dataesr/scientific-paragraphs-categorization huggingface dataset:
5. 9-r16)  49. Variant calls were determined using the GATK Unified Genotyper 50 using the parameter and quality thresholds described in the supplementary information of a previous paper  30. The resulting VCF files were then combined with the VCF files downloaded from the Pf3k project and filtered to remove non-variant sites using VCFtools  51. To determine whether any previously implicated drug resistance SNPs were associated with PPQR, we analyzed the SNPs in the genes listed in the Supplementary Data 2. Samples were divided into two categories based on their AUC values. Samples with an AUC ≤ 35 were considered PPQS and samples with AUC > 35 were considered PPQR. Based on these categories, we used the software package PLINK (version 1. 8)  52  to determine whether any SNP was associated with PPQR.
P38 is suspected to be involved with gamete fertilization  53  and under some level of immune-mediate balancing selection  54  , but little is known about the functions of the other genes. None have been implicated in drug resistance studies. Identity by descent analysis. The relatedness between two strains was calculated as the proportion of the genome inherited from the same ancestor, or identical by descent. Relatedness was calculated from SNP data using a hidden Markov model described in  31. Networks of relatedness were generated using NetworkX (https:// github. com/networkx/networkx), a Python package for the creation, manipulation, and study of complex networks, and visualized using Gephi  55  , an open-source software for network visualization and analysis. Statistical analysis. Calculations of EC 50 , Student's t-test, one-way analysis of variance and correlations were performed using GraphPad Prism version 7 (GraphPad Software La Jolla, CA).
Acknowledgements We thank  Courtney Edison  for slide reading and subcloning the KH001_053 isolate. The computations in this paper were run on the Odyssey cluster supported by the  FAS Division of Science ,  Research Computing Group at Harvard University. N. J. W. , C. J. W. were supported by  Bill and Melinda Gates Foundation Global Health  Grant Number  OPP1040463 , which also funded culture adaptation of the parasites described herein. Additional support was provided to DFW by the  Bill and Melinda Gates Foundation Global Health  Grant Number  OPP1053604. This document is an output from a project of the  Tracking Resistance to Artemisinin Collaboration  funded by the  UK Department for International Development (DFID)  for the benefit of developing countries. However, the views expressed and information contained in it are not necessarily those of or endorsed by  DFID , which can accept no responsibility for such views or for any reliance place on them.
Data availability. WGS data that support the findings of this study have been deposited in NCBI bioproject with the PRJEB22985 accession codes (https://www. ncbi. nlm. nih. gov/bioproject/414203) or was provided by Pf3k project (www. malariagen. net/pf3k)  29. TRAC study samples are made available to the malaria research community through the worldwide antimalarial research network (WWARN): http://www. wwarn. org/news/news-articles/trac-study-samples-bemade-available-malaria-research-community. All other data supporting the findings of this study are available within the article and its Supplementary Information files, or are available from the authors upon request.
The relationship between oats, the growth of gut microbiota and short-chain fatty acid (SCFA) production based on data from human intervention trials Intestinal gas production SCFAs and no changesfaeces b-galactosidase and urease -↓ Rectal level of PGE 2no significant difference © 2018 The Authors. Nutrition Bulletin published by John Wiley & Sons Ltd on behalf of British Nutrition Foundation Nutrition Bulletin, 43, 358-373.
05 nm). The temperature is obtained using a HP34970A data logger (Keysight Technologies, LEs Ulis, France) and a bundle of five type N thermocouples (purchased from Correge, Chaignes, France) distributed along the length of the tubular furnace. The thermocouples' accuracy is given by the manufacturer as ±1. 5 • C from room temperature to +375 • C and as ±0. 004*T between 375 • C and 1000 • C, so the temperature accuracy is on the order of ±2. 8 • C at 700 • C. Thermocouple data are used to get the temperature profile along the furnace at each time and thus to extrapolate the temperature at the location of each FBG under calibration. The non-linear behavior of the Bragg wavelength with temperature is clearly visible while the evolution of the thermo-optic coefficient with the Bragg wavelength explains the difference between each FBG.
II. MATERIAL AND METHODS HEARTEN platform is an mHealth system which enables the active involvement of several actors in the management of HF patients (healthcare professionals, caregivers, physical activity experts, nutritionists, psychologists). The key actions and components that this integrated platform relies on are: (i) specific biomarkers related to the HF, (ii) a non-invasive approach for detecting the specific HF biomarkers, (iii) development of saliva/breath biosensors and biosensor measurement unit (BMU), (iv) utilization of sensors for monitoring the blood pressure, the body temperature, the physical activity, the heart and respiratory rate, the electrocardiogram activity and the weight, (v) development of communication tools (mobile application, web application) for the involved ecosystem actors, (vi) creation of a software tool (Knowledge Management System) for the data analysis and decision support, based on artificial intelligence and data mining techniques, (vii) development of a Dynamic Patient.
Based on the available data from previous studies reported in literature  [11] , a set of potential biomarker compounds have been selected and evaluated during the HEARTEN project. For the breath biomarkers analysis, a three step process was followed: (i) setup and quality control of adequate analytical methods for the reliable identification and quantitation of the selected VOCs in trace (nmol/L) concentrations, (ii) in depth analysis on potential confounding variables onto marker concentrations in the patient cohort, (iii) definition of concentration ranges and variations of selected biomarkers in patients with HF in comparison to normal/ healthy patient cohorts. Regarding the saliva biomarker analysis, a standard operating procedure was followed. Briefly, saliva was collected by asking the patient to roll a Salivette polyester swab in mouth for 2 minutes.
C. Biosensors measurement unit The biosensors are connected to the Biosensor Measurement Unit (BMU), which provides the following functions: (i) electrical power to operate the biosensors, (ii) interface circuits for the readout of the output signal of the biosensors (analog-to-digital signal conversion), (iii) digital interface with the smartphones running the HEARTEN mHealth apps, which also controls the unit. Data is transferred between the BMU and the smartphones. The communication is based on the Bluetooth Low Energy (BLE) standard. The diagram of the hardware of the complete BMU is presented in Fig. 2.
E. Mobile Application and Communication Tools The communication between the HEARTEN ecosystems actors is enabled through the mobile application (Android and iOS platforms are supported). Data coming from the sensors and biosensors through the bluetooth channel are collected by the mobile phone, aggregated and sent for storage to the cloud platform. For the communication layer, the Bluetooth Health API is used, which implements the Bluetooth Heath Profile. This profile is a wireless interface specification, which is designed to facilitate transmission and reception of Medical Device Data, using the ISO/IEEE 11073-20601 Data Exchange Specification protocol.
F. Knowledge Management System The HEARTEN Knowledge Management System (KMS) provides multiple functionalities to the ecosystem actors. It is a valuable tool that includes advanced data-driven techniques incorporated with expert-knowledge techniques towards effectively assessing the HF patient condition and enhancing patient adherence. The KMS enables users with advanced expert-knowledge techniques to effectively assess and exploit real patient data  [12] ,  [13]. The set of the different modules of the KMS were identified by studying the nature of the HF disease, the requirements of the users and the multi-disciplinary data that are available by the clinical sides. The development of each module is accomplished as a separate subsystem of the KMS with its own interface and RESTfull webservices.
The KMS modules are using their own computational or algorithmic data processing to provide: (i) measurable results and, (ii) knowledge to the expert users. Knowledge extraction is performed by applying data mining methods on the collected data of the pilot phases. The HEARTEN KMS consists of the following modules: (i) NYHA class module, which detects the change of patient condition, (ii) Association module, which provides the experts the ability to perform profound analysis and research, originated from patient data and to discover hidden associations, (iii) Statistics module, which allows the experts to apply tools for finding and explaining dependencies that are observed frequently within the collected data and perform hypotheses testing, (iv) Adherence risk module, which estimates the adherence profile of the patient and allows the experts to identify patients risking to be non-adherent (v) Treatment adherence module, which evaluates whether the patient is adherent or not to the guidelines provided by the experts with regard to the medication, the diet and the physical activity, (vi) Score module, which calculates several acknowledged risk scores (vii) Event prediction module, which notifies the experts in case of expected potential onset of adverse events (relapses and mortality), (ix) Alerting mechanism module, which delivers critical alerts and messages regarding the patients' condition when needed.
The KMS was evaluated using a dataset of 72 patients collected by the clinical center of the Universita Di Pisa (UNIPI), the Servicio Andaluz de Salud (SAS) and the 2nd Department of Cardiology, University Hospital of Ioannina (UHI). The features collected for each patient can be grouped to the following categories: (i) General Information, (ii) Allergies, (iii) Medical Condition, (iv) Drugs, (v) Clinical Examinations, (vi) Adherence, (vii) Score, (viii) Sensor data, (ix) Biomarkers. For the NYHA Class, the Adherence Risk, the Event prediction and the Treatment Adherence (Medication adherence sub-module), a three stage approach was followed; preprocessing, feature selection and classification. In the first stage, features with more than 60% of missing values are excluded, since imputation of missing values cannot be applied due to the nature of the data.
H. Dynamic Patient Communication Protocol The DynPCP uses predefined Patient Communication Protocols (PCPs), the outcomes of KMS and the patient's data, and generates patient specific messages and notifications which are provided to the corresponding actors of the HEARTEN platform. The messages and notifications are sent either as PUSH notifications to the HEARTEN mobile application (via the Google Firebase platform) or as SMS texts to the recipients' mobile phone. DynPCP monitors the delivery and read status of each message, notification and alert. The DynPCP is developed on PHP framework Symfony version 3 and it is installed on an Nginx web server running on the dedicated Virtual Machine for DynPCP. DynPCP MySQL database is installed on HEARTEN's Database Server. Due to Symfony PHP framework development process, DynPCP is consisted by bundles, which can be treated as system modules.
I. Cloud infrastructure and Databases The HEARTEN platform is hosted in a cloud infrastructure. The technology that was selected for the development of all systems and services can be hosted in any cloud infrastructure. Each server of the HEARTEN platform resides in a Virtual Machine (VM) thus the final system architecture is composed of 6 VMs as follows: (i) the KMS server, (ii) the Web Application server, (iii) the DynPCP server, (iv) the MySQL database server, (v) the MongoDB server and (vi) the REST server. Communication between the VMs is private and only the KMS, the DynPCP, the Web Application and the REST services can be publicly accessed through secure SSL/TLS protocol.
A. Database The EEG data of 3 epileptic patients acquired in the Université Libre de Bruxelles, Hôpital Erasme (agreement of local ethical committee number P2015/242) were analyzed. Patients were studied at baseline (T0) and about 4-6 weeks after a change of the AED regimen aimed to reduce IED (T1). Table  III  shows a summary of clinical data, diagnoses, AED at T0 and T1, and IED quantification on awake and sleep EEG using a spike-wave index (SWI) during sleep and an EEG grade that assesses the diffusion of IED, as previously proposed  [33]. It was concluded from the visual analysis and quantification of the SWI during NREM sleep, performed by an experienced neurophysiologist (co-author P. V. B. ), that patient BE001 did not respond to the change of AED (a benzodiazepine), and that patients BE003 and BE004 showed impressive EEG improvement after introduction of levetiracetam.
We made no use of the preestablished EEG frequency bands, which may sometimes slightly differ between authors, but considered the whole frequency content of the signal instead, with no a priori band separation. The complexity measures presented here can be applied to a wider range of problems. For instance, in epilepsy, the time-varying versions are suitable for the prediction of A weakness of this study is that data of only 3 patients were analyzed. Our preliminary results will be expanded in a larger cohort of patients.
Data and Image Processing Raw images were processed for SFI, ½HbO 2 , and [HHb] as outlined in Ghijsen et al. 38  and Cuccia et al. 18, 19  Single raw images were used to simultaneously demodulate two spatial frequencies using algorithms originally described in Vervandier and Gioux. 36 In this technique, a Fourier transform was applied line-by-line to raw data to calculate a spatial frequency spectrum of each column vector within a given raw image. The frequencydomain data were then subdivided into DC and AC component spectra using top-hat window functions. The DC and AC demodulated images were then computed by performing line-by-line inverse Fourier transforms. Compared with twofrequency, three-phase SFDI, this decreased by a factor of 6 the amount of raw data needed to decouple μ a and μ 0 s , thus allowing for real-time imaging.
Five-Minute Arterial Occlusion Experiment A 5-min arterial occlusion was performed on a healthy 29-yearold male subject's left hand while being imaged with cSFDI. During this imaging procedure, the dorsal left hand was imaged while a full arterial occlusion was applied to the brachial artery using a pneumatic cuff capable of near-instantaneous inflation to 210 mm Hg (E20 Rapid Cuff Inflator, Hokanson Inc. ). The duration of imaging consisted of 3 min of baseline (no occlusion), followed by 5 min of arterial occlusion, and then 3 min of postrelease. Two-dimensional (2-D) maps of ½HbO 2 , [HHb], and SFI (both wavelengths) were reconstructed for each time point for a total of 10,560 time points (11 min × 60 s∕min ×16 frames∕s). These data were, in turn, used to produce tMRO 2 maps. Time-series data were then obtained from ROIs and used to analyze physiology of the underlying tissue.
Intralipid Hemoglobin Titration Experiment Figure  3 (b) shows the results from the hemoglobin titration experiment. The y-axis shows total hemoglobin concentration obtained with cSFDI, and the x-axis shows results obtained using a validated commercial SFDI device (OxImager Rs, Modulated Imaging, Inc. , Irvine, California). Both axes are in arbitrary units due to the fact that the OxImager Rs uses proprietary software that does not specify units. These data demonstrate response linearity in terms of total hemoglobin recovery when compared with a standard commercial system. The accuracy of both technologies (SFDI and cSFDI) in terms of chromophore quantitation has been validated at length in previous work. 18, 38 This experiment is only intended to demonstrate linearity between the two instruments.
Time-series data were extracted from the smaller 80-pixel × 80-pixel ROI to improve sensitivity to dynamic tissue-level changes. This is because spatial variation and differential temporal dynamics across the tissue lead to diminished signal quality when large ROIs are selected; the changes in one region are confounded by those in another. See Appendix for a description of the video included in the online component of the article. subtracted from the pre-and postocclusion measurements to account for this offset. Baseline SFI (SFI bl ) is inserted into Eq. (  7 ) to calculate α. Postrelease SFI (denoted SFI) is inserted into Eq. (  4 ). Absolute and relative metabolic rates of oxygen consumption were then determined by inserting the extracted parameters derived pictorially in Fig. 5 (a) into Eqs.
(  4 ), (  5 ), and (7). The images along the top of Fig. 6  show tMRO 2 maps in millimoles per liter per second at different times throughout the arterial occlusion protocol. The plot beneath the images shows the same SFI time-series data presented in Fig. 6  and is provided as a reference for the tMRO 2 image sequence. The numbers at the top left of each image correspond to the numbers in the SFI plot. Image 1 is taken from the entire baseline duration from 0 to 180 s (see Sec. 2 for details). Images 2 and 3 were calculated from the middle to end of the occlusion and correspond to 260 and 480 s, respectively. Both images 2 and 3 required a linear fit to [HHb] as described in Sec. 2. Images 4 and 5 were derived from the postrelease phase and correspond to 490 and 600 s, respectively. The tMRO 2 images demonstrate a progressive reduction in tissue metabolic rate during the occlusion.
Acknowledgments The authors gratefully acknowledge funding provided by the  NIH NIBIB Biomedical Technology Research Center   LAMMP P41EB015890 ,  NIH  (Grant Nos. :  R21-EB014440 ,  K01DK-093603 , and  R01-HD065536 ), the  Military Medical Photonics Program, AFOSR  Grant No. FA9550-08-1-0384 , ICube, and the  Arnold and Mabel Beckman Foundation. Michael Ghijsen  was supported by the  NIH NHLBI Ruth L. Kirschstein National Research Service  Award  5 F30 HL132481-02 , the  BEST IGERT program  funded by the  National Science Foundation   DGE-1144901 , and the  Medical Scientist Training Program  funded by the  National Institute of Health   T32-GM08620.
The chemical structure is probed using 19 F-NMR to estimate the global ionic exchange capacity (IEC) and IR spectroscopy in the attenuated total reflectance (ATR) mode to look for chemical heterogeneities at the anode and cathode sides of the aged sample. Information on the different water populations in the composite structure is given by the analysis of the 1 H-NMR spectrum and the global water uptake is measured through the sorption isotherm. Finally, we use pulsed-field gradient (PFG)-NMR to characterize the water mobility and diffusion at the micron scale  (13). To this point, our goal is not to study the chemical degradation mechanisms but rather to elaborate an experimental approach able to deliver a set of data indicative of membrane degradation. The collection of structural, sorption and transport data on PEM membranes represents a long and tedious process and necessitates quite a lot of resources (analytical instruments and access to large instruments such as synchrotrons  (14, 15) ).
PFG-NMR The water self-diffusion coefficients were measured using a stimulated-echo sequence (PGSTE) with unipolar gradients and calculated from a plot of the observed signal attenuation as a function of the applied gradient strength g. The experiments were performed with a pulse time δ = 1. 0 -1. 6 ms, a diffusion delay Δ = 5 -10 ms and a gradient strength 20 -700 G/cm. The water self-diffusion coefficient D s was obtained from a fit of the experimental data using the Stejskal -Tanner equation  (21). The error on diffusion measurements is estimated to be on the order of 5%, the main source of errors being the gradient calibration and the control of both temperature and water content.
This complex behavior proves that two water populations contribute to the low amplitude resonance line of the 1 H spectrum (populations 2. 1 and 2. 2). The PFG-NMR diffusion experiments help to clarify the situation. Figure  1-c   (13, 20, (27) (28) (29) (30). As already observed in the relaxation data, the behavior of population 2 is more complex and the fit to the data must be made with two exponential components. Again two water populations are detected. Population 2. 1 diffuses with a diffusion coefficient which is about half the value of population 1. This is coherent with the two similar relaxation times, the lower value of the diffusion coefficient being due to confinement in the porous PTFE. Population 2. 2 is diffusing very fast, its diffusion coefficient being approximately one third of the coefficient of bulk water at the same temperature (0.
FTIR-ATR spectroscopy The FTIR investigation was carried out in order to measure exclusively the chemical degradation of the ionomer part of the reinforced membrane. The ATR mode was used to characterize the PFSA outer layers on both sides of the reinforced membrane and in different areas to ensure reproducibility. The data are plotted in the 4000-650 cm -1 frequency range on Figure  5 -a. The spectra were normalized to the asymmetric CF 2 stretching mode (1145 cm -1 ). As expected, they exhibit the characteristic signal of the LSC -PFSA type ionomer. The assignment is detailed in Table  1  based on literature  (37) (38) (39) (40). All studies agree on the absorption bands assignment except for the peak located at ~ 980 cm -1     whose attribution is still open to discussion. Some authors  (39, 41, 42)  attributed this peak to the stretching mode of C-O-C group close to the main chain while others assigned it to CF stretching mode of the CF 2 -CF(CF 3 )-group  (38, 43).
A Kinetic Model Identifies Rhythmically Transcribed and Rhythmically Degraded Transcripts from Total RNA-Seq. The temporal accumulation of mRNAs in cells is governed by many processes including transcription, (cotranscriptional) splicing, polyadenylation, mRNA export, and mRNA degradation. In the context of 24-h diurnal rhythms, we can assume that mRNA levels are for the most part determined by the kinetics of transcription and mRNA degradation, since the other RNA processing steps occur on faster timescales  (28, (31) (32) (33). Here, we combined a kinetic model for mRNA accumulation with time course measurements of pre-mRNA and mRNA (Fig. 1A ) simultaneously quantified from the same biological samples, using recently published RNA-seq data from 48 total RNA time points collected along a daily cycle with 2-h resolution in mouse liver (8) (SI Materials and Methods and Dataset S1).
Moreover, the peak time of rhythmic degradation, whose precise estimation depends on the half-life, can still be placed within a 6-h window, from being antiphasic to that of mRNA accumulation, to peaking 6 h after peak mRNA accumulation. To assess whether half-life could be reliably identified, we implemented the profiling likelihood (PL) method (37) (SI Materials and Methods, Fig. S2F , and Dataset S1). We thus conclude that that despite the identifiability issue for kinetic parameters in M3, the model selection was overall able to detect even small temporal changes in rhythmic degradation (Fig. S2B ). Given these validations, we then applied our model to the total RNA-seq measurements and retrieved the optimal model as well as the estimated parameters of 12,216 expressed genes in liver (SI Materials and Methods and Dataset S1).