dataesr/scientific-paragraphs-categorization huggingface dataset:
Biological plant dataset On the plant dataset  [37] , A-Pro on AA gene trees returned a species tree (Fig. 5 ) that was largely similar to the main single-copy ASTRAL tree reported by the original study (only 4 differences). Using C12 gene trees resulted in only one change, swapping Chara and Coleochaetales. In contrast, DupTree differed from the ASTRAL tree in 33 out of 77 branches (21/77 for iGTP-DupLoss) and violated many known biological relationships (Fig. S10 ). The A-Pro trees are consistent with ASTRAL for major groups, including placing Zygnematales (not Chara) as sister to all land plants, the placement of Amboerlla as sister to the rest of angiosperms, and monophyly of Bryophytes (liverworts, mosses, and hornworts). The four changes between the ASTRAL and A-Pro trees are interesting.
Each of the two tripartitions has two leaves of α G (Q) in one of its parts and the other two leaves on two different parts. Also, the two leaves that are together can only be a and b or c and d and thus, only one of P 1 and P 2 would include both a and b in the same part. Therefore, by condition (iii) of Definition 6, exactly one of Q Table  S2 : Rank of methods on S100 dataset over all 120 test conditions. Ranks are obtained using mean species tree error, rounded to two significant digits to create tie for cases where error values are extremely close. q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Loss/Dup: 0 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Model: 1/1 Model: STAG A-Pro ψ G (Q) ----→ P 1 and Q ψ G (Q) ----→ P 2 can be true.
D Figures 0 20 40 ILS Figure  S8 : Species tree error on S100 dataset. We compare the species tree error of the STAG method to A-Pro, showing mean and standard error over 10 replicates for each model condition, with varying numbers of genes (k) and sequence lengths (with Inf signifying true gene trees). Model conditions are labeled as a/b where a is the level of ILS (1 or 5) and b is the duplication/loss rate (1, 2, or 5). Cases with missing STAG results are due to STAG failing to run on those model conditions. Note that STAG infers a species tree from the input gene trees that have at least one leaf representing each species of interest; if none of the input gene trees satisfy this requirement, then STAG fails to return a tree.
Figure4: Species tree error on S100 dataset. We compare the species tree error of the four methods, showing mean and standard error over 10 replicates for each model condition, with varying numbers of genes (k) and sequence lengths (with Inf signifying true gene trees). Model conditions are labeled as a/b where a is the level of ILS (1 or 5) and b is the duplication/loss rate (1, 2, or 5).
This panel was designed with NimbleDesign software. For each sample, paired-end libraries were created with the KAPA HTP Library Preparation Kit for Illumina platforms (Roche Nimblegen), SeqCap EZ Library SR (Roche Nimblegen) and the NEXTflex-96 PreCapture Combo Kit for indexing (Bioscientific). Sequencing was conducted on a MiSeq system (Illumina), according to the standard operating protocol. NGS sequencing reads were mapped to the published human genome reference (UCSC hg19) using Bowtie2 aligner. Following best practices proposed by GATK, indel realignment was done to determine suspicious intervals likely to need realignment (GATK RealignerTarget-Creator function), followed by local realignment of reads to correct misalignments due to indels (GATK IndelRealigner function). Finally, a base quality score recalibration was done (GATK BaseRecalibrator function).
Duplicated reads were marked with Picard tools and variants were called with "Haplotype Caller" from GATK (100341 variants were obtained). We annotated the final VCF file with SnpEff/SnpSift. We used the dbSNP, ExAC, 1000 Genomes, and ClinVar databases, and the prediction tools PolyPhen2, SIFT and Mutation Taster. Variants were prioritized with the web-based server B_Platform (Bitgenia, https://www. bitgenia. com/). Candidate variants were analyzed in terms of their presence in the set of candidate genes (a panel of 55 autoinflammatory genes), population frequency, possible impact on protein sequence, and previous clinical reports in databases. Plasmids and transfection-HEK293T cells were transiently transfected in the presence of Lipofectamine 2000 (Invitrogen). The variants were introduced with the QuikChange II XL site-directed mutagenesis kit, using the pDONOR221-ISG15 vector.
Cells were then washed and fixed, resuspended in diH 2 O containing EQ Four Element Calibration Beads (Fluidigm) and acquired on a CyTOF2 mass cytometer (Fluidigm). Data files were normalized with a bead-based normalization algorithm (CyTOF software, Fluidigm) and debarcoded with CD45 gating. The gated populations were visualized in lower dimensions with viSNE in Cytobank (https://www. cytobank. org/). Immunohistochemistry-Immunohistochemistry staining was performed using a Discovery Ultra Ventana instrument (Roche), with the staining module of RUO Discovery Multimer V2 (V0. 00. 0083). Slides were first incubated in blocking agent containing 2% BSA PBS, and Discovery Ultra antibody block (Cat # 760-4204) (Roche). Slides were incubated with primary antibodies for 60 minutes at 37°C. The following primary antibodies were used: anti-phospho-STAT1 (Tyr701) 58D6 (Cell Signaling) and anti-CD68 Clone Kp1 (Abcam) at recommended dilution in blocking agent.
The sequencing data were processed with the Cell Ranger pipeline. Gene-cell matrices were then imported into the SEURAT V2. 3 R toolkit (https://satijalab. org/seurat/) for quality control, clustering and data analysis. The standard workflow was followed, with the filtering out of clear outliers of UMIs (presumed debris and doublets) and filtering according to the percentage of mitochondrial genes (dying cells), data normalization according to total expression, scaling by a factor of 1E4 and logtransformation, calculation of genes with variable expression, which are used for canonical correlation analysis for common sources of variation between datasets, the selection and alignment of statistically significant and relevant canonical correlation vectors for graphbased clustering, non-linear dimensionality reduction by tSNE for visualization, the definition of conserved biomarkers unique to each cluster via differential expression analysis, the manual curation of cluster identity with cluster-specific gene enrichments that are well-documented markers of cell type and the exploration of genes displaying differential expression between individuals.
(I) HEK293T cells were transfected with a constant amount of wild-type USP18 (WT) together with various amounts of the different variants of ISG15 (WT, c. 284del, or c. 83T>A). Cell lysates were analyzed by western blotting with the indicated antibodies. (J) Epstein-Barr virus (EBV)-transformed B cells from a control donor or P1 were stimulated with 1,000 U/mL IFN-α2b for 24 h. Cell lysates were analyzed by western blotting with the indicated antibodies; data from a representative experiment are shown. (K) hTert-immortalized fibroblasts from a control or an ISG15-deficient patient were transduced with luciferase, WT ISG15, or c. 310G>A ISG15 and sorted. These fibroblasts were treated with 1,000 U/mL IFN-α2bfor 12 h, washed with PBS, and left to rest for 36 h, after which relative mRNA levels were determined for MX1, three times for each individual, with technical triplicates; a representative experiment is shown.
Cell lysates were analyzed by western blotting with the indicated antibodies; data from a representative experiment are shown. (D) WT and ISG15-deficient endothelial cells were stimulated with 1,000 U/mL IFN-α2b for 12 and 24 h. Relative mRNA levels for IFIT1, MX1, OAS1, and CXCL10 were determined by qRT-qPCR. Bars represent the mean ± SEM. (E) Representative H&E staining and staining for pSTAT1 (purple) and CD68 (brown) immunohistochemistry (IHC) results for skin biopsies from P1 and a healthy control. Original magnification: 10× (a and e), 40× (b-d, f, and g). Arrows indicate pSTAT1 + cells. Asterisks indicate CD68 + cells (macrophage infiltration).
ACKNOWLEDGMENTS We would like to thank  Ludovic Debure  and  Dr. Thomas Wisniewski  of the  New York State Center of Excellence for Alzheimer's Disease  for their technical assistance with the SiMoA assay. This research was supported in part by the  Intramural Research Program  of the  National Institute of Allergy and Infectious Diseases of the National Institutes of Health (NIH). We would like to thank  Rebeca Perez de Diego  for her advice. This work was supported  R21AI129827 , a grant from the  March of Dimes  to D. B. , and  The Helmholtz Association's Cross-Program Initiative on Personalized Medicine , iMed. The  Laboratory of Human Genetics of Infectious Diseases  is supported by grants from the  St. Giles Foundation , the  Jeffrey Modell Foundation , The  Rockefeller University Center for Clinical and Translational Science , grant  UL1TR001866  from the  National Center for Research Resources  and the  National Center for Advancing Sciences (NCATS) ,  National Institutes of Health , the  National Institute of Allergy and Infectious Diseases  ( 5R01AI089970-02  and  5R37AI095983 ), grants from the  Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence  ( ANR-10-LABX-62-IBEID ), the  French Foundation for Medical Research (Fondation pour la Recherche Médicale  [FRM],  EQU201903007798 ) and the  French National Research Agency (ANR)  under the  "Investments for the Future" program  ( ANR-10-IAHU-01 ),  GENMSMD  ( ANR-16-CE17-0005-01 ),  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Paris University , and The  Rockefeller University.
Acknowledgements This study was funded by  EU ERC  CoG -682387 REVOLINC and by the  Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission or of the IAEA or FAO. We thank the  Institute of Research and Development (IRD) , the  Vectopole Sud network  (http://www. vectopole-sud. fr/)and the  French Agricultural Research Centre for International Development (CIRAD)  for providing the infrastructure needed for pest insect experimentation. We thank  J. Eapen  and  M. Vimal  from  Tagros  company for providing the pyriproxyfen active ingredient. We specially thank  Annick Rakotomahefa ,  Bernadette Tchicaya  and  Adeline Ségard  (CIRAD), for help with the sorting of the tsetse flies and  Gilles Valette  ( Montpellier University ) for making the HPLC analyses.
Data analysis. Data analysis were carried out with R  55  through Rstudio software version 1. 1. 447 -© 2009-2018 and Microsoft Excel version 15. 20 © -2016. Normality was tested via a Shapiro test and homoscedasticity via a Fisher Snedecor test according to the data to be analysed. Survival analyses were performed with R "survival" package  56. Survival functions of PP treated and untreated flies were compared with the semi-parametric Cox model test and the non-parametric Log-Rank test. To compare averages of two independent samples that did not follow a normal distribution, the non-parametric Wilcoxon Mann-Whitney test was used  57. In order to test relationships between two series of observations that did not follow a normal distribution, we calculated Kendall's "tau" coefficient  58. To determine only one factor effect on more than two independent samples not following the normal distribution, the non-parametric Kruskal-Wallis test was used  59.
Extensions of the analytical normalization methods to accommodate boundary effects have also been proposed  (Mirouze and Weaver, 2010; Mirouze and Storto, 2016). In this article, we evaluate different normalization techniques in a global version of the NEMOVAR ocean data assimilation system  (Mogensen et al. , 2012). Some of the techniques are already documented in the published literature, while others are relatively new. An objective of this article is to compare them in a consistent experimental framework. The organization of the article is as follows. In Section 2, we outline the mathematical problem of estimating normalization factors within the context of the diffusion-based background-error covariance matrix used in NEMOVAR. We review some fundamental theoretical results from the diffusion equation, which provides the starting point from which we derive the approximate expressions of the normalization factors that are evaluated in this article.
Basic notation and statement of the problem The model state variables in the Nucleus for European Modelling of the Ocean (NEMO;  Madec and NEMO team 2008)  are temperature, salinity, the two components of horizontal velocity, and sea surface height (SSH). The state vector x ∈ R N x contains the discrete values of the state variables on the model grid. In NEMOVAR, the control variables that are estimated through data assimilation are temperature, and the unbalanced parts of salinity, horizontal velocity and SSH. The control vector w ∈ R N w contains the discrete values of the control variables on the model grid where N w = N x if all the unbalanced fields are estimated. We consider the following standard form of the background-error covariance matrix  (Weaver et al. , 2005; Mogensen et al. , 2012; Balmaseda et al.
Here, we focus on the three-dimensional variational assimilation system used for the production of ECMWF's Ocean Reanalysis ORAS5  (Zuo et al. , 2019). The observations assimilated in that system contain information about temperature, salinity and SSH, but not horizontal velocity. As a consequence, the unbalanced part of horizontal velocity is unaffected by data assimilation and can be ignored. For this particular case, horizontal velocity is determined entirely from the velocity balance relationship (geostrophy) in K b , which becomes a rectangular matrix since N w < N x. In order to provide flexibility in the shape of the correlation functions, C  can be defined as a linear combination of univariate correlation matrices C  l , l = 1, … , L  (Mirouze et al. , 2016) : C 𝛼 = L ∑ l=1 𝚼 𝛼 l C 𝛼 l 𝚼 𝛼 l , (1) where   l is a diagonal weighting matrix such that L ∑ l=1 𝚼 2 𝛼 l = I, in order to preserve the property that a correlation matrix has 1s on its diagonal.
SUMMARY AND DISCUSSION Accounting for flow-dependent background-error correlations with a diffusion operator requires an efficient method to estimate the diagonal of the diffusion-based covariance matrix in order to compute normalization factors. In this article, we have described several general techniques for this purpose. We have evaluated them in a global ocean configuration of the NEMOVAR data assimilation system. The principal parameters of the diffusion operator are the elements of the diffusion tensor, which are related to the spatial length-scales of the underlying covariance functions. These are key parameters that largely influence the amplitude and spatial structure of the normalization factors. Here, they have been estimated from a climatological ensemble of perturbations from ECMWF's low-resolution ocean reanalysis ORAS5-LR.
ACKNOWLEDGEMENTS This work has been funded by the  Copernicus Climate Change Service  ( C3S ), one of six services of the  European Union's Copernicus Programme , which is implemented by ECMWF on behalf of the  European Commission. It is a contribution to contract C3S_321_INRIA: " Technical preparations for C3S Seasonal Initialization and Global Reanalysis: Enabling an Ensemble of Data Assimilations  for the Ocean". Additional support has been provided by the  French national programme LEFE/INSU. The development of NEMOVAR is a collaborative effort with contributions from numerous people from CERFACS,  ECMWF ,  INRIA  and  Met Office. B. Ménétrier  is funded by the  JEDI  project of the Joint Center for Satellite Data Assimilation. The authors thank two anonymous referees whose comments helped improve an earlier version of the article.
However, the results were validated by simulations in Cooja. In collaborative signal processing, also referred to as Wireless Distributed Computing (WDC), a master node which needs to perform a complex computational task in a limited time frame divides the computational task into a number of subtasks and then assigns these subtasks to some slave nodes (neighboring nodes)  [54]. In  [9] , the authors discussed some of the possible applications of WDC such as image processing and pattern recognition, distributed data storage and database search, Synthetic Aperture Radar (SAR) processing, etc. Energy savings in WDC were demonstrated in this study by a wireless ad-hoc network comprised of a tactical handheld, radio nodes attached to a UAV, or sensor nodes. The findings showed that the reduction in energy consumption of the wireless nodes is achieved firstly by the fact that WDC enables processing within the network which reduces number of bits transmitted over the long backhaul at the cost of computational energy consumption.
However, one drawback with this approach is that does not operate in real time, as leaks cannot be immediately detected whenever they occur. Another reason is that it is difficult to find quiet times in certain areas such as city centers. In  [26] , Karray et al. propose a solution called EARNPIPE which is comprised of a Leak Detection Predictive Kalman Filter (LPKF) and Time Difference of Arrival (TDOA) to detect and locate leaks. The data collected from sensors were filtered, analyzed and compressed locally with the same Kalman Filter (KF)-based algorithm. A laboratory testbed was set with plumbing components and a network was deployed, consisting of nodes composed of Arduino Due board (with an ARM cortex M3 microcontroller inside), FSR sensors used for measuring pressure and Bluetooth for communication.
In this work, the high consumption of the Arduino Due board combined with the power hungriness of Bluetooth communication resulted in high consumption profiles and then a shortened lifetime for the network. The centralized approach adopted for leak detection and localization is another drawback  [12]. Ismail et al. , in [25, 35, 77] , presented the development of a water pipeline monitoring system using low-cost off-the-shelf components. The experimental setup consisted of low-cost vibration sensors such as MPU6050, ADXL335 and MMA7361 sensors for the measurement of vibration occurring along the pipes, an Arduino Uno and an XBEE module for wireless transmission to a centralized decision support system. Their work showed that low-cost off-the-shelf components can be effective for leak detection in plastic pipes.
ESP32 ESP32 is a low-cost, low-power System-On-Chip (SOC) increasingly used in the hobby and research development of connected embedded systems. This chip is widely used in tiny devices embedding Python or its derivatives (MicroPython, CircuitPython, etc. ) for wireless embedded systems driven by a strong community such as Pycom  [82]  or CircuitPython  [83]. This chip, although quite unused in traditional WSN hardware  [44] , has two main advantages: a 32 bit dual-core unit and an Ultra-Low-Power Processor (ULP) for low computation tasks. Last but not least, it presents wide support for conventional Operating Systems but also for more prospective Real-Time Operating Systems such as RIOT  [84] , Zephyr or Zerynth  [85]. From a technical point of view, the ESP32 offers: - Engineered for mobile devices, wearable electronics, and IoT applications, the ESP32 offers advanced power management features such as Ultra-Low Power (ULP) consumption through power saving features including fine-resolution clock gating, multiple power modes, and power scaling  [10].
Leak Detection Algorithm When a leak occurs, there is a transient change in the water pressure, which leads to a drop in the internal pipe pressure and an increase in the pipe surface vibration  [34, 60]. The pipe surface vibrations resulting from the sudden drop in pressure can be detected by an accelerometer. However, since low-cost sensors are used in our case study, their readings might be inaccurate and also be drowned out by noise from the surrounding and operational conditions such as the opening/closing of pumps, valves, etc. For this reason, an appropriate leak detection algorithm is required for processing the measured vibration data to improve on the accuracy of detecting leaks. To maximize leak detection and minimize the number of false alarms without involving any form of centralized computation, we proposed the use of a Distributed Kalman Filter (DKF), which is simply a combination of a local Kalman filter and distributed data fusion, as the leak detection algorithm.
Simulations The implementation and deployment of a WSN incurs cost and it is also time consuming. So, it is important to simulate the operation of a specific design before deploying it. We performed simulations to evaluate the performance of the proposed algorithms. Simulations were carried out in Cupcarbon 4. 2, which is a Smart City and Internet of Things Wireless Sensor Network (SCI-WSN) simulator that is used to design, visualize, debug and validate distributed algorithms for monitoring, e. g. , the collection of environmental data  [98]. It offers two simulation environments; one enables the design of scenarios with mobility and the generation of natural events and the other enables the simulation of discrete events in WSNs. CupCarbon simulation is based on the application layer of the nodes.
It is composed of four modules: a microcontroller, radio unit, sensing unit and a battery  [99]. It also includes a script called SenScript, which allows us to program and to configure each sensor node individually. From this script, it is also possible to generate codes for hardware platforms such as Arduino/XBEE  [99]. This feature makes it suitable for simulating distributed algorithms in a WSN environment and the ability to demonstrate distributed computing in a WSN, since it permits us to write and simulate applications that will be implemented on real sensor nodes. In addition, in CupCarbon, the energy consumption can be calculated and this allows us to represent the detailed energy profile of each sensor node. This allows us to test the feasibility and realistic implementation of a network before its real deployment.
One thousand acceleration values generated by the natural event were used in the simulations. Sensors 2020, 20, x FOR PEER REVIEW 19 of 39 e. g. , the collection of environmental data  [98]. It offers two simulation environments; one enables the design of scenarios with mobility and the generation of natural events and the other enables the simulation of discrete events in WSNs. CupCarbon simulation is based on the application layer of the nodes. It is composed of four modules: a microcontroller, radio unit, sensing unit and a battery  [99]. It also includes a script called SenScript, which allows us to program and to configure each sensor node individually. From this script, it is also possible to generate codes for hardware platforms such as Arduino/XBEE  [99]. This feature makes it suitable for simulating distributed algorithms in a WSN environment and the ability to demonstrate distributed computing in a WSN, since it permits us to write and simulate applications that will be implemented on real sensor nodes.