dataesr/scientific-paragraphs-categorization huggingface dataset:
Plasma insulin concentrations varied significantly with diet (p = 0. 013) and time of the day (p<0. 001) with a significant diet:time interaction (p = 0. 015). B mares had significantly higher plasma insulin concentrations at 10:30 AM and tended to have higher concentrations at 9:30 AM, 11:30 AM, 2:30 PM and 5:30 PM. IVGTT were performed before and after the dietary treatment started. Data are shown in  Fig 4. No difference between groups was observed in August. Plasma glucose increments, AUC and maximal increment during IVGTT in February were unaffected by diet. Plasma insulin increments, however, were significantly higher in B versus F mares at 7, 12, 15, 30, 60 and 90 min after the injection started (p = 0. 002 for the dietary effect). Plasma insulin AUC was significantly increased in B versus F mares (p = 0.
Acknowledgments The authors are grateful to  William Martin-Rosset  for design of the nutritional plane of broodmares,  Joseph Bellonie  and  Patrice Dupuis  for care and management of animals and assistance during metabolic tests,  Françoise Ternois  for management of NEFA assays,  Sarah Valentino  for assistance during IVGTT,  Céline Houdard  for management of clamps and  Isabelle Caudron  and  Brigitte Deliège  for management of X-rays.
Body weight (Table  1 ) or food intake (not shown) were also not modified by pollutants in 7wk-old female mice, as previously shown for the 12-wk old exposed females fed HFSD  [25]. Metabolic profiles were completed with GTTs. We observed that 7 wk-old exposed females had a better glucose tolerance with a significant decrease (-20%, p = 0. 01) in the area under the curve (AUC) compared to age-matched females fed pollutant-free HFSD. Thus, pollutant-exposed mice fed HFSD ranged between mice fed standard chow and mice fed pollutant-free HFSD (Fig  1 ). These data are the compilation of the glycaemia values of 7 litters of HF0 females and 9 litters of HFp females from 4 independent experiments. A subset of mice (3 litters per group) were maintained until 12 wks of age and shown to have an aggravated glucose intolerance when exposed to pollutants compared to females fed pollutant-free HFSD (Fig 1 ), consistent with data previously reported and expressed per female mice  [25].
Acknowledgments The authors are very grateful to  E. Meugnier ,  E. Loizon  and  S. Pesenti  for technical help with the design of PCR primers. Part of the experiments was performed on the "Animage" platform of  CERMEP Imagerie du Vivant, Lyon, France. The authors would specially like to thank  R. Bolbos  and  J-B Langlois  from  CERMEP  for performing Magnetic Resonance acquisitions in mice. We also thank  Patrick Manas  for animal care.
Acknowledgments We thank the staff of the Akonolinga district hospital, who provided diagnoses and delivered treatment to BU patients, and the staff of the  Service de Mycobactériologie at Centre Pasteur du Cameroun , who provided laboratory confirmation of diagnoses. Dr. Landier  specializes in infectious diseases epidemiology. While working on his PhD degree at the  Institut Pasteur in Paris , he focused on BU epidemiology and M. ulcerans transmission in Central Africa.
Acknowledgements The authors would like to thank the coordinator  Pilar Santolaria , and other researchers who helped collecting the data in the different locations ( Helena Resano ,  Gabriela Zeballos ,  Nuria Panella-Riera ,  M. Mar Campo ,  Saoussan Khliji ,  Ana Guerrero ,  M. Angels Oliver  and  Carlos Sañudo ), as well as  Faouzi Lyazrhi  for his contribution in clustering analysis.
Calculations The data recorded during the testing situations were downloaded using the K4b 2 management data software (Cosmed, Rome, Italy). Cardiorespiratory responses were filtered and averaged over 5 s, and the curves were smoothed with a 3-point index  (Duffield et al. , 2004). The different phasesthat is, wu, ex and recof the testing situations were identified by comparing the recorded time for each phase and time indicated by VO 2 recordings. Data were then transferred to Excel files to convert VO 2 and VCO 2 into ml/min per kg and to calculate the respiratory exchange ratio (RER) as VCO 2 (l/min)/VO 2 (l/min). Oxygen uptake at rest (VO 2rest ) was defined as the mean of the last 30 s of the rest period before the beginning of the testing situations with horses not moving. Mean VO 2 , VCO 2 , RER and HR during the exercise phase (VO 2ex , VCO 2ex , RER ex and HR ex ) were defined as the means of VO 2 , VCO 2 , RER and HR, respectively, measured throughout exercise phases of the four testing situations.
, 2014). Although they should be considered with caution, given the small number of horses tested, our results are the first to provide figures of the amount of aerobic energy utilized during training, ~80 400 kJ/week or 11 500 kJ/day. Further investigation is necessary to confirm our data using more horses for estimating the anaerobic energy contribution, as well as the energy from additional physical activities (such as treadmill exercises, horse walker sessions or races). Despite these limitations, our estimations of energy cost related to physical activity should be added to the maintenance requirements to determine the daily energy requirements of exercising horses. This indeed contributes to assess energy balance in the nutrition of trotters.
Acknowledgements This study was supported by  Institut National de la Recherche Agronomique (INRA) Department Physiologie Animale et Systèmes d'Elevage (PHASE) , the  Burgundy Region and AgroSup Dijon. Authors acknowledge the trainers of horses and the staff of  Unité de Recherche sur les ANImaux de l'Elevage (URANIE)  for their active participation.
Instrumental setup Samples were acquired on a custom-made instrumental setup comprised of a projector, a collection objective lens and a monochrome camera. The projector consisted of a digital micromirror device (DMD; GFM, Berlin, Germany), fiber-coupled coupled to a 670nm laser diode (LDX Optronics, Maryville, Tennessee) and projecting pre-programmed patterns using MATLAB (Mathworks, Natick, MA). A first linear polarizer (Moxtek, Inc. , Orem, Utah) was placed in front of the projector lens (NIR S2 + ; Carl Zeiss, Oberkochen, Germany). Images were collected through a custom imaging system (Qioptic, Waltham, MA) and imaged onto a monochrome camera (ORCA, Hamamatsu, Bridgewater, New Jersey). A second linear polarizer was placed in front of the collection objective lens, cross-polarized with respect to the first polarizer, to preferentially collect light that has diffused inside the medium and minimize the influence of specular reflections.
Acknowledgments The authors would like to thank  Florin Neacsu  for assistance with the SFDI acquisition software. This work was supported by  NIH/NIDDK  Award Number  K01-DK-093603 ,  NIH/NIDDK  Award Number  F31-DK-105839 , by the  Dutch Center for Translational Molecular Imaging (MUSiS) , and by  ICube.
Prior to the experiment, a standard MRI screening questionnaire was given to participants to ensure their safety upon entry to the MRI unit. Participants were also asked to complete the Oldfield handedness questionnaire  [40]  to indicate their handedness. Of the 29 healthy participants who were recruited and participated in the study, 20 datasets from these participants (average age = 25. 87 ± 5. 9) were usable for the finger-tapping portion of the experiment. Of these 20 participants, 11 were "control" and 9 were "exposed" to the 3000 μT, 60 Hz MF. For the mental rotation portion of the experiment, 21 datasets were usable (average age = 25. 3 ± 5. 6). Of these 21 participants, 11 were in the "control" group (6 males, 5 females) and 10 in the "exposed" group (5 males, 5 females). Dataset exclusions were due to the following: excessive movement during imaging (n = 4), technical issue preventing finger tapping recording (n = 1), inability to see pictures without glasses during the mental rotation task (n = 2), alcohol during the 24 hours preceding the experiment (n = 1), and a drop-out (n = 1).
Apparatus The experiment was run using a 3. 0 Tesla Magnetic Resonance Imaging (MRI) Scanner (Siemens Verio, Erlangen Germany) and a 32-channel head coil. The MF exposure was produced by the MRI Z-gradient coil (programmed by our medical physicist, Dr. Jean Théberge, and Siemens Medical Ltd, Canada). An MRI-compatible button press system taped to a glove on the participant's right was used to record participant response to visual stimuli (go/no-go). Visual stimuli for both tasks were displayed on an MRI-compatible projection screen using a custommade LabView (National Instruments, Austin, USA) program. Participants were instructed when to tap by displaying "TAP" or "STOP" on the screen seen by the participant from inside the MRI using a mirror attached to the head coil. The LabView program also recorded tapping times and mental rotation answers.
Since our main focus was to extend our pilot results, the finger tapping task was always performed first after the one hour exposure period; acknowledging the possibility of decreasing the probability to identify an effect in the mental rotation task if it exists. Functional data was then acquired for each of the three conditions. Then, the one-hour 60 Hz MF sequence was delivered (exposure or control). After the 60-minute exposure period, participants repeated the same three tasks under the same three imaging sequences. Upon removal from the MRI, participants completed a Field Status Questionnaire (FSQ)  [46]. This questionnaire evaluated whether participants were able to detect the presence of the 60 Hz MF, as well as the degree of certainty in doing so, and the level of comfort and stress felt during the study.
Data analysis Regions of interest (ROI) were chosen using a priori information regarding the brain regions associated with the tapping task  [28] [29] [30]. fMRI data files were analyzed using the software BrainVoyager (BrainVoyager QX 1. 9. 10, Brain Innovation, The Netherlands). First, functional data were pre-processed to compensate for head movement and signal drift, using a built-in 3D motion correction algorithm and temporal filtering. The quality of head movement correction was systematically inspected after this processing step for each set of fMRI images, using the movement curves and video files provided by BrainVoyager QX. Second, spatial smoothing was applied using a built-in 3D Gaussian smoothing of 8 mm Full Width at Half Maximum to improve the reliability of comparisons between participants.
Pre-exposure activation maps were produced for all participants to determine if our results replicated previously published BOLD fMRI studies of the finger-tapping task  [38, 44]  and the mental rotation task  [37, 44, 45]. 'Postminus pre-exposure' images were produced for each of the control and 60 Hz MF conditions for each task. Beta weights, evaluating the intensity of the correlation between a predictor function (on/off) relative to the task and the measured BOLD activity in given brain voxels, were then extracted from each ROI. Beta weights for each ROI were analyzed separately using SPSS (SPSS 16. 0, SPSS Inc. , Chicago, USA), using a mixed design ANOVA for repeated measures with a between-subjects factor (exposure group). For this study, p < 0. 05 was considered significant and was reported as a 'non-significant trend' when 0.
05 p 0. 1. Bonferroni corrections accounting for multiple comparisons were systematically integrated in the statistical analyses conducted with SPSS. Button press data from the tapping task were analyzed using Matlab (The Mathworks, USA) to determine the frequency (inverse of the time period between two button presses) and regularity of the recorded responses. Similarly, for the mental rotation task, the task performance (percentage of correct answers) was calculated from the button press data using Matlab. A repeated measures ANOVA (within subjects: time; between subjects: condition) was applied to determine if there were significant differences in tapping frequency between groups before and after the 60 minute exposure period for both groups ("control" and "60 Hz MF") using SPSS. Finally, a χ-squared test was applied to the FSQ results for both groups to evaluate the potential capability of participants to detect the presence of the 60 Hz MF.
Immunoblotting with antisera The proteins were completely transferred to membranes using the TE70/77 PWR Semi-Dry Transfer Unit (GE Healthcare) according to the manufacturer's instructions. The membranes were blocked for 1 h at room temperature using 5% rabbit serum in Tris-buffered saline containing 0. 1% Tween-20 (TBS-T) and then washed three times for 20 min in TBS-T. The blots were incubated overnight at 4 °C with a 1:500 dilution of the antisera derived from the immune-challenged and control cattle. The blots were then incubated for 1 h at room temperature with a 1:5000 dilution of goat anti-bovine IgG HRP-conjugated antibody (Sigma, USA). After washing, the immunolabeling was detected using ECL Western blotting reagents (GE Healthcare). Finally, specific immunogenic proteins were visualized using a ChemiDoc XRS Camera and the Quantity One 1D analysis software (Bio-Rad).
Protein identification by MALDI-TOF MS /MS analysis All spectra were collected using an ABI 4700 proteomics analyzer Plus TOF-TOF Mass Spectrometer (Applied Biosystems, USA). MS/MS data were obtained using this instrument with a Nd:YAG laser with a 200 Hz repetition rate, and accumulation of up to 4000 shots were performed for each spectrum from which the three highest intense peaks were processed to an enhanced resolution. When the three intense peaks were subjected to downstream analysis, these were ignored for a period of 60 s. MS/MS mode was operated with 2 keV collision energy supplying air as the collision gas, which resulted in completion of nominally single collision conditions. MS/MS data were obtained using the default instrument calibration without internal or external calibration.
The quality control parameters included based on the Mascot algorithm were the following: maximum of one missed cleavage permitted by trypsin, fixed modification (including residue specificity) of carbamidomethyl, variable modifications (including residue specificity) of oxidation, charge state of 12 to 14, mass tolerance for peptide ion (m/z) of 0. 1 to 0. 2 Da, cut-off score/expectation value for accepting individual MS spectra of highest expectation (probability on profound search, PPS). All protein identifications were made by only single protein spot and were collected using a score with the minimal number of high quality peptides per protein is 22. Peptide mass data were used to query the NCBI protein sequence and annotated genome databases of Brucella using the Mascot search engine (Matrix Science, London, UK)  [15].
Based on the sequences identified using mass spectrometry, biological information on the chosen proteins was retrieved using the EXPASY database  [16]. The sub-cellular localizations of bacterial proteins were predicted using PSORTb v. 2. 0. 4  [17]. Functional annotations were made based on the cluster of orthologous groups (COG) protein database generated by comparing all of the complete sequences of microbial genomes from the NCBI COG  [18].
Identification of immunogenic proteins at different infection stages Amongst the 55 immunogenic proteins detected at all three stages of infection, the signal intensities of 23 immunogens were higher than the average when normalized to the total valid spot intensity; these 23 proteins were analyzed using MALDI-MS/MS. The data revealed that several novel immunogenic proteins with diverse ORF had varying values for M r and pI in MALDI-MS/MS (Table  3 ). NCBI BLAST searches of the proteins identified using MALDI-MS/MS show that 10 (43. 5%), 2 (8. 7%) and 2 (8. 7%) proteins were predicted to have cytoplasmic, outer membrane-bound periplasmic and ribosomal localization, respectively. However, 9 spots (39. 1%) were unknown proteins. Analysis of each identified protein indicates that 13 of the 23 proteins participate in multiple enzymatic activities.
27 20 cytoplasm F: Nucleotide diphosphate kinase transport and metabolism 257 BruAb2_0845 3341366 hypothetical protein YP_223598. 1 Q577E7 177 BruAb2_0845 500 18506 18517 5. 02 43 unknown S: Function BruAb2_0845 unknown 263 ohr 3341640 organic hydroperoxide YP_223139. 1 Q579A6 140 BruAb2_0347 8 14337 14232 5. 63 13 unknown R: General resistance protein function prediction only a b Theoretical molecular weight from the UniProtKB database entry. c Subcellular locations were predicted using PSORTb v. 2. 0. 4. d Cluster of orthologous groups (COG) protein database generated by comparing microbial genomes from the NCBI COG.
Revocation. IdentUser(rsk, DB REG , T ick k ): This deterministic algorithm takes as input the private key rsk, the database DB REG and a valid m-ticket T ick k. It outputs the identifier ID U of the user who obtained T ick k. If ID U does not belong to DB REG , it outputs ⊥. IdentTicket(rsk, view, ID U ): This deterministic algorithm takes as input the private key rsk, a user's identifier ID U and a transcript view of an execution of TokenRequest with this user. It outputs all the mtickets that can be generated from the token obtained after the execution of the TokenRequest protocol that led to view.
Trust assumptions At the user's side, we consider an untrusted mobile platform (smartphone) with an incorporated trusted secure element. We use a SIM card as a tamper resistant secure element since it could be certified EAL4+  [24]. Using a SIM card will ensure the integrity of the implementation. Indeed, the m-ticketing cardlet manages the cryptographic credentials of the m-ticketing application and executes required cryptographic computations without delegating any of them to the smartphone. Consequently, if the mobile application is compromised, the security will not be impacted because all the sensitive data and credentials are stored and managed in the SIM card. If the SIM card gets compromised, we planned countermeasures (cf. Section 7. 4. 2).
(T ick OReportT icket(ID U , view) is an oracle that takes as input the identifier ID U of an honest user and a transcript view of a TokenRequest execution with this user and outputs the set of unused m-tickets. For each unused m-ticket T ick k , the oracle records (ID U , T ick k ) in Set. In the sequel, we denote by A(keys, DB: oracles) an adversary who receives the keys "keys". This adversary has only read access to the databases "DB" and can query the oracles "oracles".
User registration phase We denote by ID U a user's identity and DB REG the database where the TA saves the identities of registered users. First, the user sends his public key h U and his identity ID U to TA. Then, he signs, using Schnorr signature scheme  [46] , a random challenge rc received from TA, using his private key x U. If the signature is valid, TA saves the tuple (ID U , h U ) in DB REG. Then, the user securely introduces his banking credentials in order to be subsequently charged.
Revocation We distinguish two levels of revocation: the user's anonymity revocation and the m-tickets revocation. In the first case, the transport authority would like to get the user's identity corresponding to a given m-ticket. In the second case, the transport authority would like to revoke all the m-tickets of a given user, e. g. , upon the request of the user further to the theft of his smartphone. In order to recover a user's identity based on a used m-ticket T ick k = (B k , E, Π), TA sends E to the revocation authorities. Together, the revocation authorities decipher E and retrieve the commitment of s (g s 1 ). By using DB REG the database of registered users and g s 1 , TA will find the user's identity in the tuples (A, C 0 , s 2 , c, µ, ID U , h U ). In order to retrieve the m-tickets of a user based on his identity ID U , first of all, TA must find the tuple (A, C 0 , s 2 , c, µ, ID U , h U ) from DB REG such that ID U = ID U.
Real time computations Compute: c = H(C, C 1 , C 2 , B 0 , T , T , Com, B, ch ------------Choose ch ∈ Z * p D, K, C 1 , C 2 , R , R , C , T 4 , Com 1 , D 1 , K , L , ch) s 1 = k 1 + c × k mod p; s 2 = r 1 + c × ν mod p Check that B = 1 G 1 s 3 = l 1 + c × l mod p; ω 1 = a 1 + c × a mod p • If The validator holds the private signature keys y and γ (First case). Hence the prover does not send the value D and C. The verifier can compute it: D = B y , C = B γ 0. Then goes to (*) ω 2 = d 1 + c × s mod p; ω 3 = t 1 + c × r 2 mod p ω 4 = b 1 + c × β mod p; ω 5 = α 1 + c × α mod p ω 6 = t + c × r mod p; ω 8 = t 3 + c × r 3 mod p ω 10 = t 5 + c × r 5 mod p; ω 11 = d + c × δ mod p • Otherwise, if the validator doesn't know the private signature key y and γ (Second case), then, check that   DT )  to TA in order to be saved within the secured database DB U sedT ickets ; ID V is the identity of the validator and DT is the date and time of the transaction.
D. 2 Validator details The validator has been developed in Java and Scala. The Java software uses a native library for EC scalar multiplications and pairings. This library depends on libGMP for big integers computations and benefits from its assembly optimizations. Furthermore, computations are distributed between threads (at JVM level) to benefit from the multi-core architecture of the PC. The PC is an Intel(R) Xeon(R) with a E5-1620 CPU with 4 cores running at 3. 70GHz under a 64-bit Linux OS. The NFC reader of the validator is an Omnikey 5321 dual interfaces.
G 1 ,G 2 ,G T Multiplicative groups of prime order p g,g 0 ,g 1 ,g t , g T ,g U ,h,G,H Generators of G 1 (x T , h T ) ElGamal private and public keys of the revoca- tion authorities ((a, b), (n, p P )) Pailler private and public keys of the revocation authorities (γ, (W, W )) BB-signature scheme private and public keys of the transport authority (y, Y ) BB-signature private and public keys of the transport authority for set membership proof (x U , h U ) Private and public key of the user A A permission token ID U The identity of the user T ick k The k th m-ticket B k The serial number of the k th m-ticket E The ElGamal encryption of the user secret cor- responding to his set of m-tickets Π A proof (POK) ch A challenge DB REG The TA secure registration database DB U sedT ickets A secure database where the TA centralises the used m-ticketsTable 1.
To the best of our knowledge, very few corpora are related to ageing voices in French  [14]. The different available corpora are stems of projects related to the study of French language like the "Corpus de Français Parlé Parisien des années 2000 5  ". This corpus is made of recordings of inhabitants of different districts of Paris in order to study the influence of French spoken language over France and the French speaking world. The "Projet Phonologie du Français Contemporain 6  " is a database of records according to the region or the country. The records of 38 elderly people (above 70 years old) are included, each record is made of a word list, a small text and two interviews. Other available sources come from videos of testimonies of Shoah survivals and recorded in the framework of "Mémorial de la Shoah 7  " which collect testimonies and organize conferences.
This process will be presented in Section 4. Moreover, the recording microphone was set in the ceiling and not as usual at a short distance in front of the speaker but in Distant Speech conditions. The corpora were processed by the ASR of the CMU toolkit Sphinx3  [20]. The acoustic vectors are composed of 13 MFCC coefficients, their first and second derivatives. The Acoustic Model (AM) is context-dependent with 3-state left-toright HMM. We used a generic AM trained with BREF120, a corpus made of 100 hours of French speech. The language model was a 3-gram-type LM resulting from the combination of a generic language model (with a 10% weight) and the domain one (with 90% weight). The generic LM resulting from French news collected in the Gigaword corpus was 1-gram with 11,018 words. The domain The target is that only the sentences of interest could be recognized by the system (i.