The sensors associated with the evapotranspiration chamber (gas analyser, radiometer, thermocouples) were connected to a CR800 Datalogger (from Campbell Scientific®) to collect 395 measurements every second. A python script was written to verify the adequate correspondence between the steps of installation and removal of the chamber with the increase in the absolute humidity rate, and determines an evapotranspiration value for each handling. The resulting data are synthetized in a unique file (YYYY_MM_DD_EvapotranspirationData. csv) and the contain of each column is detailed in Annex. 400 As already presented in details in  Versini et al. (2020) , 𝑘 𝑎 and 𝑇 𝑠𝑜𝑖𝑙 data from TDR sensors are transmitted to a CWB100 wireless base, which transfers them to a CR6 Datalogger (from Campbell Scientific®).
Presentation of the available data set and scripts 430 For each campaign day, a specific folder has been constituted (YYYY_MM_DD, 2019_07_10 for example). Every folder contains 3 datafiles (when they exist) regrouping the measured data for the 3 different methods : Surface Energy Balance, Transpiration chamber rand Water Balance respectively (YYYY_MM_DD_Data_SEB. csv, YYYY_MM_DD_ETCH. csv, YYYY_MM_DD_Data_WB. csv). 435 For each method, a Python script also been provided to select the monitoring campaign and the associated data, transform this data in physical variables, and carry out some first analysis. An additional script is proposed to study and represent the soil, surface and air temperatures computed from the different sensors. The 4 Python scripts are presented in details in the next 440 sub-sections.
Read_Data_SEB. py This Python script aims to read the data measured during the SEB campaigns. It is structured as follow: 445 -Campaign selection: This part can be modified to select the monitoring campaign to be considered (13 dates are proposed). -Data selection and transformation: the associated data file corresponding to the measurement method is opened and read. The conduction heat flux is computed by using 450 soil temperature data and Eq. (  14 ). The latent heat flux is also deduced from the energy balance equation following Eq. (  2 ). -Representation of the computed data: A figure is proposed to illustrate and analyze the produced data. It represents the different components of the Surface Energy Balance: 455 net radiation computed with the radiometer (Rn), conduction heat flux computed with the thermocouples (Qg), sensitive heat flux computed with the scintillometer (Qh), and the latent heat flux deduced from the previous component (Qe).
Read_Data_ETPCH. py 460 This Python script aims to read the data measured during the evapotranspiration chamber campaigns. It is structured as follow: -Campaign selection: This part can be modified to select the monitoring campaign to be considered (8 dates are proposed). 465 -Data selection and transformation: the associated data file corresponding to the measurement method is opened and the evapotranspiration deduced from Eq. (  3 ) is read. -Representation of the computed data: the punctual measurements carried out by the 470 evapotranspiration chamber are depicted in a specific figure.
Read_Data_WB. py This Python script aims to read and transform the data measured by the TDR water content sensors. It is structured as follow: 475 -Campaign selection: This part can be modified to select the monitoring campaign to be considered (16 dates are proposed). -Data selection and transformation: Regarding the water balance method, the dielectric 480 constant measured by the 16 TDR sensors are converted in volumetric water content by using the Topp equation (Eq. 15). It is possible to smooth this data by using a moving window. Then, the difference between 2 consecutive water content measures is computed to estimate water loss. Finally, these values are transformed in latent heat flux by using Eq. (  17 ) and an average value for all available TDR sensors is computed. 485 -Representation of the computed data: A first figure represents the volumetric water content computed from the Topp equation for the 16 TDR sensors.
Read_Temperature. py This Python script aims to read and compare the different temperature data (in the soil, on the surface, in the air) measured during the different protocols. It is structured as follow: -Campaign selection: This part can be modified to select the monitoring campaign to be considered (17 dates are proposed). 495 -Data selection: Depending on data availability, the data files corresponding to the 3 different methods are read. The temperature measurements are specifically extracted. They correspond to: soil temperatures measured from the thermocouples placed in the substrate layer, air temperature (from the CNR4 radiometer, and thermocouples in and 500 outside the evapotranspiration chamber), and surface temperature from the TDR sensors. -Representation of the computed data: Theses temperature data measured by the different sensors are depicted.
Acknowledgments This work has been supported by the  ANR   EVNATURB  project ( ANR-17-CE22-0002-01 ) dealing with the evaluation of ecosystem performance for renaturing urban environments. It has also been supported by the  Academic Chair "Hydrology for Resilient Cities" , a partnership between  École des Ponts ParisTech  and the  Veolia  company. The authors particularly thank 625 Georges Najjar  from the  University of Strasbourg  for having providing the MKI scintillometer, the  Cerema  for the evapotranspiration chamber and the  Kipp & Zonen  company for their support concerning the sensors implementation.
The stop signal task (SST) paradigm with its original roots in 1948 has been proposed to study humans' response inhibition. Several statistical software codes have been designed by researchers to simulate SST data in order to study various theories of modeling response inhibition and their assumptions. Yet, there has been a missing standalone statistical software package to enable researchers to simulate SST data under generalized scenarios. This paper presents the R statistical software package "SimSST", available in Comprehensive R Archive Network (CRAN), to simulate stop signal task (SST) data. The package is based on the general non-independent horse race model, the copulas in probability theory, and underlying ExGaussian (ExG) or Shifted Wald (SW) distributional assumption for the involving go and stop processes enabling the researchers to simulate sixteen scenarios of the SST data.
Introduction This introduction is divided into five subsections as follows: Section 1. 1 provides a brief review of the stop signal task. Section 1. 2 presents the horse race model (independent/generally non-independent) as one of the main methods in which the stop signal task is studied. Section 1. 3 provides a summary of two key parametric reaction times distributions in which the underlying processes in the horse race model in this work are modeled. Section 1. 4 discusses the motivations behind the "SimSST" statistical software package. Finally, Section 1. 5 presents the study outline.
Motivation Until the early 2020s, several studies have tested various hypotheses in the stop signal paradigm concerning inhibition functions, the validity of assumptions of the horse race model, and a new index estimation of SSRT using simulations  [26] [27] [28] [29] [30] [31] [32]. One may find the codes used for the simulations of the stop signal task in C  [26] , Matlab  [27] , R  [28] [29] [30] , and Python  [31, 32]. However, the authors believe further improvement is needed for the following reasons. First, based on our literature review, we could not find a standalone statistical software package that allows researchers to simulate the stop signal task data effectively, easily, and timely. Second, the scripts noted above are based on the assumptions that conceptualize the go-and-stop processes using the ExG distribution, which violates peak hazard behavior observed in many empirical reaction times.
This violation ultimately restricts the use of their implementations. Third, either fixed SSD or the tracking methods are written in the scripts noted abovethere are no tools allowing researchers to work simultaneously with both methods yet. Fourth, the above methods all use an independent horse race model, yet no statistical software code has been proposed until now to simulate the generally non-independent horse race model. Finally, R provides a flexible and rich environment to work with other data simulation packages successfully established in various medical fields, such as psychology and neuroscience  [33] [34] [35] [36] [37] [38]. In addition, the researchers can take advantage of open-source and free software across platforms with active community development  [39, 40]. Hence, the authors' main motivation to present the R package, SimSST  [41] , available on CRAN is to enable the researchers to simulate the stop signal task data easier, freely, faster, more efficient, effectively, and with continuous community support.
Study Outline This paper is organized as follows. We start by providing minimal but required theories and instructions for installing the SimSST package. The main functions simulating SST data with fixed SSD and tracking methods are then illustrated. Next, we present a simulation study for estimating the parameters used in distributions of modeling GORT and SSRT using the package. Finally, we discuss the contribution of the SimSST package to the stop signal task study, current limitations, and future development plans.
The Tracking Method Based Simulated SST Data The function simsstrack() similarly simulates the SST data based on the independent horse race model and the tracking method in which at each stage a constant of 50 ms is added or subtracted to the following SSDs. Its parameters are the same as those of the former function simssfixed() and its code in R is the following: simsstrack(pid,block,n,m,SSD. b,dist. go,theta. go,dist. stop,theta. stop).
The General Tracking Method Based Simulated Correlated SST Data The functions simssfixed() and simsstrack() proposed in the package "SimSST" have two limitations. First, both are based on the stochastic independence of GORT and SSRT processes. Recent studies have shown a severe violation of the assumptions needed for the independent horse race paradigm  [46]. Solutions to overcome this issue are considering SSDs from 200 ms and incorporation of the Spearman's rank correlation  [47]  for the case of the relaxation of the assumption of stochastic independence between GORT and SSRT. (Note that for simulation purposes, the Pearson correlation is an inappropriate parameter as it may not be preserved under monotonic transformations of the original simulated data  [47]. ) Second, there is no mechanism to connect the functions simssfixed() and simsstrack().
( ) 2 As the first working example for the fixed SSD-based simulated SST data, let us consider a participant John Smith for two rounds (i. e. , blocks) of stop signal task trials, each of size ten trials, including four random stop trials. We assume that for these rounds of SST trials, the initiating stop signal delays are 220 ms and 240 ms, respectively. A further assumption is that the GORT and SSRT are independently distributed as ExG(µ = 440, σ = 90, τ = 90) and ExG(µ = 120, σ = 80, τ = 70), respectively. The R code given bellow produces the simulated dataset presented in Figure  3a  (note that the probability of stop signal is given by p ss = m/n = 0.
Method: Tracking Stoch. Ind. Status: Independent Initial SSD: 220 ms d parameter: 50 ms total trials: n = 100 k (1 ≤ k ≤ 10) stop trials: m = 25 k (1 ≤ k ≤ 10) GORT distribution: ExGaussian(ExG) θ go = (µ go , σ go , τ go ) (440, 90, 90) SSRT distribution: ExGaussian(ExG) θ stop = (µ stop , σ stop , τ stop ) (120, 80, 70). We now generates the first of ten simulated data using the function simsstrack in the SimSST package. After saving all ten datasets in. csv format, we convert them as input to the BEESTS software  [48]  with the following minor modifications: (i) Record positive SRRT values in the GORT column, (ii) Rename the columns of Trial, Inhibition, SSD and GORT to ss-presented, inhibited, ssd and rt, respectively, and (iii) Sort the dataset using the ss-presented column in ascending order.
We provide the R code in Appendix A to avoid these tedious manual modifications. The reader can access the simulated datasets in the supplementary materials. We now have the BEESTS formatted data for analysis. The following prior specification and MCMC setting are used to apply the standard BEESTS IBPA  [15, 48] : Prior Setting Information: No. Figure  4  shows each parameter estimate of GORT and SSRT with their 95% CrI fitted by the BEESTS IBPA. We confirmed that for all cases, the parameters used for generating the SST trial dataset are captured by the 95% CrI. Note that the estimation of the GORT parameters can generally be improved by increasing the total trial sample size, resulting in a narrower 95% CrI. On the other hand, estimating the parameters associated with SSRT generally produces a wider 95% CrI, and some oscillations for the τ stop parameter are found.
Summary and Contributions We provide an R package, SimSST, which is the first statistical software package implementation of simulating stop signal task data since its concept was introduced in 1948. The package can simulate data (1) using either a fixed SSD or tracking method and/or (2) either stochastically independent or dependent GORT and SSRT processes and/or (3) either monotone increasing hazard or peaked hazard reaction times distribution describing GORT and SSRT processes. In addition, flexibility on controlling the number of stop trials and total trials allows researchers to explore the asymptotic property through testing the hypothesis. We anticipate that the package assists the researchers to simulate SST under various conditions using a general non-independent horse race paradigm efficiently.
Limitations and Future Work Our work has several limitations, but they are taken as new perspectives for extensions as follows: First, the current version of the SimSST package supports a limited number of distributions, such as ExG and SW for a three-parameter family of distributions. The key two parametric candidates such as Weibull, Gamma, and Lognormal  [51]  need inclusion. One may ask to include an Exponentially modified Wald (ExW) distribution  [52]  for the three-parameter family for the next version. Second, the package "SimSST" does not yet a address simulation trials with trigger failures (TF). This issue creates another dimension for its extension in the subsequent version. Third, the scope of a simulation study using the SimSST package depends on the current support of BEESTS software.
For example, BEESTS does not include SW distributional assumption. This limits parameter estimations scenarios. Fourth, the illustrative example with this SimSST package is based on one participant with a single block trial with various sizes of trials. The extension of this example to the case with several participants (each with several blocks of trials) needs investigation. The implementation of this scenario requires the hierarchical Bayesian parametric approach (HBPA) supported by BEESTS. Aside from the above four potential future work directions, there is another potential future work direction through the presented simulations in the SimSST package: investigation of behavior of inhibition function P(Inhibition|SSD) (Figure  1b ) in terms of relative position of GORT and SSRT processes when they are simulated from the same parametric distributions  [53].
Acknowledgments: This paper is dedicated to the memory of  Professor Emeritus Paul N. Corey  (1941-2022) for his half-century service to the academic family at the  University of Toronto, Canada. The corresponding author is grateful to the following researchers for their direct and indirect influence on him on the prior years yielding to this paper:  Mike Escobar ;  Annie Dupuis ,  Wendy Lou ;  Russell Schachar ;  Keith Knight  ( University of Toronto, Canada ); and  Gordon Logan  ( Vanderbilt University, USA ). Both authors are grateful to journal reviewers for constructive comments on the first draft of the paper.
Another approach proposed by Seilov et al. [43]  involves building networks of digital twins to tackle problems in complex telecommunication networks with two specific examples in feedback loops and traffic monitoring. In the former case, the network of digital twins allows the developer the tracking of all changes made and intervention in the development of the telecommunication system. In the latter case, the network of digital twins enables the network operators to solve urgent problems in emergencies where the network traffic suddenly changes due to the dysfunction of some network elements. In another context, Yigit et al. [44]  present a digital twin-enabled framework aimed at Distributed denial of service (DDoS) attack detection for autonomous core networks. Since existing DDoS solutions are insufficient for data centers and edge networks in terms of scalability, detection rates, and latency, the authors develop an online ML-based algorithm for effective DDoS detection.
𝑥 𝑖+2 𝑡-𝑇 , 𝑥 𝑖+2  While the aforementioned approaches have demonstrated efficiency in traffic prediction, they do not take into account the similarity among different types of cellular services (e. g. , calls, internet) and regions. To address this issue, Zhou et al. [72]  introduce a transfer learning strategy based on a GCN model for large-scale traffic prediction called STA-GCN. The proposed model integrates transfer learning, an attention mechanism, the CNN model, and GCN to capture both temporal and spatial dependencies. The experiments conducted with the Telecom Italia dataset validated the efficacy of transfer learning in enhancing knowledge reusability and accelerating model fitting, without com-promising prediction accuracy. Moreover, traffic in a wireless network is impacted not only by historical traffic and cross-domain datasets but also by handover traffic from the BS.
[77] , where the authors present a spatio-temporal fine-granular user traffic prediction mechanism for cellular networks called STEP. Specifically, STEP is based on the integration of GCN and GRU models to capture the spatiotemporal features of the individual user traffic. In addition, Wang et al. [78]  introduce an attention mechanism to assign appropriate weights to each node. They propose a time-series similarity-based graph attention network for cellular traffic prediction known as TSGAN. This model uses dynamic time warping  [79]  to compute the time-series similarity between the network traffic of every pair of cells and GATs to extract spatial features. Comparative experiments conducted on the Telecom Italia and Abilene datasets against GNNs and GRU models demonstrated the superior performance of TSGAN.