dataesr/scientific-paragraphs-categorization huggingface dataset:
4C ). To examine the potential physiological relevance of mTOR translocation to the plasma membrane, we examined mTOR co-localisation with nuclei and blood vessels. Whilst mTOR did not associate with the nucleus post exercise (DAPI positive regions -data not shown), we did observe mTOR accumulating in regions close to the blood capillary endothelium as determined via UEA-I Lectin positive staining (Fig. 5 ). TSC2 dissociates from Rheb and plasma membrane regions post-exercise. TSC2 and Rheb were found to associate in close proximity to the plasma membrane in pre-exercise samples in both the CON and FED groups (Fig. 6 ). Following contraction, TSC2 and Rheb association decreased by ~20% across the entire post-exercise recovery period in both the CON and FED groups (Fig. 6 ). The dissociation of TSC2 and Rheb post exercise appeared to be mediated by TSC2 export from the cell membrane as we observed a ~30% decrease in TSC2 association with the plasma membrane (Fig.
In contrast, we observed TSC2 to be located at the plasma membrane, co-localising with Rheb as opposed to the two proteins associating at the lysosome. In our hands, TSC2 dissociated from the membrane following resistance exercise, leaving Rheb and the mTOR/LAMP2 complex to co-localise. It is unclear why the location of this event is different in human as opposed to murine skeletal muscle but may be Immunofluorescence quantification of mTOR (Red) and eIF3F (Green) interaction, displayed as a composite image (Merge) and WGA (Blue). Yellow regions represent mTOR and eIF3F interaction. Each panel represents one subject from CON and FED across the experimental time course (A). Group data is quantified and reported in (B). Circles represent CON, squares represent FED. All image processing was kept constant between images, including exposure time, gain, and image frame.
All image processing and quantitation was carried out in ImagePro Plus 5. 1 and kept constant between images, including exposure time, gain, and image frame. Total protein fluorescence intensity was quantified by measuring the signal intensity within the intracellular regions of a mask created by a specific membrane marker (dystrophin, WGA). For colocalisation analysis, five to seven areas per section were randomly selected and imaged under the same capture settings. Images were processed and analysed under the Image-Pro Plus 5. 1 software (Media Cybernetics, MD, USA). Prior to colocalisation analysis, all images underwent a no neighbour deconvolution algorithm as a filter. Image signals generated by WGA or dystrophin were used to estimate cell membrane borders, which were merged with the corresponding target protein images to identify the association between the protein of interest and the plasma membrane.
Pearson's correlation coefficient (Image-Pro software) was used to quantify localization with the plasma membrane and mTOR associated proteins. S6K1 and AKT activity assays. 50 mg of muscle was used for the measurement of S6K1 and AKT activity as previously described  45. Statistical Analysis. Immunofluorescence image analysis was performed in duplicate, with 5-7 regions per cross section used for analysis. Pearson's correlation coefficient values at each group/time point were analyzed using a two-way ANOVA (Prism, Graphpad) with Tukeys post-hoc analysis where appropriate. Significance was set at P < 0. 05. Data are presented as Mean ± SEM and reported relative to the baseline biopsy (i. e. -40 min timepoint) in the CON group (PRE-EX CON).
Acknowledgements This study was funded in part by a  BBSRC New Investigator Award   BB/L023547/1  to AP and a  Natural Sciences and Engineering Research Council (NSERC)  grant to L. L. S. Z. S. was supported by a  Li Siguang Scholarship  from the  China Scholarship Council  whilst D. R. M. was supported in part by an  NSERC  Post-doctoral fellowship. T. H. was supported by the  National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health  ( AR057347 ), and the  Department of Defense  ( W81XWH-14-1-0105 ). The authors would like to thank  Dr Phil Atherton  ( University of Nottingham ) and  Dr Keith Baar  ( UC Davis ) for their insightful discussions during the preparation and revision of the manuscript.
Whereas in Experiment 1 and 2 participants predicted recognition performance when making their JOLs, in Experiment 3 they predicted recall. This allowed us to test the generalizability of our findings across different experimental contexts. The key novel feature of the present study was that participants provided written justifications on a subset of their JOLs, which were then analyzed using natural language processing techniques. Participants were not given any instructions on how to write their justifications nor did we manipulate any additional variables known to influence JOL confidence. Three methods of text data analyses (described in more detail in the Methods section) were used to examine in detail the content of these justifications as well as evaluate differences between justifications for different JOL responses.
g. cue familiarity) more salient than others. We also assessed whether and how confidence and binary yes/no JOLs compare with each other in terms of the underlying influences that participants reference in their justifications. This was done in the context of participants predicting future recognition performance when making their JOLs (Experiment 1 and 2) and participants predicting recall performance (Experiment 3). The general procedure and majority of methods adopted to analyse the text data were modeled on  Selmeczy & Dobbins (2014).
Procedure The study was programmed using PsychoPy  (Peirce, 2007) , with all participants completing the delayed JOL task individually on a computer, in the presence of the 1 The primary data of interest were the written justifications. In both experiments each participant could provide at most 3 justifications per JOL type with most participants providing fewer justifications than the allowed maximum. Further, participants used some JOL responses more commonly than others. In Experiment 2 this was especially pronounced with, for example, the yes-guess JOL response being used relatively infrequently by all participants. At the outset of Experiment 2 we decided that we wanted to collect at least 100 justifications per JOL response type to make the dataset comparable to Experiment 1. This condition and the less evenly distributed nature of JOL responding in Experiment 2 meant that the sample size of Experiment 2 was necessarily larger than that of Experiment 1.
Text analysis methods Text data pre-processing. Before any text analysis was carried out, we corrected spelling mistakes in the text and removed articles (a and the). We also removed justifications where participants explicitly indicated they wanted to change the previously rendered JOL response (in total, three justifications in Experiment 1, six justifications in Experiment 2). In all reported analyses, we aggregated the descriptive reports for each JOL confidence level and response type across participants for comparison. A minimum of 102 justifications was collected per JOL type (see   1997). The online LSA tool (available at http://lsa. colorado. edu/) offers a semantic space that has been trained on general reading corpus with factors  Dennis, 2006). We used this to classify the semantic similarity between each justification and the cuetarget pair it was written in response to.
Once an SVM has been trained it can be used to classify new data which will be assigned either of the categories the SVM has been trained on, based on which side of the margin it falls. The SVM analysis was implemented with scikit-learn, an open source toolkit developed for Python  (Pedregosa et al. , 2011). To compare two JOL response categories (e. g. 0% vs. 20% JOL), the justification responses for both were labeled and combined. We trained the classifier on a randomly selected half of the combined data with a linear kernel and a cost value of 0. 10 and tested it on the other half. Once the classifier was trained, it was then used to classify the remaining half of the data, and its performance was evaluated by its ability to correctly distinguish the JOL for which a given text was written.
Participants All 64 participants were native English speakers and randomly assigned to one of two experimental conditions. Six participants were excluded from data analysis because they did not follow the instructions when writing their justifications (e. g. referring to multiple items rather than only to the JOL given on the last trial. This left 58 participants (19 men, mean age = 21. 3, SD = 2. 7). Of those, 28 were in the numeric confidence JOL condition and 30 in the binary JOL condition. 4 The participants were all students at the University of St Andrews and received £5 as reimbursement for taking part in the study. The study was granted ethical approval by the University Teaching and Research Ethics Committee at the University of St Andrews.
3 pg/ml), IL-13 (3. 7 pg/ml), IL-15 (2. 1 pg/ml), IL-17 (4. 9 pg/ml), Eotaxin (40. 9 pg/ml), basic FGF (27. 2 pg/ml), G-CSF (2. 4 pg/ml), GM-CSF (63. 3 pg/ml), IFN-γ (92. 6 pg/mL), IP-10 (18. 8 pg/ml), MCP1 (2. 1 pg/ml), MIP-1α (1. 4 pg/ml), MIP-1ß (2 pg/ml), PDGF-BB (7 pg/ml), RANTES (2. 2 pg/ml), TNF-α (5. 8 pg/ml) and GM-CSF (0. 75 pg/ml), VEGF (5. 5 pg/ml). Briefly, 50 μL of CSF or serum were incubated with antibody-linked magnetic beads for 2 h, washed twice and incubated for 1 h with biotinylated secondary antibodies. A final incubation step of 30 min with streptavidin-phycoerythrin secondary antibodies preceded acquisition on the Luminex 100IS and the data were acquired using the StarStation 2. 0 software (Applied cytometry systems, Sheffield, UK).
Assessment of HMGB1 and anti-HMGB1 autoantibody concentrations in CSF and serum Quantification of CSF and circulating HMGB1 was performed with an ELISA kit (IBL, Hamburg). Considering that HMGB1 forms highly inflammatory complexes with ssDNA, LPS, IL-1beta, nucleosomes, and also antibodies  [33] , and taking into account our own data aimed at identifying what form of HMGB1 (bound or unbound) is detected by this ELISA kit, we considered that this kit only detected the unbound HMGB1 molecule. We called it "residual HMGB1". The concentrations of anti-HMGB1 autoantibodies in CSF and serum from patients' samples were determined with an in house quantitative ELISA assay. 96-well plates were coated overnight at 4C with recombinant HMGB1 (HMGBiotech, HM-115) in PBS. Simultaneously, coating of serial dilutions of the calibrator human serum IgG (Sigma) was performed.
After washing the plates with PBS/ 0. 05% (v/v) Tween ® 20, unbound sites were blocked with PBS/2% (w/v) BSA. Since anti-HMGB1 antibodies were reported to bind HMGB1 in serum  [34] , we dissociated these complexes before titration of the antibodies. Thus, CSF and serum samples were treated with 1. 5 M Glycine and further diluted with 1. 5 M Tris, v/v, pH 9,0. Treated samples were then immediately diluted (from 1/10 to 1/1000) and distributed on coated plates. Goat anti-human IgG alkaline phosphatase-conjugated antibodies were added, and detection of HMGB1-specific antibodies was performed after 30 min of incubation at 37 °C with 100 μl pNPP substrate. Concentration of HMGB1-specific antibodies was calculated according to the standard curve obtained from standard immunoglobulin solution absorbance by Ascent software, Thermo Electrocorp, as we previously reported  [35].
Statistical analyses Data are expressed as Median and IQR range. Statistical comparisons were performed using a two-tailed unpaired non-parametric Mann-Whitney U test for comparison between groups, and the Wilcoxon's signed rank-tests was used for comparisons within study group. The relationship between the panel of immunological and neurological biomarkers and early neurological impairment was assessed by receiver operating characteristics area under curve (ROC-AUC) analysis. A Spearman rank test was used for all correlations. Statistical analyses were performed with Prism version 6. 0a (GraphPad Software, Inc).
However, NAA/Cr ratio was not able to discriminate between patients with no HAND and patients with no HAND with a single deficit, the average AUC being 0. 563 and p > 0. 05 (Fig. 4B ). Regarding the correlations of chemokine concentrations with cerebral metabolites, higher levels of MCP1 correlated with higher levels of Cho/Cr (Spearman r = 0. 25, p = 0. 05) and higher levels of Cho/NAA (Spearman r = 0. 26, p = 0. 05), both ratios being markers of inflammation (Fig. 5A ). Using ROC curves, the Cho/NAA ratio displayed the best diagnostic performance for identifying patients with HAND, with a cut-off identified at 0. 575 (data not shown). Considering this cut-off, significantly increased levels of anti-HMGB1 were associated with higher levels of Cho/NAA (p = 0. 04). In a similar way, IP-10 levels were increased in patients with Cho/NAA ratio >0.
BACKGROUND: The emergence of central sleep apnea (CSA) during positive airway pressure (PAP) therapy has been observed clinically in approximately 10% of obstructive sleep apnea titration studies. This study assessed a PAP database to investigate trajectories of treatment-emergent CSA during continuous PAP (CPAP) therapy. METHODS: U. S. telemonitoring device data were analyzed for the presence/absence of emergent CSA at baseline (week 1) and week 13. Defined groups were as follows: obstructive sleep apnea (average central apnea index [CAI] < 5/h in week 1, < 5/h in week 13); transient CSA (CAI $ 5/h in week 1, < 5/h in week 13); persistent CSA (CAI $ 5/h in week 1, $ 5/h in week 13); emergent CSA (CAI < 5/h in week 1, $ 5/h in week 13). RESULTS: Patients (133,006) used CPAP for $ 90 days and had $ 1 day with use of $ 1 h in week 1 and week 13.
Data Inclusion A U. S. positive airway pressure (PAP) device telemonitoring database (AirView; ResMed) was queried. The study was exempt from ethics review given that the database is deidentified (an institutional review board waiver was provided). Patients and session records satisfying the following criteria were included: random sample representing 30% of database population; patients registered in the United States; first device mode was fixed-level CPAP or automatically adjusting CPAP; use of an AirSense/ AirCurve 10 device (ResMed) reporting apnea-hypopnea index (AHI)/central apnea index (CAI)  13  ; therapy start date from January 1 to 2, October 2015; CPAP therapy continued for $ 90 days; $ 1 day with device usage of $ 1 h in both week 1 and week 13 of therapy; valid data entry (age plausible [< 123 years] and valid data blocks [synchronized]).
The PAP devices used differentiate between central and obstructive apneas using forced oscillation techniques (FOTs) to determine airway patency. 13 Central apneas are scored when upper airway resistance is low and obstructive apneas are scored when the resistance is high. Each session record contained five types of data: (1) patient demographic data, (2) treatment usage, (3) clinical metrics including statistical summary (eg, median, 95th percentile) of leak and pressure (for automatic pressure modes), (4) respiratory events (residual AHI, apnea index, hypopnea index [HI], obstructive apnea index [OAI], CAI, unknown apnea index), and (5) pressure settings (for fixed-pressure modes). For the purposes of this analysis, CPAP refers to both fixed-level and automatically adjusting CPAP. For patients using automatic CPAP, 95th percentile pressure data were regarded as equivalent to the pressure setting for fixed CPAP.
Study Population In the 30% random sample of US patients who started PAP therapy in the assessment window, 189,946 patients were treated with a CPAP device with AHI/CAI reporting. Of the eligible CPAP-treated patients, 133,006 had $ 90 days on CPAP and $ 1 day with use $ 1 h in both week 1 and week 13 (Table  1 ). A flow diagram showing patient identification and selection is shown in Figure  1. The proportion of patient sessions with missing data in at least one data field was 1. 2%.
In addition, the telemonitoring strategy used transmitted data on a daily basis and recordings took place in a home setting where patients' natural sleep was minimally disturbed compared with PSG-based controlled studies during CPAP titration. Furthermore, the large and unselected patient population provides the study with good external validity. Although it permits evaluation of a large data set, there are some limitations to conducting scientific research based on databases created for administrative, rather than scientific, purposes. These include the availability of limited baseline clinical and demographic data, and no information about the severity or type of SDB, CPAP titration, and comorbidities. While we assume that all patients included in the analysis were using CPAP to treat OSA, this assumption may not always have been the case.
Data Processing and Analysis Force and acceleration signals were smoothed with a zero phaselag autoregressive filter (cutoff 10 Hz). A trial was defined as a series of cyclic movements. On average, per trial, participants performed 19. 5 cycles for 0. 7 Hz (SD = 6. 9), 20. 9 cycles for 1 Hz (SD = 2. 2) and 26. 3 cycles for 1. 3 Hz (SD = 2. 5). The largest number of cycles common to all conditions was 17. We analyzed trials and cycles separately. Furthermore, since load force varied differently within a cycle whether we take into account the effects of the rotation or not, we also analyzed four phases of the cycle. Quantile-quantile plots were used to assess normality of the data. Repeated measures ANOVA was conducted on cycle frequency, grip forces and on the regression coefficients between grip force and load force.
When relevant, we assessed the R + a m. The vertical cursor marks the largest discrepancies between the two accelerations. effects of Session (1, 2, or 3), Frequency (0. 7, 1, or 1. 3 Hz), Repetition (1 or 2), Cycle (1 to n) and Phase (1, 2, 3, or 4) on the above variables. Post-hoc comparisons were made using Fischer least significant differences (LSD). Paired ttests of individual subject means were used to investigate differences between conditions on the above variables. Data processing and statistical analyses were done using Matlab (The Mathworks, Chicago, IL). We report partial eta-squared values for significant results (p < 0. 05) to provide indication on effect sizes.
ACKNOWLEDGMENTS We wish to thank the MEDES team for their excellent efforts in helping us with the passing of the ethical agreement, recruitment of participants and for carrying out the experiment on the centrifuge. We are also grateful to the participants who took part in these demanding experiments. The authors declare no conflict of interest. This research was supported by the  "Centre National d'Etudes Spatiales" grant 4800000665 (CNES) , the  "Institut National de la Santé et de la Recherche Médicale" (INSERM) , the  "Conseil Général de Bourgogne" (France)  and the  "Fonds Européen de Développement Régional" (FEDER).
A picospritzer (General Valve Corporation, Fairfield, NJ, USA) was used to puff-apply baclofen (300 µM in ACSF) or GABA (100 µM in ACSF, in the presence of the ionotropic GABA(A) and glutamate receptor antagonists) from a glass pipette in stratum radiatum at a distance of about 100 µm from the soma. The pressure varied from 10 psi to 20 psi, and the duration of the puff was 100 ms. In a set of experiments describing the effects of the GABA(B) receptor blockers, GDPs were evoked by electrical stimulation in the stratum radiatum via a theta glass electrode or via a bipolar nickel-chrome electrode. Extracellular and patch-clamp recordings were digitized at 10 kHz with a Digidata 1440A interface card (Axon Instruments) and analyzed offline using MATLAB (MathWorks, Natick, MA, USA) routines.
Data Analysis GDPs were detected from the extracellular recordings either from MUA burst analysis as described previously  (Khalilov et al. , 2015)  or from the LFP. In brief, raw data were preprocessed using custom-written functions in MATLAB (MathWorks, Natick, MA, USA). Raw data from extracellular recordings were explored to detect MUA, following which the raw data were down-sampled to 1000 Hz. MUA was detected at a band-passed signal (>400 Hz and <4000 Hz), where all negative events exceeding 3. 5 standard deviations were considered to be spikes. Further analysis of extracellular units and LFP data were performed using custom-written, MATLAB-based programs. When MUA bursts were used for the detection and analysis of GDPs, MUA in the CA3 pyramidal cell layer was binned in 20 ms windows and smoothed with a sliding Gaussian window with length of 10 ms.
ACKNOWLEDGMENTS This work was supported by  INSERM (LIA) , the program of competitive growth of  Kazan University , the subsidy allocated to KFU for the state assignment 6. 5408. 2017/9. 10 in the sphere of scientific activities and  RFBR  (grant  14-04-01457 ). We thank  Jean-Luc Gaiarsa  for the valuable comments on the manuscript.
2. Pipette [Ca 2ϩ ] was 50 nM as calculated with Webmax software (http://www. stanford. edu/ ~cpatton/webmaxcS. htm), the composition of the 200 nM pipette [Ca 2ϩ ] solution was calculated with the same software. Single-channel currents were recorded with an EPC 7 PLUS patch-clamp amplifier (HEKA Electronik). The holding potential was Ϫ80 mV and pulse duration of voltage steps to various potentials was 15 s long. Voltage ramp stimulation ranged from Ϫ70 to ϩ70 mV and was applied over a 10 s time period. Currents were filtered by a 7-pole Bessel lowpass filter at 1 kHz cutoff frequency. Data were digitized at 2 kHz using a NI USB-6221 data acquisition device (National Instruments) and WinWCP acquisition software designed by Dr. J. Dempster (University of Strathclyde, UK). Hemichannel membrane charge transfer (Q m ) was calculated by integrating the unitary current traces over the duration of the voltage step as follows: Q m ϭ ͵idt.
45 mM EGTA and 0. 14 mM CaCl 2 were added to the control intracellular astrocyte solution to maintain intracellular free CaCl 2 at a steady-state concentration of 50 -80 nM (calculation by Web-MaxChelator; adapted from  Henneberger et al. , 2010). When required, the currently most effective serine racemase inhibitor L-erytho-3hydroxyaspartate (HOAsp) (Wako Chemicals; K i ϭ 49 M) was added to the astrocyte intracellular solution  (Strisovsky et al. , 2005). Whole-cell currents were recorded with a MultiClamp 700B amplifier (Molecular Devices), sampled with a Digidata 1322A Interface (10 kHz sampling, 2 kHz filtering), and the analysis was performed with pClamp9 software (Molecular Devices). Series resistances were compensated at 80% for pyramidal cells and astrocytes. Input resistance was measured in voltageclamp mode by applying hyperpolarizing voltage pulses (Ϫ10 mV, 150 ms) from a holding potential of Ϫ60 mV for pyramidal neurons and from a holding potential of Ϫ80 mV for astrocytes.
Intercellular diffusion of sulforhodamine B was captured thereafter with the CCD camera (Pixelfly QE; Cook) on different focal planes and the number of surforhodamine B-positive cells (i. e. , cells dye coupled to the recorded astrocyte) was determined using ImageJ software  (Liu et al. , 2013). Recording of NMDA and AMPA synaptic currents in pyramidal neurons. NMDA EPSCs were recorded in response to electrical stimulation of L2-3 after blockade of GABA A receptors by picrotoxin (100 M) and AMPA receptors by NBQX (10 M) at a holding potential of ϩ40 mV. The NMDA EPSCs were abolished by the bath application of (2 R)amino-5-phosphonovaleric acid (D/L-AP5, 50 M), an NMDAR blocker (see Fig. 2A1 ). AMPA EPSCs evoked by L2-3 electrical stimulation were also recorded at a holding potential of Ϫ70 mV in presence of picrotoxin (100 M), a GABA A receptor blocker, and D/L-AP5 (50 M) to block NMDA EPSCs.