dataesr/scientific-paragraphs-categorization huggingface dataset:
Microarray Data Processing and Visualization. Scanned Gene Chip images were processed using the Affymetrix GCOS 1. 4 software to obtain the intensity value signal and detection call for each probe set using the default analysis settings and global scaling as the first normalization method. Probe intensities were derived using the MAS5. 0 algorithm. Checking normal distribution of microarray data was performed using the relative log expression plot. To compare the gene expression profiles between sample groups, a probe set selection using the absent/present 'detection call' (present in at least 50% of samples) and a coefficient of variation (CV) ≥40% between samples was first performed. Then, the Significant Analysis of Microarrays (SAM, Stanford University  [13] ) was used to identify genes the expression of which varied significantly between MII oocytes (n=4) and day 3 embryos (n=5), MII oocytes and day 5/6 blastocysts (n=5), and day 3 embryos and day 5/6 blastocysts for human samples and between MII oocytes (n=3) and day 1.
5 embryos (n=3), MII oocytes and day 3. 5 blastocysts (n=3), and day 1. 5 embryos and day 3. 5 blastocysts for mouse samples (fold change, FC >2; false discovery rate, FDR < 5%). Then, significantly upregulated and downregulated genes were analysed using Ingenuity Pathway Analysis (IPA) software (Qiagen, Redwood City) to identify the top canonical pathways and regulator effect networks associated with each comparison. IPA predicted activation and inhibition of upstream and downstream regulators and utilized the regulator effects algorithm to connect identified upstream regulators with the dataset genes and downstream functions and diseases to generate a hypothesis with a consistency score >2 or <-2. Only consistent predicted relationships are shown in networks (i. e. , inconsistent and not predicted interactions were deleted).
2E-07), the epithelial adherens junction signalling (p=6. 6E-05), the cell cycle control of chromosomal replication (p=7. 0E-05), and the G2/M DNA damage checkpoint regulation (p=3. 1E-04). All genes related to these signalling pathways were underexpressed in the EGA compared with the MII stage. The oxidative phosphorylation, mitochondrial dysfunction, and the sirtuin signalling pathway were the top canonical pathways associated with the BL compared with both MII and EGA stage (Figure  1(b) ). At the EGA compared with the MII stage, IPA predicted inhibition of upstream regulators such as Gast/Foxc1, Tbx2/Mitf /E2f1, Tcf4, Xbp1/Tbx2/Mknk1 with high consistency scores between 4 to 6. 5. Underexpression of Gast (x-5. 1, FDR<0. 0001) and Foxc1 (x-2. 2, FDR=0. 02) transcripts promotes cell cycle interphase and cell viability.
s. l. )) is located approximately 185 km inland from the coast in a moderately hilly region which is favorable for LLC formation  (van der Linden et al. , 2015; Adler et al. , 2017; Kalthoff et al. , 2018). A comprehensive mea-surement set-up at this supersite allows the detailed investigation of atmospheric conditions and identification of atmospheric processes relevant to the observed life-cycle of LLC. Meteorological data used here comprise near-surface meteorological parameters, 30-min averaged turbulence fluxes and turbulence variables, which were calculated using the TK3. 11 software  (Mauder et al. , 2013) , and radiation fluxes  (Kohler and et. al. , 2016). The dynamic and thermodynamic conditions in the ABL were measured with radiosondes (normal and frequent) and different continuously running active and passive remote sensing instruments.
SBDART radiative transfer model Vertical profiles of radiative fluxes are computed with Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. SBDART is a software tool which computes plane-parallel radiative transfer in clear and cloudy conditions within the Earth's atmosphere and at the surface and the description of the program is given by  Ricchiazzi et al. (1998). Thanks to the rich data set obtained within the field campaign, many of the input parameters can be specified, allowing for a realistic modeling of radiative fluxes. For example, based on the radiosonde measurements, vertical profiles of pressure, temperature and water vapor are used in the model, while microwave radiometer provides IWV values. The standard tropical ozone density profile is used as input (linearly interpolated to model levels), since this information is not available from measurements.
The advection term (II) is considered here as a residual term since we can not 10 Atmos. Chem. Phys. Discuss. , https://doi. org/10. 5194/acp-2018-776 Manuscript under review for journal Atmos. Chem. Phys. Discussion started: 13 August 2018 c Author(s) 2018. CC BY 4. 0 License. calculate this term for each phase, but is estimated for a specific period in Sect. 4. 3. 1. The radiative flux divergence term (III) is determined using the radiative transfer (SBDART) model  (Ricchiazzi et al. , 1998). The latent heat release term (IV) is relevant in the case when clouds are present, and is determined from the LWP measurements, assuming that the liquid water content obtained by unmanned arial system (UAS) ALADINA  (Altstädter et al. , 2015; Bärfuss et al. , 2018)  in order to get the insight into their characteristics.
Summary and conclusions The data collected during a comprehensive DACCIWA ground-based field campaign at the supersite in Savè (Benin) on 7-8 July 2016 are analyzed in order to investigate the diurnal cycle of LLC and related atmospheric processes. This particular time period is chosen since the conditions during this case study reflect typical conditions and features related to undisturbed south-westerly monsoon flow. These typical features include the onset of NLLJ and the formation of LLC. The associated dynamic and thermodynamic conditions allow the definition of five different phases of the LLC diurnal cycle. These include: the Stable phase indicates the period after the sunset and before the onset of the NLLJ, when the wind speed in the ABL is low and increasing static stability causes the decoupling of the mixed layer from the stable ABL.
Acknowledgements. The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502. We thank the staff of  KIT (Karlsruhe Institute of Technology)  and  UPS  ( University of Toulouse ) for helping to install and run the equipment as well as to staff of INRAB in Savè for allowing to use their grounds for the experiment. We thank  Andreas Fink  and his group for performing the radiosoundings at Accra and  Christine Chiu  for providing the SBDART code which 5 uses Mie phase function calculations.
A surprisingly small portion of reading research has been dedicated to investigating how the visual word recognition process is influenced by embedded words (e. g. , 'arm' in 'charm'), and no research has yet investigated embedded words in a natural reading setting. Covering this issue, the present work reports analyses of eye-tracking data from the GECO bilingual book reading corpus. Word viewing times were analyzed as a function of the number, frequency and proportional length of embedded words. We anticipated two scenarios: embedded words would either facilitate processing due to increased word-letter feedback, or inhibit processing due to increased lexical competition. A main facilitatory effect of embedded words on the recognition process was established, with an increasing number of embedded words resulting in shorter word viewing times and fewer fixations.
g. , 'house', which contains 'hose'), meaning that they did not test true intact embedded words (comprised of contiguous letter combinations). Hence, attempting to draw the bigger picture concerning embedded words, the present paper assesses embedded words in a natural reading setting. This investigation invokes analyses of the GECO book reading corpus  (Cop, Dirix, Drieghe & Duyck, 2017) , which contains eye-tracking data of 18 Dutch-English bilingual subjects and 14 English monolingual subjects reading an entire novel. 4 The eye-tracking data comprise a multitude of variables, including, for instance, word viewing times, which are used as a measure of word recognition speed (e. g. ,  Rayner, 1998). Importantly, the vast size of the GECO corpus provides us abundant statistical power to determine whether and how effects of embedded words are modulated by factors such as length and frequency.
Eye movement data was collected for 59,716 words in the Dutch version of the novel (per two subjects, as Dutch subjects read half of the book in English), and for 54,364 words in the English version. The text was presented in paragraphs (with a maximum of 145 words per display) with black 14-point Courier New font on a light grey background and with triple line spacing. Subjects could press a button on a control pad to move from one paragraph to the next. After each of the 18 book chapters, subjects were presented multiplechoice questions to ensure that they had paid attention throughout the chapter. Reading of the entire novel took approximately four hours and was carried out in four 1-hour sessions. Eye movements were tracked with the EyeLink 1000 system (SR Research, Canada) with a sampling rate of 1 kHz.
Our datasets From the GECO corpus we abstracted two datasets, representing Dutch reading (by 18 Dutch bilingual subjects) and English reading (by 14 monolingual English subjects). We retrieved each subject's first 2000 words with a length between 4 and 15 characters, amounting to a total of 36,000 datapoints for Dutch reading and 28,000 datapoints for English reading. Names (e. g. , 'John') and contractions (e. g. , 'don't') were avoided. For each datapoint we determined the set of embedded words contained by the target word. This was done by checking for each of the target's contiguous letter combinations (e. g. , 'ro', 'oc', 'ck', 'roc' and 'ock' in 'rock') whether it occurred in the Dutch Lexicon Project database of  Keuleers, Diependaele and Brysbaert (2010)  for Dutch words, or the British equivalent by Keuleers, Lacey,  Rastle and Brysbaert (2012)  for English words.
g. , 'farm' in 'farmer') than non-edge-aligned embedded words  (Grainger & Beyersmann, 2017). Finally, for each target word we retrieved the gaze duration (GD) and the total viewing time (TVT) from the GECO corpus. GD reflects the sum of all first-pass fixation durations on a word, and arguably provides the most direct measure of word recognition speed (e. g. ,  Rayner, 1998). TVT represents the sum of all fixation durations on a word (that is, including fixations following a regression), and reflects contextual comprehension. We further determined how many times each target was fixated (with zero times indicating a word skip). Some descriptive statistics about our datasets are shown in Figures  1 2 3. Unsurprisingly, longer targets contain more embedded words on average (which is why target length was added as a factor in our models;  Section 2.
Results Below, our analyses are presented separately for Dutch and English reading. Skipped words (33. 2%) were excluded from the word viewing time analyses. Words with a word viewing time (as reflected in GD) beyond 2. 5 SD from the grand mean (1. 81% of words) were excluded from the analyses of word viewing times as well as number of fixations. We employed linear mixed-effect models (LMMs) with subjects entered as random factor (e. g. ,  Baayen, 2008). 6 The models were fitted with the lmer function from the lme4 package  (Bates, Maechle, Bolker & Walker, 2015)  in the R statistical computing environment. Following  Barr, Levy, Scheepers and Tily (2013)  we determined the maximal random effect structure permitted by the data. This led us to include by-subject random slopes alongside the random intercept.
37, SE = 0. 07, t = 5. 31; in TVT, b = 0. 45, SE = 0. 12, t = 3. 80; in number of fixations, b = 2. 49 * 10 -3 , SE = 5. 30 * 10 -4 , t = 4. 70. Next, we assessed whether the effect of embedded words on target word recognition was modulated by embedded word length. This was done by including embedded word length as a variable in the model alongside number of embedded words and target word length. A significant interaction of embedded word length and the number of embedded words on word viewing times was established, with b = 12. 02, SE = 5. 19, t = 2. 32 in GD, and b = 27. 54, SE = 9. 92, t = 2. 78 in TVT. This interaction was also expressed in the number of fixations: b = 0. 09, SE = 0. 04, t = 2. 16. Aiming to explore the nature of this interaction, we split the dataset in two, based on embedded word length being above or below the median of 0.
Morphology Lastly, we assessed whether the main facilitatory effect of number of embedded words may have been driven by morphological relationships between the embedded words and their supersets. Given the obvious impracticality of manually determining morphological relationships between all items and each of their respective embedded words (necessitating one to evaluate more than 175,000 word pairs within the present datasets), we opted for an alternative strategy whereby we made use of the fact that stems (e. g. , 'farm' in 'farmer') are most often edge-aligned  (Grainger & Beyersman, 2017). We thus reasoned that, if the facilitatory effects were driven by morphemes, these effects should be stronger when isolating items that exclusively comprise edge-aligned embedded words. 8 s it turned out, in Dutch reading, no influence of number of embedded words was observed in the subset of items with exclusively edge-aligned embedded words (  56.
49% of all items): in GD, b = -0. 85, SE = 3. 15, t = -0. 27; in TVT, b = -1. 16, SE = 4. 66, t = -0. 25; in number of fixations, b = 0. 03, SE = 0. 02, t = 1. 35. On the other hand, inhibition was found in items that exclusively contained non-edge-aligned embedded words (17. 25% of all items), albeit exclusively in the GD measure: in GD, b = 12. 26, SE = 5. 77, t = 2. 13; in TVT, b = -3. 87, SE = 8. 13, t = -0. 48; in number of fixations, b = -0. 02, SE = 0. 05, t = -0. 53. In the English dataset, edge-aligned embedded words (53. 20% of all items) were found to facilitate: in GD, b = -18. 52, SE = 5. 26, t = -3. 52; in TVT, b = -10. 60, SE = 6. 00, t = -1. 77; in number of fixations, b = -0. 05, SE = 0. 03, t = -1. 82. No effects were observed in items exclusively containing non-edge-aligned embedded words (26.
The work presented in this paper deals with the fatigue damage assessment of bolted joints. The influence of the preload is particularly underlined as it is shown that it strongly improves the fatigue resistance of the bolt whatever the geometrical parameters are. A 3D tee-stub model with the bolt submitted to preload force is simulated by using the Salome-Meca FEM software. A parametric study is also carried out to analyse the influence of the bolt location m and the column flange thickness   onto the bolt loading. In order to analyse the effect of the stress concentration generated by the head-shank transition of the screw, a second 3D finite bolt element model is developed. This allows to exhibit multiaxial stress states in the vicinity of the fillet radius. Finally, a multiaxial fatigue post-treatment tool has been implemented on Matlab software for damage assessment purpose.
Fatigue analysis tools A fatigue post-treatment tool is implemented on MATLAB software in order to analyse the fatigue behaviour of the bolt. In the case of constant amplitude multiaxial stress states, multiaxial fatigue criteria are useful tools for assessing the fatigue resistance of the material submitted to any periodical stress states cycle  [6]. As multiaxial stress states are observed in stress z x y z y x concentration zones, we have implemented Dang Van and Zenner criteria. The purpose is also to investigate the validity of the so-called critical plane approach versus the integral approach. Dang Van criterion is related to the critical plane approach and Zenner criterion deals with the integral approach.
Influence of the fillet radius on the stress distribution within the screw Accounting for the fillet radius considerably modifies the stress distribution in the stress concentration zone, i. e. in the connection zone of the screw head and its shank. Two cases without fillet radius and with a 2 mm fillet radius have been studied. The fillet radius makes decrease the maximum value of the stress up to 40 MPa (Fig 15. and Fig. 16 ). In the following section the tee-stub is subjected to the dynamic load, for the purpose of the bolt fatigue analysis. We use here the post-treatment tool that is implemented on MATLAB software with Dang Van and Zenner multiaxial fatigue criteria.
Influence of the preload on the bolt fatigue damage assessment The external loading cycle considered for the study of the bolt fatigue behaviour varies from 0 kN to 10 kN and back to 0 kN. The fatigue damage function E in the screw is assessed with the following configuration of the tee-stub: -Bolt location m : 20 mm column flange thickness  : 15 mm As the S-N curves of the bolt material are not precisely known at the present time, some fatigue data are taken from the literature to analyze the influence of the preload on the fatigue behaviour of the bolt in the tee-stub. The considered S-N curve derives from the Wöhler model and is expressed for fully reversed tensile load (R=-1) as: 𝝈𝝈 (N) = 404 -31 log (N) ( 6 ) Where N is the fatigue lifetime. The damage function E is calculated at the fatigue strength of the material at 2.
4, 1 mM EDTA, 150 Mm NaCl, 1% TRITON) supplemented with protease and phosphatase inhibitors (Sigma-Aldrich). Proteins (30-50 micrograms) were separated by SDS-PAGE and transferred to PVDF membrane (Invitrogen). Unspecific binding was blocked in 5% not fat dry milk in TBST buffer (20 mM Tris HCl pH 7. 6, 137 mM NaCl, 0. 5% Tween 20), then the membranes were incubated overnight with the primary antibody diluted in 5% BSA (Sigma) in TBST. After washing in TBST, the membranes were incubated with the secondary HRPconjugate antibodies (Bio-rad 170-6515 or 170-6516) and signals were detected by using ECL chemistry (Advansta). Images were acquired using the ChemiDoc MP imaging system (Bio-Rad). Densitometric analyses were performed by measuring the band intensity for each sample using the Image J software.
Acknowledgments This work was supported by  FIRB 2012  ( RBFR12BUMH ) grant from the  Italian Ministry of Education, Universities and Research ; and by  Sapienza  research project  2016  ( SapMedi2016 ) and Sapienza research project  2017  ( RM11715C78539BD8 ). Funding: MIUR -FIRB;  Sapienza University of Rome.
Compared with other methods, our average posterior distributions largely agree, however our credible intervals appear to much better reflect the uncertainty during periods of sparse data coverage. Because each ensemble member of the posterior distribution is piecewise linear, we only fit oscillations when required by the data. As an example, we show that an oscillatory signal, associated with temporally localized intensity maxima reported for a sparse Hawaiian data set, is not required by the data. However, we do recover the previously reported oscillation of period 260 yr for the Paris700 data set and compute the probability distribution of the period of oscillation. We further demonstrate that such an oscillation is unresolved when accounting for age uncertainty by using a fixed age and with an artificially inflated error budget on intensity.
Archeological data are typically assumed uniformly distributed between two dates determined by typology or historical reference; in contrast, volcanic data are dated using radiocarbon techniques and in this work their errors are assumed normally distributed with zero mean and a given standard deviation, conforming to the databases from which they are extracted. By including the data ages as additional or hyperparameters, a Bayesian formulation of the problem allows us to sample both the data ages and the model description in the same way, producing marginal posterior distributions for the data ages themselves  (Lanos 2004; Hellio et al. 2014; Schnepp et al. 2015; Hervé & Lanos 2017)  alongside that of the archeomagnetic intensity model. In this way, data with different intensities but the same mean estimated age may be judged by the posterior distribution to be associated with distinct ages.
(1) Paris700: The first data set, Paris700, relies on stringent selection criteria (see  Genevey et al. 2013) , has quasi-uniform temporal resolution from about 1000 BC to 2000 AD and comprises data collected from within 700 km from Paris. Its 154 entries are all reduced to the latitude of Paris (48. 9 • N) using the virtual axial dipole moment (VADM) approximation. The data age errors are assumed uniformly distributed within given ranges (shown by the horizontal error bars in Fig. 1 (top panel). (2) Hawaii: To examine the effect of data sparsity and nonuniform sampling on our method, we consider a second data set of Hawaiian intensity measurements taken from the Geomagia. v3 database  (Brown et al. 2015)  over the past 4500 yr. Here, we adopted minimalist (i. e. loose) selection criteria and this case therefore corresponds to extracting a local data set from the global database in a relatively blind manner.
The arrows indicate how the Monte Carlo method allows each parameter to change. data intensities being realized is proportional to e -φ , where φ = 1 2 N data j=1 g(a j ) -F j 2 /σ 2 j , ( 2 ) where σ j is the given estimate of the standard deviation. We note that, because only the ratio of likelihoods is relevant, the constant of proportionality never enters the analysis. In Sections 5 and 6, for the Paris700 and Hawaii data sets, the distribution of residuals will confirm the assumption of normally distributed intensity errors. The integrated likelihood approach of Sambridge (  2016 ) is a generalization of the formula above to the case where the data ages are formally unknown but have a known distribution. By integrating (2) over all its possible values, it is possible to calculate a likelihood weighted by the age distribution; see Section 4.
Recovery of known underlying behaviour In order to test the AH-RJMCMC methodology, we created synthetic versions of the Paris700 and the Hawaii data sets, termed Synt-Paris700 and Synt-Hawaii, both based on the smooth intensity variation of the coupled-Earth (CE) dynamo model  (Aubert et al. 2013). The particular time window in the CE model was chosen such that the magnitude of variation of intensity was comparable to the observations, and is time-shifted in order that it is defined over the same age ranges as the observational data sets. It is worth remarking that the CE model time is scaled to terrestrial ages through considerations of the secular variation  (Lhuillier et al. 2011) , and any resemblance to existing intensity changes at either location is fortuitous. The CE model was sampled at the appropriate latitude/longitude location and, crucially, according to the same age distributions as the Paris700 and Hawaii data sets themselves.
Here, these are at 1400 BC, 800 AD and 1700 AD, and are caused by either poor sampling or because there is insufficient evidence to exclude a linear fit. Also of note is that, for the discrepancy around 1400 BC, the uncertainty bounds are relatively narrow compared with the Hawaiian data set (Fig. 13 ) despite being sampled at the same times and with the same assumed errors on age and intensity. This is because the synthetic data happen to be consistent with a single linear segment (which in our framework will always be preferred if the data allow), whereas for the Hawaiian data set any linear evolution fits poorly. Although the absence of the true solution feature within the 95 per cent credible intervals may be viewed as a limitation of the method, it is really a reflection of the fact that the non-linear Downloaded from https://academic.