dataesr/scientific-paragraphs-categorization huggingface dataset:
1 dBm and -101. 7 dBm) and CCA (i. e. , between -111. 2 dBm and -119. 9 dBm) values caused by multi-path propagation and external interference effects, leading to poor PDR values (i. e. , from 65. 9% to 87. 4%), despite the fact that nodes are well above the sensibility limit for each modulation. To the best of our knowledge, this is the first paper to present a large dataset using IEEE 802. 15. 4g SUN modulations in an industrial environment, and we expect that the data gathered will enable researchers and practitioners to study alternatives to enhance the dependability properties of low-power wireless communications in the context of the RAW WG at the IETF. The remainder of this article is organized as follows. Section 2 provides an overview of the SUN modulations that are standardized in IEEE 802.
15. 4g SUN receivers and a Huawei E3531 HSPA+ modem to provide Internet access. Power to the gateway is supplied through an internal PSU (Power Supply Unit), which translates the 220 VAC to 5 VDC. The gateway runs GNU/Linux and executes a Python script that reads the packets from each OpenMote-B receiver, converts the raw payload to a JSON payload, and forwards it to the cloud back-end using the MQTT (Message Queue Telemetry Transport) protocol. Nodes operate on a 60 second period with an active sub-period of 1750 ms and a sleep sub-period of 58,250 ms, as depicted in Figure  6. During the active sub-period, the nodes sample the environmental sensors (100 ms) and then perform three transmit cycles. At each transmit cycle, the node transmits three different packets, one with each of the IEEE 802.
Network Operation The configuration parameters for the different IEEE 802. 15-4 SUN modulations that are used to transmit the data packets are summarized in Table  2. As can be observed, all modulations provide an effective data rate of 50 kbps, but they trade off the link budget and occupied bandwidth. For example, SUN-FSK provides a good link budget (i. e. , 129 dB) and the largest number of channels available (i. e. , 34) thanks to its narrow bandwidth occupation (i. e. , 200 kHz). In contrast, SUN-OQPSK provides the highest link budget (i. e, 131 dB) at the expense of bandwidth occupation (i. e. , 1300 kHz). Finally, SUN-OFDM provides a trade-off between channel occupation (i. e. , 800 kHz) and link budget (i. e. , 120 dB). Notice that for all the SUN modulations, the transmit power of the AT86RF215 radio transceiver was set to its maximum value.
Performance evaluation This section presents a description of the dataset and conducts a statistical analysis of the RSSI, the CCA, and the PDR values, which are defined as: • RSSI (Received Signal Strength Indicator): average power (dBm) of a packet that has been received by the gateway; • CCA (Clear Channel Assessment): average power (dBm) that a transmitter has sampled before transmitting a packet; • PDR (Packet Delivery Ratio): percentage of packets that have been successfully received by the gateway within a given period of time.
Overview The dataset, which can be found at a public GitHub repository (https://github. com/wine-uoc/ wisun_traces) together with the Python scripts used to create the plots presented in this article, is composed of 11 files formatted in CSV (Coma Separated Value), one for each sensor that was deployed, and contains the data captured between 31 July and 10 November 2019 (103 days). However, due to external reasons (i. e. , loss cellular connectivity), we do not have enough data for 4 days (20-21 August 2019 and 28-29 October 2019, respectively) and, thus, the data analyzed only encompasses 99 days of the total. The files in the dataset are named data_x. csv, where x is the last two bytes of the EUI-64 node address, and each file contains 13 columns. The first 4 columns are independent of the IEEE 802.
Conclusions In this article, we have presented a long-term deployment in a large real-world industrial environment using the SUN (Smart Utility Network) modulations, as introduced in the IEEE 802. 15. 4-2015 standard. The resulting dataset shows that despite the increased communication range (i. e. , links up to 273. 5 m), the use of the SUN modulations alone cannot guarantee the PDR (Packet Delivery Ratio) requirements of industrial and smart grid applications (i. e. , PDR > 99%) due to multi-path propagation and external interference effects present in such a challenging scenario. Taking that into account, in the future, we aim to use the presented dataset as a tool to explore and validate the suitability of novel diversity mechanisms, such as combining packet replication with modulation diversity, to increase the PDR of low-power wireless communications while meeting its energy and regulatory constraints (i.
, 1998; Stevenson-Paulik et al. , 2003). However, no experiment in planta validates this result and using the Simple Modular Architecture Research Tool (SMART) software, we were not able to retrieve the PH domain. Because of the lack of predicted domain (except for the kinase domain), we decided to consider other targeting mechanisms involving possible protein partners. Indeed, protein targeting to a membrane can be multifactorial and may require coincidence binding of lipids and protein partners.
Arabidopsis genome codes for only two proteins with a HYCCIN domain subsequently called HYCCIN-CONTAINING1 (HYC1, At5g21050) and HYCCIN-CONTAINING2 (HYC2, At5g64090). HYC2 is broadly expressed, while HYC1 expression is restricted to pollen according to the eFP browser data set (https://bar. utoronto. ca/efp/cgi-bin/efpWeb. cgi). Hence, we chose to confirm whether HYC2 was interacting with PI4Kα 1 and NPGR2 in sporophytic tissues. To this end, we raised a UBQ10::HYC2-mCITRINE expressing lines and successfully isolated antibodies raised against NPGR2 (residues 1 to 273 of NPGR2). The expected size of NPGR2 is 82kDa. The antibody recognized a band at ca. 80kDa that is not present in npgr2-1 or npgr2-3 knock out mutants or npgr1npgr2-1 double mutant (Supplemental Figure  1B ; Table  S1 ). Moreover, the antibody recognized NPGR2-mCI-TRINE around 110kDa, but did not recognize NPGR1-mCI-TRINE indicating that the antibody specifically recognized NPGR2 (Supplemental Figure  1B ).
We then investigated the role of the Cys-rich motif, which was deleted in the efop4-4 allele. Such motif could be a site of S-Acylation; a lipid posttranslational modification that can anchor protein to the plasma membrane  (Zaballa and Goot, 2018). Indeed, according to the SwissPalm prediction software, this motif is predicted as S-acetylated with a high (in green) or medium level of confidence (in orange) (Figure  9A ). Confirming this hypothesis, all four Arabidopsis EFOP proteins were found to be S-Acylated in a recent proteomic study  (Kumar et al. , 2020). Notably, all Cys-residues (boxed in Figure  9A ) within the Cys-rich region of EFOP3 and EFOP4 were found to be S-acylated with high confidence in planta  (Kumar et al. , 2020). To experimentally test the importance of such lipid modification in EFOP localization, we mutated the two conserved cysteines (C20 and C23) into serine and generated a UBQ10prom:EFOP2-CC-mCITRINE construct.
Structure Modeling To model a structure of individual PI4Kα1 complex subunits, we used several different threading algorithms, namely Swissmodel  (Waterhouse et al. , 2018) , HHPred  (Zimmermann et al. , 2018)  and RaptorX  (Källberg et al. , 2012). All three threading algorithms lead to highly similar results. For subsequent protein-protein docking and analyses, we used the models calculated using the Swiss-model program as the obtained models had the best ProSA score  (Wiederstein and Sippl, 2007). Next, we utilized a hybrid protein-protein docking algorithm implemented in the HDOCK program  (Yan et al. , 2020)  to position PI4Kα1 to NPG1 and NPG1 to HYC1. To analyze amino acid conservation, we employed the Consurf server with the default parameters  (Ashkenazy et al. , 2016). The electrostatic potential of EFOP1 was calculated by solving the nonlinear Poisson-Boltzmann equation using the APBS-PDB2PQR software suite  (Baker et al.
Artificial microRNA phenotype Surface-sterilized seeds were plated on Murashige and Skoog (MS, Duchefa BiochemieR) media supplemented with sucrose (10g per L) containing 5μM β-estradiol or the equivalent volume of DMSO. Plates were vernalized 3 days before being placed under continuous white light conditions for 11 days. Plates were scanned. Root length was measured using ImageJ software. Similar result was obtained for five independent experiments.
Fluorescence recovery after photobleaching (FRAP) Fluorescence in a rectangle ROI (5x1,7μm) at the plasma membrane was bleached in the root by successive scans at full laser power (150 W) using the iLas2 FRAP module (Roper scientific, http://www. biovis. com/ilas. htm). Fluorescence recovery was measured in ROIs and in controlled ROIs (rectangle with the same dimension in unbleached area). FRAP was recorded continuously during 120 s with a delay of 1s between frames. Fluorescence intensity data were normalized as previously described  (Martinière et al. , 2012). The mobile fraction was calculated at t=120s with the following formula: I(t)-Min(I)/Ictrl(t)/Min(I) where I(t) and Ictrl(t) are the intensity of the bleached and control region at a time t, repectively. Data were obtained in several independent experiments.
Acknowledgments: We thank the present and past members of the SiCE group as well as  G. Vert  and  G. Ingram  for discussions and comments, the  NASC/ABRC collection  for providing transgenic Arabidopsis lines,  L. Kalmbach  and  M. Barberon  for the gift of pLOK180_pFR7m34GW,  A. Lacroix ,  J. Berger  and  P. Bolland  for plant care. We acknowledge the contribution of  SFR Biosciences  ( UMS3444/CNRS ,  US8/Inserm ,  ENS de Lyon, UCBL)  facilities:  C. Lionet ,  E. Chatre  and  J. Brocard  at the  LBI-PLATIM-MICROSCOPY  for assistance with imaging and  V. Gueguen-Chaignon ,  A. Page  and  F. Delolme  at the  Protein Science Facility (PSF)  for assistance with mass spectrometry. Transmission Electron microscopy images were acquired at the  CTµ (Centre Technologique des Microstructures) Université de Lyon1 , we acknowledge  Xavier Jaurand  and  Veronica La Padula  for technical assistance.
YJ. was funded by  ERC  no. 3363360-APPL under FP/2007-2013 and  ANR caLIPSO  ( ANR18-CE13-0025 ), M-C. C. by a start-up package "font de recherche -projet émergent" from  ENS de Lyon , SM  by  project  PlayMobil   ANR-19-CE20-0016-02  and by project  PhosphoREM-domain   ANR-19-CE13-0021-01 , L. C. N. by a PhD fellowship from the  French Ministry of Higher Education  and R. P. by the  Czech Science Foundation  grant  19-21758S.
Background: The increasing statistical power of genome-wide association studies is fostering the development of precision medicine through genomic predictions of complex traits. Nevertheless, it has been shown that the results remain relatively modest. A reason might be the nature of the methods typically used to construct genomic predictions. Recent machine learning techniques have properties that could help to capture the architecture of complex traits better and improve genomic prediction accuracy. Methods: We relied on crowd-sourcing to efficiently compare multiple genomic prediction methods. This represents an innovative approach in the genomic field because of the privacy concerns linked to human genetic data. There are two crowd-sourcing elements building our study. First, we constructed a dataset from openSNP (opensnp.
org), an open repository where people voluntarily share their genotyping data and phenotypic information in an effort to participate in open science. To leverage this resource we release the 'openSNP Cohort Maker', a tool that builds a homogeneous and up-to-date cohort based on the data available on opensnp. org. Second, we organized an open online challenge on the CrowdAI platform (crowdai. org) aiming at predicting height from genome-wide genotyping data. Results: The 'openSNP Height Prediction' challenge lasted for three months. A total of 138 challengers contributed to 1275 submissions. The winner computed a polygenic risk score using the publicly available summary statistics of the GIANT study to achieve the best result (r 2 = 0. 53 versus r 2 = 0. 49 for the second-best).
Background As costs for genetic analyses keep dropping, genetic testing is becoming more available and affordable for increasing numbers of people -a trend that can be seen in the rising number of customers that use Direct-To-Consumer (DTC) genetic testing services like 23andMe and AncestryDNA  [1]. Decreasing costs and increased availability have lead to the creation of a number of genomic data resources, such as the Personal Genome Project  [2] , DNA. land  [3]  and openSNP  [4]. Amongst these data resources, openSNP is unique, in that it offers open participation and open access to the data: Participants of openSNP can use the platform to openly share their existing DTC genetic test data, putting their data in the public domain. In addition, participants can share phenotypic traits, such as eye color, hair color or height.
Since its start in 2011, over 5,000 people have used the platform to make their genetic data available. Crowd-sourced competitions of data analysis have become more and more popular in the past few years allowing data science experts and enthusiasts to collaboratively solve real-world problems, through online challenges. This approach allows the broad exploration of the model space on a specific dataset by people with data analysis skills coming from very different backgrounds. In the context of genomic prediction of complex diseases, it is unprecedented. While the most widely used platform, kaggle. com, offers monetary rewards, crowdai. org is more academic-centered and offered the winner the opportunity to present her work at a scientific conference. We hereby present a crowd-sourcing experiment where participants could compete on crowdai.
Materials and methods openSNP Cohort Maker Because on opensnp. org, no restrictions are enforced on what users can upload, after downloading the data dump of the whole community, there is a need for in-depth data curation to produce a clean cohort of genome-wide genotyped individuals. To make these data accessible to anyone, we developed the openSNP Cohort Maker tool that through a systematic approach produced a clean and up-to-date openSNP cohort of genome-wide genotyped individuals. When running the openSNP Cohort Maker, the data processing starts by downloading the archive containing all data that were uploaded on opensnp. org by the community. Then, files are removed if: they are not text or compressed text; they correspond to exome sequencing; they are genotyping data from decodeme; they are set on the GRCh38 reference; they are corrupted.
For individuals who submitted multiple genome-wide genotyping data, either as duplicates or from different DTC companies, only the largest file is kept. A set of tools are integrated into the pipeline: genotyping data with coordinates based on NCBI36 are upgraded to match the GRCh37 reference  [5]  with liftOver  [6] ; PLINK  [7] [8] [9]  is used to convert file formats. VCFtools  [10]  is used to sort variants; BCFtools  [11]  is used to normalize reference and alternate alleles on the GRCh37 reference genome, rename samples, index files, and finally merge all individuals into one file. The output file can be directly imputed on the Sanger Imputation Service  [12].
CrowdAI Challenge The dataset that we used for the challenge was produced by the openSNP Cohort Maker and imputed on Sanger Imputation Service with HRC (r1. 1). We sent to the opensnp. org community a survey asking for their height, allowing us to create a dataset regrouping 921 individuals with both height phenotype and genotyping data. Challenge participants could use two versions of the genotyping data. One version was a sub-dataset containing 9,894 genetic variants, including the top 9,207 variants (p < 5x10 -3 ) associated with height in the GIANT study  [13] , and 687 Y chromosome predictions for the samples of the test set. The test set predictions were then submitted to the CrowdAI platform for evaluation and scoring. The score was produced based on the Pearson's correlation (r 2 ) between the predicted and true height.
Results A total of 138 challengers participated, contributing a total of 1275 submissions. The winner computed a polygenic risk score (PRS) using the publicly available summary statistics of the GIANT study to achieve the best result (r 2 = 0. 53 versus r 2 = 0. 49 for the second-best). The winning method was based on PRS. The training set and testing set were combined for quality control and data preparation. As self-reported sex was not provided, participant's chromosomal sex (i. e. XX vs XY) was imputed using PLINK, which uses the X chromosome inbreeding coefficient (F) to impute sex. Standard cutoffs were used, whereby F < 0. 2 yielded an XX call, while F > 0. 8 yielded an XY call. One participant yielded an F of exactly 0. 2, and was removed from subsequent analyses (they were in the training data).
Of the remaining 920 individuals, 396 (43%) were XX, and 524 (57%) were XY. The openSNP platform contains genomic data of relatives. The presence of relatives has the potential to bias results, as closely-related individuals will dominate the estimation of principal components and will inflate prediction accuracy statistics  [15]. The genetic relationship between participants was calculated using the PLINK computation of identity-by-descent (IBD), which is an estimate of the percent of the genome (excluding sex chromosomes) shared between two individuals. The IBD analysis identified seven pairs of strongly-related individuals (first-cousin or greater), including two pairs of monozygotic twins. The analysis also identified a surprising cluster of 18 individuals estimated to be 3 rd cousins, or equivalent.
PRSice and PLINK were used to compute PRS for height in the openSNP sample, using the results from the GIANT study of height. PRS were computed at 14 different p-value thresholds (p < 10 -8 to p < 1. 0), shown in Fig 1. Linear regressions predicting height in the training data were fit in R  [26]. Chromosomal sex was the first variable included in the model, followed by the top three genome-wide principal components, which help to control for differences in ancestral background  [27]. Chromosomal sex predicted 46. 81% of the variance in the training data, and the addition of the three principal components subsequently explained 0. 91% of variance. Finally, each of the 14 PRS were added to the model and compared. The PRS at p < 1x10 -5 was observed to perform best, and captured an additional 10.
variants. The second versions was a full dataset containing 7,252,636 variants which passed a quality threshold, defined as an imputation score IN F O > 0. 8, genotyping missingness frequency F m < 0. 1, and a Hardy-Weinberg equilibrium exact test p -value < 1e -50. Both versions of the data were given in the VCF format, as well as in an additive format where each genetic variant is represented by 0 (homozygous for reference), 1 (heterozygous), 2 (homozygous for the alternate allele) or NA (missing data or variants of allosomes), easier to handle for participants unfamiliar with genetics. The data were separated into two sets, a training set with 784 samples and a test set of 137 samples (an 85/15 split). The challengers were provided the training set with the genotyping data and the height phenotype and the test set with the genotyping data only.
Furthermore, the significant distortion of the 2s state resulting from closest neighboring ions will make both intra-atomic and inter-atomic 2s-1s transitions possible, which are dipole-forbidden for an isolated atom and relatively low-density systems when deeply bound 2s and 1s states preserve their ideal s symmetry. To classify various transitions, we use the word of intra-atomic for transitions of electron having both initial state and final state belong to the same atom, while inter-atomic transitions involve two atoms that can be either the same type or different species. Here, we present interspecies radiative transition results from first-principles calculations by thermal density-functional theory (DFT) using the ABINIT software package  33, 34  in the planewave-based projector augmented-wave (PAW) approach.
To calculate the ratio of K α emission to Auger decay, we have used the atomic kinetic modeling code PrimSPECT  38  , which is extensively used in the plasma physics community. For the concerned plasma densities varying from 250 g cm -3 to 2000 g cm -3 and kT = 50 eV, the averaged ionizations of Fe and Zn ions are about <Z> = 15. 8~16. 2 and <Z> = 19. 5~20. 1, respectively. Namely, such superdense plasmas mainly consist of neon-like ions of Fe 16+ and Zn 20+ , which both have the dominant electronic configuration of 1s  The ratio of inter-atomic Kα emission (from Zn to Fe) to the regular intra-atomic Kα signal (within Fe) as a function of inter-atomic Fe-Zn distance. b Similar to a but for inter-atomic Kα emission from Fe to Zn. All of these calculations were done at different plasma densities with the same temperature of kT = 50 eV.
Methods Our DFT calculations were performed with the ABINIT software package  33, 34  , in which electrons are treated quantum-mechanically with a plane-wave finite-temperature Kohn-Sham DFT description. The electrons and ions are in thermodynamic equilibrium with an equal temperature (Te = Ti). The electron-nucleus interaction is described in the PAW approach by a pseudopotential generated with a very small matching radius (rc = 0. 2 bohr). All electronic wavefunctions are explicitly computed in the thermal DFT formalism. For the electronic exchange and correlation interactions, we use the generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional  44. It is noted that the PBE functional has been widely used in DFT calculations for warm-/hot-dense plasmas  12, [45] [46] [47]  that showed good agreements with HED experiments; Our results presented here are insensitive to the choice of exchange-correlation functional, for which the localdensity approximation (LDA) gives essentially identical results, except for small energy shifts (see Supplementary Information).
Acknowledgements This material is based upon work supported by the  US Department of Energy National Nuclear Security Administration  under Award Number  DE-NA0003856 , the  University of Rochester , and the  New York State Energy Research and Development Authority. This work is partially supported by  US National Science Foundation PHY  Grant No. 1802964  for S. X. H. and V. V. K. This report was prepared as an account of work (for S. X. H. , V. V. K. , and P. M. N. ) sponsored by an agency of the  U. S. Government. Neither the U. S. Government  nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights.
emac. gsfc. nasa. gov/ dataset/thai after requesting an IP address authorization to Thomas Fauchez (thomas. j. fauchez@nasa. gov). Table  3. Instantaneous fields to be output by the GCM. For each diagnostic, the mean value and the standard deviation are computed from data output at the specified frequency and number of orbits for the case. OLR and ASR correspond to outgoing longwave radiation (at TOA) and absorbed shortwave radiation (at TOA), respectively, SW and LW correspond to shortwave and longwave, respectively, CF corresponds to cloud fraction and MMR at mass mixing ratio. The main objective of THAI is to highlight how differences in atmospheric profiles produced by each GCM are going to impact the predictions of atmosphere detectability and observational constraints for habitable planet targets such as TRAPPIST-1e  (Morley et al.