dataesr/scientific-paragraphs-categorization huggingface dataset:
, San Diego, USA) according to the manufacturer 1 s protocol. Libraries were quantified by fluorescence using the Qubit dsDNA HS Assay Kit (Life Technologies, Carlsbad, USA) and diluted to 4 nM according to the mean size and quantification of each library. Products were sequenced in an Illumina MiSeq platform using 1% denatured PhiX DNA as a sequencing control. The HLA alleles were typed, commonly at the six-digit resolution (some with eight-digit), using the commercial software HLA-Twin (Omixon Inc. , Budapest, Hungary).
NGS sequencing of HIV proviral DNA The sequencing of samples from Bordeaux and Montreal using 454 Roche technology has already been described  [15, 16]. For PGM Ion Torrent sequencing, amplicons were purified prior to enzymatic shearing, adapter and barcode ligation and size selection according to The Ion Xpress Plus library preparation manual. Template enrichment was performed on the Ion OneTouch ES Instrument the following day. Prior to PGM sequencing, quality control of the template was ensured by a Tapestation measurement. Sequencing was performed on the Ion PGM System with the Ion PGM Sequencing 400 Kit using an Ion 316 chip. For the patients in Brazil, the MiSeq (Illumina) sequencer was used to produce NGS data of near full-length HIV genome  [32, 33]. Raw data (SFF or FASTQ files) were submitted to the Smartgene pipeline as previously described  [34]  to generate a BAM file for each patient and each studied gene for subsequent analysis.
Prediction of affinity between epitopes and HLA alleles Using our TutuGenetics software  [35] , we were able to estimate the theoretical affinity between the putative epitopes and the HLA class I molecules of each patient. The TutuGenetics algorithm moves a sliding window of 8 to 10 amino acids across the test sequence and calculates the MHC IC 50 value according to each HLA allele of each patient to identify theoretically new HLA-epitope pairs. In order to identify potential epitopes with high binding affinity and conserved sequence across the available DNA sequence dataset, a conservative cut-off was applied so that only those epitopes with a theoretical MHC IC 50 < 50nm binding affinity and observed in >80% of all sequences analyzed were included. Sabrina Caldato, Jean Delaune, Isabel Prellwitz, Marianne Garrido, Sayonara Ribeiro, Juliana Siqueira, Ornella Botelho.
� 24:02 (one epitope), A � 03:01 (2 epitopes), A � 11:01 (2 epitopes), A � 68:02 (one epitope) and HLA-B � 07:02 (2 epitopes), B � 08:01 (one epitope), B � 35:01 (2 epitopes), B � 15:01 alleles (one epitope) (Table2). The data for the other HLA alleles were not recorded at this step since the IC 50 values for the affinity between epitopes and HLA alleles were above the threshold of 50 nm for MHC IC 50. The 15 epitopes have a length ranging from 8 to 10 residues, most of them (9 out of 15) being composed of 9 residues. Pol02 (FLWMGYEL) is not described in the Los Alamos database; Pol03 (YELHPDKWTV), Pol06 (TVQPIVLPEK) and Pol07 (DVGDAYFSV) are largely overlapping with described epitopes with a shift of one or two residues compared to epitopes noted in the HIV database; Pol07 (DVGDAYFSV) has a theoretical affinity for A � 68:02 (MHC IC 50 : 20.
2 nM) while it is listed in the database as having affinity for A � 02:01; Pol10 (SMTKILEPF) was found to have an affinity for the HLA allele B � 15:01 (MHC IC 50 : 24. 8 nM) in our in silico study, while no HLA allele is mentioned in the database. The other epitopes are already referred to in the database along with a specific HLA class I restriction and published in vitro functional immunological data.
Acknowledgments The following clinicians biologists or methodologists participated in the study from 2012 to 2018:  Pr Michel Dupon ,  Pr Estibaliz Lazaro ,  Pr Charles Cazanave ,  Dr Denis Lacoste ,  Dr Cedric Martell ,  Dr Noelle Bernard ,  Dr Francois Paccalin ,  Dr Marie Carmen Pertusa ,  Dr Emmanuel Ribeiro ,  Dr Pierre Duffau ,  Dr Isabelle Faure ,  Dr Denis Dondia ,  Dr Marie-Anne Vandenhende ,  Dr Thierry Pistone ,  Dr Pascal Biscay ,  Dr Aurelie Saunier ,  Dr Mathieu Mechain ,  Dr Marie-Catherine Receveur ,  Dr Sandrine Reigadas ,  Jennifer Papuchon ,  Pascal Bonot ,  Lise Lucas ,.
The effect of mini-spadin on basal TREK-1 current amplitudes was tested with a wide range of concentrations (from 10 -11 to 10 -5 M). Data acquisition was carried out using a computer with pClamp software (Axon Instrument, USA). Whole-cell currents were generated by running a pulse or ramp protocol every 5 s from -100 to +60 mV with a holding potential maintained at -80 mV. Data were analyzed using Clampfit (Molecular Devices, USA) and the dose-response curves were drawn using Origin 8. 6 (Northampton, MA, USA). Normalized ITREK-1 after application of peptides was analyzed using GraphPad Prism.
2-6 Body weight The weight gain was calculated according to the following formula: weight gain (%) = [(body weight at measurement point (g) -body weight before MCAO (g)) / body weight before MCAO (g)] x 100. Data are reported as Mean ± SEM in the different experimental groups: saline (n=17), spadin (n=14), mini-spadin (n=15) and escitalopram (n=15).
2-7-1 Accelerated rotarod test To assess motor coordination  (Bourourou et al. , 2016) , mice were conditioned to the accelerating rotarod (Ugo Basile, France) for three days before ischemia. Mice were first placed on the apparatus during 30 sec with no rotation and thereafter for 120 sec with a constant low speed (4 rpm). After 10 min rest, each mouse then received a single baseline trial on the accelerating rotarod in which the spindle increased in speed from 4 to 40 rpm during 600 sec. The maximum duration animals were able to walk on the rotarod before falling (latency to fall) was measured (maximum value: 600 sec) during the test trial. Mice were tested over three daily trials in the accelerated condition (4-40 rpm). Data are reported as Mean ± SEM in the different groups: saline (n=16), spadin (n=14), mini-spadin (n=15) and escitalopram (n=14).
The strategies used by mice to carry out the task were taken into account  (Garthe and Kempermann, 2013). The strategy categories (see Supplementary Fig. 6 ) reached from thigmotaxis and random search, which are inadequate strategies to perseverance, chaining and scanning that correspond to efficient spatial strategies. Recorded trials were analyzed with the tracking software Anymaze (Stoelting,USA). The speed of swimming and the total swum distance were also measured. Data are reported as Mean ± SEM in the different groups: saline (n=16), spadin (n=13), minispadin (n=15) and escitalopram (n=14).
2-10 Statistical analysis Data show the mean ± SEM. Analysis was performed using Graphpad Prism software version 6. 0. Significant differences between two groups of data were determined using a Mann & Whitney U test for non-parametric data or when necessary by one-way ANOVA with a Dunnett's multiple comparison test for comparing the treatment effect. Morris Water Maze data were analyzed with a Two-Way ANOVA (treatments and days) with a tukey's multiple comparison test. Level of significance was set at P < 0. 05. Data and statistical analysis comply with the recommendations on experimental design and analysis in pharmacology  (Curtis et al. , 2015).
3-3 Mini-spadin improved the motor and cognitive deficits induced by ischemia Motor and cognitive deficits are common neurological sequels in stroke patients  (Cumming et al. , 2013; Langhorne et al. , 2009). The beneficial motor effects of mini-spadin were first observed in the accelerated rotarod test, which assessed the motor coordination (Fig. 2B ). All mice were identified and their performances were measured in a pre-ischemia test (data not shown). For each mouse the pre-ischemic value was considered as 100%. At Day 3 after MCAO, all groups showed a strong decrease in their performances when compared to the corresponding pre-ischemia group (Fig. 2B ). Only the mini-spadin group showed a significant improvement of their performances when compared with the saline-treated ischemic group ( # P=0.
Data collection After obtaining written informed consent, study staff administered a questionnaire to each participant to elicit information on demographics, stove type and location, and behaviors that may affect exposure to air pollution, including cigarette smoking, ventilation behavior (opening and closing windows and doors, turning fans on and off ), cooking fuel use, and burning substances other than for cooking. The participant was then asked to show his/ her home and cooking space. Study staff measured the size of the main sleeping space of the home using a measuring tape. Air quality in the main sleeping space serves as a proxy for individual exposure, as many homes consist of only one room: the sleeping space where family members spend substantial time. Staff recorded the location of the main cooking area, including distance from the home to the cooking area (if external to the home) and the distance from index to neighbor homes in steps (approximately 0.
Analysis We calculated geometric mean PM 2. 5 and CO concentrations at each monitor location for the following times: between 5 and 6 am (baseline), during observed biomass cooking in the index home, the observation period during which no biomass cooking occurred in any home, and 24 h of monitoring time. Previous research has shown that the lowest PM 2. 5 concentrations are observed in Mirpur from 5 to 6 am  [4] , so the 5-6 am hour served as a baseline for this study. We used data from observations to identify biomass cooking times in the index home. Although neighbor homes primarily used cleaner fuels, our observations indicated that some biomass fuel use occurred when cleaner fuels were unavailable. For our measure of biomass cooking time in an index home, we excluded any times during which an enrolled neighbor home was also observed to cook with biomass fuel.
PM 2. 5 and CO measurements We monitored PM 2. 5 and CO for a total of 95 locations, at least 24 h each, from 44 households. Due to equipment error, five PM 2. 5 recordings had missing data for 12 or more hours. Additionally, one index stove produced extremely high concentrations of PM 2. 5 and CO (over 10 times the mean of other homes during biomass cooking), so we removed the monitors from that home from our analyses. Data analysis was performed using the data from the remaining 88 PM 2. 5 recordings and 93 CO recordings. In one cluster, we did not observe biomass cooking in the index home during our observation period. For this cluster, we estimated index cooking time based on elevated CO concentrations in the index home at a common cooking time during the day prior to observation, as biomass cooking was the only obvious source of sustained elevated CO concentrations for all homes in the study.
Introduction: Mozambique is one of three countries with high prevalence of tuberculosis (TB), TB/human immunodeficiency virus coinfection, and multidrug-resistant TB. We aimed to describe Mycobacterium tuberculosis spoligotypes circulating among drug resistant (DR) strains from Beira, Mozambique comparing them with genotypes in the country. Methods: We performed spoligotyping of 79 M. tuberculosis suspected of DR-TB compared all spoligotype patterns published on the international database and PubMed. Results: Both in Beira and Mozambique (n=578), the main clades were Latin-American-Mediterranean, East-African-Indian, Beijing and T, with no extensively DR TB cases. Conclusions: Beira and Mozambique share the same population genetic structure of M. tuberculosis.
ACKNOWLEDGMENTS We thank  MMP Nascimento ,  RHC Pocente ,  V Sofia ,  T Ines ,  J Beirão , and  J Ferro  for the assistance in data collection and test performance;  Beamedex SAS company (C Ripoll, MGomgnimbou)  for providing TB-SPOL reagents free of charge (www. beamedex. com,Orsay, France); and the  I2BC-IGEPE team (B Klotoe ,  G Refrégier , and  S Panaiotov ) for their technical support and assistance in test performance. Financial Support Fundação de Amparo à Pesquisa do Estado de São Paulo  Process number  15/13333-3 ,  Fundação de Apoio ao Ensino, Pesquisa e Assistência do Hospital das Clínicas da FMRP-USP ,  Fogarty International Center   HIV Research Training Program  grant, and  National Institutes of Health  to the  University of Pittsburgh  ( D43TW009753 ). Chaires  Franco-Brésiliennes dans l'Etat de São Paulo international collaborative program -2017  ( C Sola  and  VR Bollela  laureates).
, 2014; Jaegle & Ro, 2014; Romei et al. , 2011 Romei et al. , , 2010)). Finally, a recently published report by our group, based on the same dataset we here aimed to further analyze, provded evidence that 30 Hz rhythmic TMS delivered over the right Frontal Eye Field (FEF), a key region of dorsal attention orienting networks, was able to locally entrain high beta oscillations in the frontal region below the stimulation coil. Most important, it suggested that local entrainment could be causally linked to improvements of conscious visual detection for left-lateralized near-threshold targets  (Vernet, Stengel et al. , 2019). Through the above mentioned studies, rhythmic TMS has built a solid credibility as the only tool able to causally explore the oscillatory basis underlying the top-down modulation of conscious visual perception in humans by identifying performance shifts tied to the entrainment of local rhythmic activity at specific frequency bands and cortical sites.
However, the role of the interregional synchrony between a stimulated region (in the current case the right FEF) and other areas of the attentional orienting networks and the ability of this biomarker to be involved in the modulation of conscious visual perception during the delivery of rhythmic TMS pulses remains unexplored. To this end, given indirect evidence showing that TMS-evoked oscillations can spread through connections to distant regions  (Rosanova et al. , 2009) , we further analyzed the data of an existing TMS-EEG dataset  (Vernet, Stengel et al. , 2019) , aiming to extend prior results supporting a perceptual modulatory role of local episodic entrainment of high-beta activity in the right FEF. We hypothesized that brief entrainment of local 30 Hz oscillations by rhythmic TMS patterns on the right FEF would induce transient inter-regional phase synchronization at a high beta frequency, likely within a dorsal fronto-parietal system linking this frontal site with posterior parietal areas  (Capotosto et al.
Participants We performed further analyses on an existing TMS-EEG dataset used in a recent publication to demonstrate local high-beta entrainment during stimulation of the right FEF and its association with improvements of conscious visual performance (see  Vernet, Stengel et al. , 2019 for details). A group of right-handed 14 healthy participants (9 women) aged between 20 and 34 years old (24  4) took part in the original experiment. Participants had normal or corrected-to-normal vision. They all took part voluntarily after having signed a consent form and were naïve as to the purpose of the experiment. All the experimental procedures were performed according to the Declaration of Helsinki. A research protocol including all the interventions of this study was sponsored by the INSERM and approved by an Institutional Review Board known in France as Comité de Protection de Personnes (CPP Ile-de-France IV).
Conscious visual detection paradigm An in-house MATLAB (Mathworks, version R2012b) script using the Psychtoolbox extensions  (Brainard, 1997)  was used to control the presentation of visual stimuli synchronized with the delivery of the TMS pulses. During the task, participants were seated with their heads resting on a chin-rest set so that their eye's canthi remained 57 cm away from the center of the screen. Each trial started with a gray resting screen lasting for 2. 5 secs, followed by a fixation screen that displayed a central fixation cross (size 0. 5x0. 5 o ) and a right and left rectangular placeholders (6. 0x5. 5 o ) drawn 8. 5° away from the center (Fig. 1A ). These placeholders indicated the potential right or left lateralized locations of the target during the trial. The duration of the fixation screen was jittered between 1.
EEG recordings EEG signals were continuously recorded during all experimental blocks with a TMScompatible system (BrainAmp DC and BrainVision Recording Software, BrainProducts GmbH). We recorded signals from 60 electrodes spread evenly across the scalp and positioned according to the international 10-20 system, plus a reference on the tip of the nose, a ground on the left ear lobe and 4 additional EOG electrodes positioned above and below the right eye and on each temple. Skin/electrode impedances were maintained below 5 kOhm. The signal was digitized at a sampling rate of 5000 Hz.
EEG epoching and artifact removal procedure All EEG data analyses were performed with the FieldTrip toolbox  (Oostenveld et al. , 2010)  running on MATLAB R2017b. The EEG and EOG data were epoched across a [-2, 2] seconds window centered on the onset of the target. Trials where the participant did not correctly fixate the central cross in the time interval between the alerting cue onset and the target offset were automatically excluded during the task performance by monitoring gaze position using an eye tracking system. Prior to any data analysis, all trials contaminated by blinks in the [-500 500] ms epoch around visual target onset were removed by visual inspection. After these procedures, an average of 126±13 trials remained for each experimental block. To remove the artifact created by the discharge of a TMS pulse, data in a window of  [-4, +12]  ms centered on the delivery of a pulse was discarded.
A second order Butterworth filter (1 to 50Hz), with forward-backwards filtering, was applied on the remaining data before the the gap of removed EEG data was filled with a piecewise cubic spline interpolation. To reduce the burden of computation time, data were down-sampled to 500 Hz before an Independent Component Analysis (ICA) was applied. To make sure that the ICA did not introduce any difference between conditions, trials for all 4 experimental blocks (whether they were active or sham TMS trials, and whatever the TMS pattern tested) were gathered together and the ICA was computed on this single dataset. This procedure enabled the removal of residual artifacts (including eye movements, electrode malfunctions, 50 Hz power line artifacts and TMS artifacts lasting longer than 12 ms). Components were identified as artifacts based on the guidelines of  Rogasch et al.
TMS-EEG data analyses Epoched EEG signals were transformed into the time-frequency domain using a 3-cycle Morlet wavelet transform on the time window [-500 +500] ms around target onset and for frequencies between 6 and 50 Hz. Three measures associatd to the notion of oscillation synchronization were calculated: power, inter-trial coherence (ITC) and imaginary phase-locking value (PLV). Power was calculated as the squared value of the modulus of the Morlet coefficients (per each time frame and frequency bin) relative to a baseline window [-500 -300] ms before target onset. This outcome measure shows, in decibels (dB) unit, the increase (positive value) or decrease (negative value) of power relative to this period. Inter-trial coherence (ITC) estimates phase consistency across trials in a given location (an electrode or a group of electrodes).
Behavioral Data Analysis Performance in the detection task was assessed through the perceptual sensitivity index (d'), a measure from Signal Detection Theory that quantifies objective perception of stimuli presented around the threshold of perception  (Stanislaw & Todorov, 1999). Trials were separated in 'hits' (when the target was correctly detected), 'misses' (when the target was not reported), 'false alarms' (when a target was reported for a catch trial, i. e. trials when no target was presented), 'correct rejections' (when no target was reported in a catch trial) and 'errors' (when a present target was reported on the wrong side of the screen). The perceptual sensitivity index was then calculated from the rate of 'hits' and 'false alarms'. See  Vernet, Stengel et al. (2019)  for full details on such analyses.
A nonparametric permutation test was then applied on these clusters (10000 permutations, alpha = 0. 05) to determine which ones survived the correction for multiple comparisons. Cluster-based permutations is a highly sensitive method for correcting for multiple comparisons in EEG data because it is adapted to data highly correlated in space and time (i. e. an effect on the EEG signal is likely to be spread over adjacent EEG leads and consecutive time points)  (Maris & Oostenveld, 2007). However, currently no consensus exists on how cluster-based permutations should be applied in factorial designs to evaluate interaction effects between multiple factors  (Edgington & Onghena, 2007; Suckling & Bullmore, 2004). For this reason, and guided by our a piori predictions of a contrast between rhythmic and random TMS patterns to isolate the impact of rhythmic structure of pulses within TMS patterns on oscillatory activity, we computed direct pairwise comparisons between conditions.
Ryhthmic and random TMS effects in alpha band synchronization Aside from the modulation of oscillatory activity in the high-beta band, our data also unexpectedly revealed alpha desynchronization over parietal areas for all 4 TMS conditions (active or sham, rhythmc and random stimulation) during burst delivery (Fig. 4 ). This outcome might not be likely associated to TMS active fields, as the delivered patterns failed to show any significant difference when comparing active vs sham TMS patterns. However, both statistical rhythmic/random comparisons (active rhythmic vs. active random and also sham rhythmic vs. sham random) showed that rhythmic patterns induced stronger alpha desynchronization than random patterns. By the end of the TMS burst, we observed alpha band phase-locking increases over the same parietal contacs (Fig.
We also posit that, very likely, such effects on frontoparietal synchrony are closely related to the local entrainment of a 30 Hz episodic rhythm induced by TMS in the right FEF, reported already in a previous analysis of this same dataset  (Vernet, Stengel et al. , 2019). Given that focal stimulation has been shown to combine local and inter-regional effects (reviewed in  Polanía et al. , 2018; Valero-Cabré et al. , 2017)  we here investigated if a focal entrainment of a 30 Hz osicllation in frontal regions previously reported by our group  (Vernet, Stengel et al. , 2019)  could be conveyed to distant interconnected cortical regions, not directly influenced by TMS patterns. To this end we focused on EEG leads overlying posterior parietal regions, notably those located in and around the intraparietal sulcus (IPS) which has been shown to interact with the right FEF as part of the dorsal attentional orienting network and shown an ability to modulate perceptual inputs  (Capotosto et al.
Our results build on the above-mentioned correlational results and, adding the value of causality, provide further proof in favor of a causal relationship between high-beta fronto-parietal synchrony and the modulation of visual perception in the human brain. Most importantly, they extend prior results derived from this same TMS-EEG dataset and suggest that short bursts of focal rhythmic TMS do not only show an ability to locally entrain frequency-specific rhythms within the targeted area dictated by the pace of stimulation  (Vernet, Stengel et al. , 2019)  but can also be used to synchronize in a frequency-specific manner the stimulated target region (in our case the right FEF) with interconnected regions (such as posterior parietal sites) and entrain distantly rhythms at this same frequency.