dataesr/scientific-paragraphs-categorization huggingface dataset:
In  [111] , a two-tier network architecture is considered. At the lower tier, multiple sensor devices worn by a single patient form a WBAN, by employing the CSMA/CA access mode of IEEE 802. 15. 4. At the upper tier, multiple WBAN coordinators (corresponding to multiple patients) located within a specific area (e. g. , a hospital ward), communicate with an Access Point through WiFi. End-to-end packet delay and access time has been modeled as a function of the number of coexisting WBANs. An extension of this work, in  [112] , has introduced service differentiation to prioritize high-rate data streams (e. g. , EEG data) by providing them contention-free access, whereas CSMA/CA access is given to low-rate streams (e. g. , ECG data). In  [113] , the authors discuss the feasibility of employing a hybrid network based on WiFi and WiMAX technologies as the access communication network in an M2M system for mHealth services.
g. , critical data such as blood pressure, pulse rate and heart rate) or normal priority (e. g. , ECG signal) and forwards them towards the healthcare provider through an heterogeneous WiFi/WiMAX access communication network. The access technology is selected depending on the patient's location, considering that WiFi hotspots cover only specific (mainly indoor) locations and WiMAX has a wider (outdoor) coverage. In addition, two types of connections are provided by the network operator: (i) low-cost reserved connections, allocated to patients for given amounts of time (e. g. , weeks); and (ii) high-cost on-demand connections, employed when the available bandwidth for reserved connections is not enough to cover the traffic load. The authors approach this mHealth scenario from the service provider's side, who has to buy in advance a certain number of reserved connections from the network operator to serve a given number of patients.
The definition of a standardized data structure is an important step towards the integration of the medical data measured by the WBAN sensors with various mHealth information systems for monitoring or administrative purposes, belonging to hospitals, individual care-givers, home-care, etc. Second, they implement a testbed of an M2M healthcare application for the remote monitoring of patients suffering from heart problems. The patient is equipped with a WBAN formed by a number of wearable sensors, a Global Positioning System (GPS) device and a PDA. The PDA aggregates the sensory and geolocation data, as well as any additional information inserted manually by the patient, and plays the role of the M2M gateway. On the one hand, it employs Bluetooth technology to communicate with the WBAN nodes and, on the other hand, it has mobile ADSL capabilities to forward the data to the remote server located at a hospital facility.
Based on the observed results, the authors provide guidelines for the design of an adaptive protocol that switches between the two long-range technologies depending on the scenario: (i) WiFi is the recommended technology, when available, especially in emergency situations due to the low data latency. In these cases, the GPRS interface should remain on but at an idle state, employed only to receive incoming calls if needed; (ii) if the WiFi connection is not available, the WiFi network interface should be switched off completely and GPRS should be employed for communication. A prototype M2M solution built on Android mobile devices is presented in  [120]. At the patient's side, wearable sensors are employed to measure physiological data such as ECG and oxygen saturation. The sensor nodes are connected to M2M nodes, which are, in turn, wirelessly connected to the Internet through the M2M gateway.
The lower tier consists of two systems: (i) a patient-worn fabric belt, which integrates the medical sensors and is equipped with a Bluetooth transceiver; and (ii) the ambient wireless sensors that form a ZigBee network and are deployed in the patient's surroundings (e. g. , in the patient's home or in a nursing house). In the middle tier, an ad hoc network of powerful mobile computing devices (e. g. , laptops, PDAs, etc. ) gathers the medical and ambient sensory data and forwards them to the higher tier. The middle-tier devices must have multiple network interfaces: Bluetooth and ZigBee to communicate with the lower tier and WLAN or cellular capabilities for connection with the higher layer. Finally, the higher tier is structured on the Internet and includes the application databases and servers that are accessed by the healthcare providers.
Although it provides an end to end solution that employs M2M communication for monitoring patients with PD, its aim is to design a control system for a subcutaneous infusion pump that administers the exact required drug dose according to the patients' level of activity without focusing in communication issues. This system is composed of the following components: (i) an intra-oral electronic drug delivery device with miniaturized, non-invasive and removable design; (ii) an external pump that delivers higher amounts of drug; (iii) a WBAN to gather information on the user environment to detect blockades; (iv) a telecommunication and services infrastructure to analyze and transfer data exchanged between the user and the automated system; and (v) a remote care unit for patient supervision. CAALYX-MV: Complete Ambient Assisted Living Experiment-Market Validation  [125]  goal is to provide an end to end solution that is focused on improving the elder's quality of life.
The proposed solution is composed of: (i) a home system capable of monitoring and controlling social and health status of elder people and providing them with some tools and services to support their daily activities; (ii) a roaming system that comprises a smart textile shirt able to measure specific vital signs, detect falls and communicate emergencies; and (iii) a care system for the monitoring of individuals by family, caretakers and health services. All sensors in the WBAN are wearable, measure different parameters such as motion, blood pressure and heart rate, and communicate using Bluetooth links with a mobile phone. The sensory data are sent through standard low-cost networking equipment to a GPS-enabled smart phone (3G/UMTS) that runs a completely autonomous software application. The application continuously analyzes sensor data in order to identify problematic conditions and promptly alert the care system.
Data extraction and exclusion criteria The bibliography search yielded a total of 473 articles. The articles retrieved by the Medline/PubMed search were evaluated based on their title and abstract. Studies including patients diagnosed using Rome Ⅰ, Ⅱ or Ⅲ criteria were included. Individual case reports, abstracts and studies published in languages other than English were excluded. Additionally, relevant publications cited in reviews, but which were not captured by the search strategy, were also considered. After selection, 175 papers were analyzed and used to write the review. The outcome measure was the description of ovarian-dependent changes in gut immunity, gut sensori-motricity, gut permeability, or pain processing, which were compared between IBS patients and healthy controls.
These results suggest that gender differences in the effects of sex hormones on the autonomic systems and hypothalamo-pituitary axis (HPA) cannot account for differential responses to stress in IBS. However, the parameters of autonomic response assessed in these studies were indirect (e. g. , skin conductance and heart rate) and stress hormones levels were determined only in plasma and saliva but not in tissue. Also, methodological confounders such as menstrual phase of the cycle in female patients are not always factored in the data analyses or the clinical designs. Hence, one cannot rule out the possibility of subtle hormone level changes in specific windows of time, or that molecular and cellular changes occur at the tissue level. Indeed, in rodent models, where the experimental designs can be controlled in a tighter manner and where invasive methodologies (e.
ACKNOWLEDGMENTS This research project was supported by grants from the  National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIAID)  grants  AI46706  and  AI057159 , a component of award  U54 AI057159  (to G. B. P. ), and  AI042347  and  HHMI  (to M. K. W. ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institute of Allergy and Infectious Diseases  or the National Institutes of Health. The following reagents were obtained through  BEI Resources, NIAID, NIH : monoclonal anti-Shiga toxin 1 subunit B (produced in vitro) (similar to 13C4) (catalog no. NR-844; BEI Resources), and polyclonal anti-Shiga toxin 2 subunit B (IgG, rabbit) (catalog no. NR-9352; BEI Resources). We thank  Anne Kane  of  Tufts University's Medical School  for providing Stx1 and Stx2 holotoxins and Stx1b protein.
ACKNOWLEDGMENTS We greatly acknowledge  Fabienne Agasse  for her critical reading of the manuscript. The Humbert and  Saudou laboratories (Patrick Pla ,  Frédéric Saudou ,  Sandrine Humbert ) are funded by grants from  Agence Nationale pour la Recherche (ANR) ,  Association pour la Recherche sur le Cancer (ARC) ,  Fondation pour la Recherche Médicale (FRM). Denis J. David  is a recipient of the  Young Investigator Award  from  The Brain & Behavior Research Foundation  and he is currently receiving investigatorinitiated research support from  Lundbeck ,  Roche , and  Servier.
No diagnosis was based on lumbar puncture during the inclusion period. Of the 40 patients, 4 died and 11 had a Glasgow Coma Scale score of Ͻ10, including 4 with a brain hematoma. Finally, 25 patients fulfilled the inclusion criteria; demographic and clinical data are summarized in Table  1. The etiologic work-up included CT angiography at admission and digital subtraction angiography (within the following 12 hours). Of the 25 patients, 15 presented with a ruptured intracranial aneurysm, which was successfully treated by coils within 48 hours after clinical onset. No patient was ventilated or received high-inspired oxygen. From 14 to 16 days (mean, 14. 75 days) after clinical onset, additional brain imaging was systematically performed, including 3T MR imaging (Achieva; Philips Healthcare) and repeated unenhanced CT (Somatom Sensation; Siemens) (median delay, 3 hours before MR imaging; range, 2-5 hours).
Image Analysis First, MR images of healthy volunteers were randomly interspersed among the patient images. MR imaging interpretation was conducted independently by 3 blinded neuroradiologists with 25 (reader 1), 10 (reader 2), and 3 (reader 3) years of experience in neurovascular imaging. For the detection of subacute SAH and IVH in each subject, the readers assessed each set of images (2D FLAIR, 3D FLAIR, 2D T2*, SWI, and DIR) separately in a randomized order. The independent sessions of image interpretation were 1 week apart to avoid recall bias. The readers were blinded to clinical data (patient with SAH or healthy volunteer) and to the results of other imaging modalities. Imaging criteria for hemorrhage at the subacute stage were the following: for CT images, increased attenuation values compared with the normal CSF; for DIR and FLAIR images, increased signal intensity compared with the normal CSF; and for T2* and susceptibility-weighted images, decreased signal intensity compared with the normal CSF.
The results of this study (supplementary figure  5 ) show that Hc1 determined by this method are independent of the aspect ratio of the sample. Furthermore, the samples used all had similar lc/la ratios (see Supplementary Table  1 ), and so any correction would not give any systematic errors as a function of x. Calculation of condensation energy. The condensation energy can be calculated from the specific heat using the relation E cond = ∞ 0 [Cs(T ) -Cn(T )] dT. ( 6 ) To calculate this we first measured a sample of BaFe2(As1-xPx)2 with x = 0. 47, using a relaxation technique in zero field and µ0H = 14 T which is sufficient at this doping to completely suppress superconductivity and thus reach the normal state. We used this 14 T data to determine the phonon heat capacity and we then subtract this from the zero field data to give the electron specific heat of the sample.
Acknowledgements We thank  Igor Mazin  and  Georg Knebel  for useful discussions, and  A. M. Adamska  for experimental help. This work was supported by the  Engineering and Physical Sciences Research Council  (Grant No. EP/H025855/1 ),  EuroMagNET II  under the  EU  Contract No. 228043 ,  National Physical Laboratory Strategic Research Programme , and  KAKENHI  from  JSPS.
METHODS: A prospective, non-comparative, and nonrandomized trial was performed and registered on Eudra CT database (2011-002953-80) and on www. trial. gov (NCT01398098). All patients had a validated indication for colonoscopy and a preparation using sodium phosphate (NaP) tablets. The day of the procedure, patients took 4 tablets with 250 mL of water every 15 min, three times. The gastric volume was estimated every 15 min from computed antral surfaces and weight according to the formula of Perlas et al (Anesthesiology, 2009). Colonoscopy was performed within the 6 h following the last intake.
MATERIALS AND METHODS A prospective, non-comparative, non-randomized open label trial was performed. Full ethical approval for the study was obtained from the local institutional review board. The study was approved by an Independent Ethics Committee, (CPP "Ile-de-France III"), the French competent health authorities (the AFSSAPS, "Agence française de sécurité sanitaire du médicament et des produits de santé'', which became Agence Nationale de Securité du Médicament et des Produits de Santé, ANSM) and was registered on EUDRA CT database (Eudra CT 2011-002953-80) and on www. trial. gov (NCT01398098). Patients undergoing total colonoscopy for screening or surveillance colonoscopy from November 2011 to May 2012 were selected for inclusion in the study. Signed informed consent was always required before any study procedure.
Statistical analysis Descriptive statistics (group size, mean, standard deviations, median, ranges, and 95%CI) were used to report patients' baseline characteristics. The analyses of primary and secondary objectives were conducted on the intention to treat (ITT) population. Comparisons between ultrasonographic examinations were performed by the Fisher exact test, the χ 2 test with Yates correction, or the Wilcoxon test when appropriate. A P value of < 0. 05 was considered statistically significant. ± 18. 2 mL, P = 0. 15). At 105 min, more than half of patients (57%) had a residual volume lower or equal to their value at baseline. This proportion constantly increased up to 87% at 150 min (Figure  5D ). Data recorded during the study did not highlight particular safety concerns, and no adverse events requiring to treatment discontinuation were reported.
Most SGWB models predict backgrounds much lower than these data were capable of detecting. However, this work sets the stage for the Advanced LIGO and Advanced Virgo detectors, which are expected to achieve four orders of magnitude improvement in sensitivity to the GW energy density at 100 Hz and be sensitive to frequencies down to 10 Hz. Having found no statistically significant evidence of a stochastic gravitational-wave signal, we present the best constraints to date on the energy density of the SGWB from LIGO and Virgo. Data. -Previous to this analysis, the best limits on the SGWB from LIGO and Virgo data were obtained using 2005-2007 data  [34] [35] [36]. For this study, we use data from the LIGO observatories in Hanford, WA (H1) and Livingston Parish, LA (L1)  [37]  as well as the Virgo observatory in Cascina, Italy (V1)  [38].
The H2 observatory in Hanford, WA was decommissioned before these data were collected. LIGO data ran from July 2009-October 2010. Virgo data spanned July 2009-January 2010 and July 2010-October 2010. Method. -The SGWB energy density spectrum is defined as Ω GW (f ) = f ρ c dρ GW df , (1) where f is frequency, ρ c is the critical (closure) energy density of the universe, and dρ GW is the gravitational radiation energy density contained in the range f to f + df  [39]. For the LIGO and Virgo frequency bands, most theoretical models are characterized by a power law spectrum so we assume the gravitational-wave spectrum to be  [35, 39, 40] : Ω GW (f ) = Ω α f f ref α. (2) Here, f ref is an arbitrary reference frequency (see Table  I ). Ω α is a constant characterizing the amplitude of the SGWB in a given frequency band.
Acknowledgments. -The authors gratefully acknowl-10 edge the support of the  United States National Science Foundation  for the construction and operation of the  LIGO Laboratory , the  Science and Technology Facilities Council of the United Kingdom , the  Max-Planck-Society , and the  State of Niedersachsen/Germany  for support of the construction and operation of the GEO600 detector, and the  Italian Istituto Nazionale di Fisica Nucleare  and the  French Centre National de la Recherche Scientifique  for the construction and operation of the Virgo detector. The authors also gratefully acknowledge the support of the research by these agencies and by the  Australian Research Council , the  International Science Linkages program of the Commonwealth of Australia , the  Council of Scientific and Industrial Research of India , the  Istituto Nazionale di Fisica Nucleare of Italy , the  Spanish Ministerio de Economía y Competitividad , the  Conselleria d'Economia Hisenda i Innovació of the Govern de les Illes Balears , the  Foundation for Fundamental Research on Matter  supported by the  Netherlands Organisation for Scientific Research , the  Polish Ministry of Science and Higher Education , the  FOCUS Programme of Foundation for Polish Science , the  Royal Society , the  Scottish Funding Council , the  Scottish Universities Physics Alliance , the  National Aeronautics and Space Administration , the  National Research Foundation of Korea, Industry Canada  and the  Province of Ontario  through the  Ministry of Economic Development and Innovation , the  National Science and Engineering Research Council Canada , the  Carnegie Trust , the  Leverhulme Trust , the  David and Lucile Packard Foundation , the  Research Corporation , and the  Alfred P.
The model of RL08 raises a few questions. In particular, with such a configuration in which the Sun is surrounded by several distinct and separate clouds, we would expect to find some lines of sight going through the gaps between the clouds, which would create cases showing no absorption. However such a line of sight has never been found: every line of sight observed with a sensitive enough absorption line in the UV shows at least one absorption component. Moreover,  Crawford et al. (1998)  failed to find evidence to support the existence of the G Cloud as a separate entity, despite very high-resolution observations of interstellar Ca II K lines toward sight lines where both clouds were expected. We therefore decided to re-examine the LISM UVabsorption database with a new focus, i. e. , making the assumption that the CHISM can be assimilated to a unique, continuous medium that envelops the Sun in all directions and produces one absorption component in every line of sight.
The database In this paper we examine the UV Mg II and Fe II data. The reasons for choosing these lines are the following: -Mg II and Fe II produce UV absorption lines that are among the most sensitive to detect small column densities of matter. -These elements are observed in a large number of sight lines because their lines are located in the near UV, where cool stars still produce a continuum with a good signal-to-noise ratio or have stellar emission lines that can serve as continua for the ISM lines (as opposed to the situations for H I, O I, N I, and C II, which have lines that are below 1500 A). -Since they all come from observations taken by the Hubble space telescope (HST), the data and their processing are easily traceable, and in particular their wavelength calibrations are more likely to be consistent, which is an important aspect for a study based on kinematic properties.
Second, we have noticed differences of up to 3 km s -1 in velocities between different (N, V, b) solutions found by different authors for Ca II or Na I for a same sight line (reported by  Redfield & Linsky (2002) ). These differences might be due to the use of different instruments, different wavelength calibrations, or different spectral analysis methods. In any case, since our intention is not to redo any measurement but to use existing material, in these cases we do not know which value to choose. Therefore, to preserve the integrity of the velocity calibration of the sample, we decided to leave out the CaII and NaI samples. Our database is drawn from  Redfield & Linsky (2002) 's Table  3  (for Mg II) and Table  4  (for Fe II), which give for each target the velocity, broadening parameter, and column density of each component.
Identifying the CHISM candidate component in each line of sight Our first step was to identify in every line of sight of the database the component that is most likely to originate in the Circum-Heliospheric ISM (CHISM). For this purpose, we calculated for each line of sight i the quantity V i,helio , the projection onto the line of sight of the velocity vector -→ V 0 of the ISM flow interacting with the heliosphere. If the Sun were completely surrounded by a contiguous interstellar medium moving as a rigid body and not rotating, then in every line of sight we would expect to see an absorption component at the appropriate value of V i,helio. The velocity vector -→ V 0 of the ISM flow into the heliosphere has been measured inside the solar system by various experiments. In particular,  Möbius et al.
Exceptions to this include only sight lines toward the apex or anti-apex of -→ V 0 , where negative velocity deviations are present, and sight lines at high positive Galactic latitudes (i. e. , perpendicular to -→ V 0 ), where positive velocity deviations are observed. 2. 4. Mean velocity vector fitting the Component 1 database. We then derived the velocity vector that best fits the Component 1 sample, i. e. , the velocity vector -→ V m -expressed in the heliocentric reference frame and defined by l m , b m , V m -which minimizes the quadratic sum of the differences between the projections of -→ V m onto the lines of sight (l i , b i )) and the measured velocities V i,1 :   1 , where they are compared to the vectors of the interstellar gas entering the heliosphere measured either by Ulysses and previous experiments  (Möbius et al.
This means that the domains identified with both the LIC and the G cloud, as well as a number of other clouds that had to be invoked to account for slight velocity deviations, are now seen to be included in this single circum-heliospheric cloud. Now that we have constructed a more simple outlook on the nature of the gaseous material that surrounds the solar system, we can move on to examine what the existing database can tell us about the structure and other interesting properties of this Local Cloud.