dataesr/scientific-paragraphs-categorization huggingface dataset:
Marginal age distributions Since the AH-RJMCMC method samples the joint distribution of the model space, we can derive the (joint) posterior distribution of any single variable, or subset of variables, by marginalization. Of particular interest is to examine the joint posterior distribution of the age and intensity of any given datum, which is simply achieved by collecting 'on the fly' the age a j (specified within each model) and its associated intensity, g(a j ), interpolated from the vertex information of the model. On the left the marginal posterior age distribution is comparable to the prior, so the Synt-Paris700 data offers no new information about the datum's age beyond what is already assumed in the prior. Although the posterior distribution of intensity is centred on approximately the same value as the datum, it has a much smaller standard deviation.
A P P L I C AT I O N T O T H E PA R I S 7 0 0 D ATA S E T We now apply the age-hyperparameter methodology to the Paris700 data set; the key results are shown in Fig. 9. As for synt-Paris700, the average, median and mode follow each other closely, and post 0 AD have a focused intensity density giving confidence in the posterior. Other diagnostic plots are given in Appendix D, which includes confirmation of the assumption of normally distributed intensity errors. The time variation after 500 AD is broadly similar to  Genevey et al. (2016) , which is not surprising given the densesampling of the secular variation and the small scatter. There are several data points which do not fit the general trend, which include a low intensity value (about 40 μT) around 100 BC. To gauge the effect of outlying results, Fig.
oup. com/gji/article-abstract/215/3/2008/5101441 by CNRS user on 30 September 2019 Figure  12. A comparison of posterior distributions of intensity using variants of the Paris700 database. From top to bottom: a reproduction of our AH-RJMCMC method applied to Paris700 (Section 5); the same as above but with assuming the ages are known to be exactly their midpoint values; as above with twice the intensity error budget; assuming known ages with a minimum intensity error of 5 μT. of zero with twice the error budget on intensities, and age errors of zero with a minimum intensity error of 5 μT (as assigned by  Korte & Constable (2011)  to all intensity data for the construction of global field models). There are two key aspects of note. First, comparing the top two panels of the figure shows that assuming fixed ages causes rapid changes in the posterior intensity (which are absent when the ages are allowed to be free parameters), particularly for relatively sparsely sampled epochs (here clearly identified pre 0 AD).
A P P L I C AT I O N T O T H E H AWA I I DATA S E T We now turn our attention to the data set of the Hawaiian area extracted from Geomagia. v3 (Brown et al. 2015)  that is much more sparse in time, and which has data ages and intensities both normally distributed. We adopt an extended prior for the intensity to be uniform in the range 10-100 μT because of the greater range of intensities in the data, and also increased the maximum number of internal vertices to 100. The increased scatter in the data (compared with Paris700) means that a converged posterior requires a longer Markov-chain. The results are summarized in Fig. 13 , with a chain length (before thinning) of 5 × 10 6 after burn-in. Other diagnostic plots are given in Appendix E, which includes confirmation of the assumption of normally distributed intensity errors.
As with previous models, the average, median and mode have a very similar time dependence. However, as expected, the more scattered data set gives less confidence in the posterior distribution, reflected in its much broader credible intervals and less focused density. Where the data is particularly sparse or scattered, the 95 per cent credible interval range is about 30-80 μT (for example, at very early times), similar to the range of the prior on the intensity of the model vertices. Apart from for the very recent data dated to the 19th and 20th centuries thanks to direct observations, the width of the credible interval range does not extend much below 20 μT. This should be contrasted with the posterior from the Paris700 data set, which has a typical credible interval width of just 5 μT. In terms of KL divergence, typical values for the Hawaiian data set are between 0 and 1 for much of the age window, which is much less than the comparable diagnostic of the Paris700 data set (of about 2, for the period post 0 AD).
Thus compared with Paris700, the Hawaiian data do not add as much information to the prior. One aspect of interest is the quasi-periodic behaviour identifiable in the top panel of Fig. 13  post 500 AD. Fig. 14  shows the same power spectral density analysis of Section 5 applied here, for the period 500-2000 AD. The scatter of the lines in the left-hand plot is a reflection of the poorly constrained ensemble of model curves (owing to the sparse data distribution), and in this case the average of the spectra is not similar to the spectra of the average model. Nevertheless, the spectra of the average, modal and median diagnostics for the posterior all have a maximum at around 325 yr. The right plot shows a histogram of the periods of the PSD maxima, with a very broad but bimodal structure with peaks loosely defined at around 210 and 330 yr.
16 , while the right-hand plot shows the datum plotted in sky blue in Fig. 16. Green denotes the posterior, orange the prior distribution on age and the normally distributed likelihood for intensity. (2017), shown in Fig. 19(a). In this case, comparable data gaps of 1500 yr between [4500,6000] BC and [1000, 2500] BC have very different credible interval widths: respectively, enormous (off scale) and narrow. For the same periods, our AH-RJMCMC method gives broad credible intervals whose widths are comparable with each other. An important issue concerns treatment of data points that do not fit with the general trend: of particular note in the Paris700 data set (e. g. Fig. 18a ) are those defining an intensity peak that may have occurred around 250 BC, and the single point defining an intensity low around 100 BC.
Indeed, perhaps a more robust definition of outliers might be to proceed probabilistically, by calculating the probability, p, of any given datum (with its assumed distribution of errors in both age and intensity) falling outside the 95 per cent posterior credible interval bounds. Data for which p is greater than some given threshold value (say) 95 per cent (so that there would be only a 5 per cent probability that the true intensity and age values of the datum came from within the 95 per cent credible interval) could be labelled as outliers; such a procedure would likely result in only a few, if any, data being marked as outlying in the Paris700 data set. A second approach that proceeds within the Bayesian framework is to keep all the data but allow the data intensity errors to Comparison of our AH-RJMCMC method and the Bayesian method of Lanos and colleagues, using a French data set  (Hervé et al.
In passing, we note that our approach, which relies on a piecewise-linear temporal interpolation, is well suited to detect rapid fluctuations of the geomagnetic field, such as archeomagnetic jerks  (Gallet et al. 2003)  or geomagnetic spikes  (Shaar et al. 2011). Next, we comment on the power of our method to assess periodic behaviour, which is only permitted as part of the solution where required by the data. For the Paris700 data set, the fact that our method shows a dominant periodic signal (see Section 5) of about 270 yr is therefore a strong conclusion. Indeed, it is notable that this period is in agreement with the period of about 260 yr deduced by  Genevey et al. (2016) , despite the differences between approaches (in particular, in the long-term trend assumed), adding weight to the robustness of this observation.
Conversely, our method can also be used to assess whether a data set is compatible with the absence of a periodic signal. We explore one such example of the sparse Hawaiian data set  (Tema et al. 2017)  that is similar to that presented in this paper but with more stringent selection criteria. It is worth recalling that our own data set can be viewed as a blind (though reasonable) use of a global database or a data compilation. Tema et al. (2017)  identified four consecutive peaks in the intensity evolution from 2000 BC to 2000 AD. Fig. 19(a)  compares results from our method with that of Lanos (see Fig. 6c  of  Tema et al. 2017). Although our method fits a quasi-oscillatory signal post 0 AD, we do not find evidence for the final oscillation around 1500 AD: this is because the data are actually consistent with linear behaviour and do not require an oscillation.
In this respect, the curve derived from the AH-RJMCMC method appears quite similar to that proposed by de Groot et al. (2013) constructed using another Hawaiian data selection. Following an application of our method, Fig. 19(b)  shows the mean posterior value of each data age and corresponding intensity, and shows that a linear dependence is compatible by shifting the data within their error bounds. In a parallel study, a similar assessment of testing whether data can be shifted (within error bounds) to fit an overall trend was recently performed by  Korte & Constable (2018)  to assess geomagnetic spikes, although in a more ad hoc and non-probabilistic fashion. Overall, we find that this Hawaiian data set is entirely consistent with a much less oscillatory signal with no obvious period (our red curve).
A C K N O W L E D G E M E N T S PWL like to thank Université Paris Diderot and the Institut de Physique du Globe de Paris for funding two research visits in 2015 and 2016, during which much of this work was begun. PWL was partially supported by the Natural Environment Research Council grant NE/G0140431. TB is supported by the European Union's Horizon 2020 research and innovation program under Grant Agreement No. 716542. We acknowledge support from the INSU (PNP) programme. We are very grateful to Maxime Le Goff for running his sliding window algorithm on our data, and for helpful comments. We also thank Gabrielle Hellio and an anonymous reviewer for constructive comments on the manuscript. All figures were created in Python using Matplotlib  (Hunter 2007). All authors contributed to the ideas behind the approach taken in the manuscript; YG provided the data sets and background on archeomagnetic secular variation curves; AF, TB and PL developed the methodology and wrote the code; PL generated the results and wrote the paper.
The magnetic intensity of each data point is assumed to be normally distributed, with a mean and standard deviation estimated from laboratory analysis. The age of each datum is unknown but assumed uniformly distributed within a given historically dated interval. All data points were acquired using the experimental method developed by  Thellier (1959)  or using one of its variants  (Coe 1967; Aitken et al. 1988; Yu et al. 2004). At least 2 partial-thermoremanent magnetization (p-TRM) checks were carried out during the experiments to assess the absence of alteration of the magnetic mineralogy during the thermal treatment. Each archeointensity result relies on the average of at least three intensity values regardless of whether this average was performed at the fragment level or at the fragment group level.
A2 Hawaii To examine the effect of data sparsity on our method, we consider a data set of Hawaiian intensity measurements taken from the Geomagia. v3 database  (Brown et al. 2015). Each data point has both an intensity and age that are assumed normally distributed; the age estimate comes from radiocarbon analysis except for the most recent data whose ages are constrained by historical observations. For the latter data, we arbitrarily fixed the age standard deviation σ = 0. 5 yr. Our approach here relied on much less selective criteria, which nevertheless led to a data set much more sparse than the Paris700 compilation. We retained the results whose quality is supported by an alteration test, regardless of the nature of this test, and which possess uncertainties in both intensity and in age.
Image acquisition Images of patterns were acquired using InnoScan 1100 AL fluorescence scanner (Innopsys) through simultaneous acquisition at 3 different wavelengths (488 nm, 532 nm and 635 nm). The fluorescence images were analysed for quantification of fluorescence intensity using Mapix software (Innopsys) or ImageJ software. Coefficients of variation (CV) were calculated as the quotient of standard deviation over the mean of fluorescence intensities. Images of cells were acquired by confocal microscopy (Zeiss, LSM 710, 63 x objective).
Acknowledgments This work was supported by the national research agency ( Agence Nationale pour la Recherche) ,  "LABCOM" program  ( ANR-13-LAB2-0009-01 ). It was partly perfomed in LAAS-CNRS micro and nano technology platform (member of the  French RENATECH network ). We thank  Charline Blatche  ´and  Sandrine AssieSouleille  for their assistance in cell culture and microfluidic experiments. We are grateful to  Sophie Allart  and  Danièle Daviaud  for technical assistance at the cellular imaging facility of INSERM UMR 1043, Toulouse.
Funding:  Agence nationale pour la recherche (ANR)  www. agence-nationale-recherche. frLABCOM program:  ANR-13-LAB2-0009-01  The study, shown in the submitted article was done into BIOSOFT lab: a joint lab between  LAAS-CNRS  (an academic lab) and INNOPSYS (a SME dealing with instrumentation). BIOSOFT is funded by the  "Agence nationale pour la recherche (ANR) ", www. agence-nationale-recherche. fr and the  LABCOM program :  ANR-13-LAB2-0009-01. This organization (ANR) did not play a role in the study design , data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials. INNOPSYS did not fund the study. The grant was given directly from  ANR  to the  LAAS-CNRS. INNOPSYS provided support in the form of salaries for authors JCC, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Congruently, genetic deletion of the catalytically active subunit presinilin-1 (PS1) is embryonically lethal in mice  [68, 81]. Conversely, BACE1 knock-out mice are viable and fertile. Recent studies have revealed mechanism-based adverse effects of BACE1 inhibition in animal models  [16, 26, 88] , but these effects are relatively mild compared to γ-secretase inhibitor-induced deficits. Hence, BACE1 is currently one of the prime therapeutic targets for decreasing cerebral Aβ levels. However, even though BACE1 inhibitors efficiently lower brain Aβ levels  [48, 59, 60] , so far two clinical trials with mild-to-moderate AD patients failed due to unspecific side effects  [60]  or lack of efficacy, as documented by the recent failure of the phase 2/3b EPOCH trial of verubecestat (ClinicalTrials.
gov identifier NCT01739348). The phase II trial of the BACE1 inhibitor LY2886721 was suspended due to possible liver toxicity  [60] , which was ascribed to unspecific side effects of the drug. Some concerns were also raised about potential mechanism-based effects associated with diminished BACE1 cleavage of β-galactoside α2,6-sialyltransferase in the liver  [51, 55]. However, liver injury does not develop after genetic ablation of BACE1 in mice  [57]  and was not reported in Merck's phase ll/lll EPOCH trial of the BACE1 inhibitor verubecestat. In this latter trial, Merck did not report on safety signals that warranted trial termination even after 18 months of treatment. However, the trial was ultimately suspended due to lack of efficacy to reduce cognitive decline in mild-to-moderate AD patients.
Membranes were incubated with horseradish peroxidase substrate (ECL, GE Healthcare or ECL Plus, Thermo Scientific) for 2 min at RT and signals were captured with the ImageQuant LAS 4000 biomolecular imager (GE Healthcare Life Science). Quantitation of protein was conducted using Multi Gauge software (Fujifilm). Hybridization of calnexin was used as a control for equal loading. Quantitative data were analyzed statistically by using two-tailed Student's t-test.
Confocal imaging Images were acquired with a LSM 780 confocal microscope (Zeiss) equipped with a 40×/1. 4 oil immersion objective. Excitation wavelengths were 405 and 561 nm, and emission was collected at 410-580 nm and 585-735 nm for Methoxy-X04 and BACE1, respectively. In each mouse brain, 3-dimensional 16 bit data stacks of 1024 × 1024 × 100 pixels were acquired in the somatosensory cortex at a lateral resolution of 0. 1 μm/pixel and an axial resolution of 0. 2 µm/ pixel.
Analysis of 3D microscopical data All data stacks obtained by in vivo two-photon microscopy were deconvoluted using AutoQuant (AutoQuantX3, Media Cybernetics). For quantification of amyloid plaques, presynaptic boutons, presynaptic dystrophies as well as BACE1 positive dystrophies, the 3D data stacks of fluorescence intensity were analyzed using custom-written MATLAB software. Initially, local background subtraction was performed to diminish intensity variations among different stacks. Subsequently, a percentile-based intensity threshold was applied, and connected component analysis was used to identify contiguous clusters of voxels. This standard analysis was slightly modified for each of the biological readouts with the detailed analysis described below. To define BACE1 positive dystrophies, the 70th percentile of immunofluorescence signal was used as threshold for each image stack.
Subsequently, individual amyloid plaques were tracked over time. For this purpose, the image data from consecutive timepoints was loaded as time series in Imaris (Version 7. 7. 2, Bitplane). Plaque volumes were extracted by 3D surface rendering and were semi-automatically tracked over time using the surface tracking module of Imaris. To identify events of plaque formation, plaques were tracked back to the first timepoint of appearance and were only assessed when present for at least 3 weeks, to warrant unambiguous distinction from background signal. Therefore, quantification of plaque density and formation only include values up to 8 weeks post-treatment even though imaging was performed up to 10 weeks. Correct tracking was manually checked for each amyloid plaque. For reliable determination of the actual size of each amyloid plaque, the largest extension in XY was determined and the radius was calculated as, radius = √ area∕ , assuming spherical shape of plaques  [37].
BACE1 inhibition mitigates progression of presynaptic pathology To assess, whether the beneficial impact of BACE1 inhibition on β-amyloid deposition mitigates synaptic pathology, the size and density of VGLUT1 positive structures was quantified in APPPS1xVGLUT1 Venus mice  [39, 64]. In each mouse, approximately 20 individual plaques were tracked with high resolution at consecutive imaging timepoints up to 8 weeks after treatment. The VGLUT1 Venus fluorescence pattern appeared punctate with small sphere-like presynaptic boutons distant from plaques and large swollen presynaptic dystrophies in proximity to plaques (Fig. 4a ). Custom-written MATLAB cluster analysis was applied for automated morphological segmentation (Fig. 4b , upper panel), to quantify how the fraction of differently sized VGLUT1 positive structures changes with distance to plaque border.
With increasing plaque size, the fraction of presynaptic dystrophies increased further. After plaques had reached a radius between 10 to 14 µm, the fraction of presynaptic dystrophies remained stable. BACE1 inhibitor treatment did not have an apparent impact on the size distribution of VGLUT1 positive structures for equally Fig. 1  Pharmacological inhibition of BACE1 reduces soluble Aβ levels. a Six-week-old mice were treated for 14 days with vehicle or NB-360 and were sacrificed thereafter to determine Aβ levels. BACE1 inhibitor treatment significantly reduced the levels of Aβ40 and Aβ42 in forebrain and plasma. Data presented as mean ± SEM with **p < 0. 01; ***p < 0. 001; n = 6 mice per group; t-test. b In vivo two-photon imaging was performed weekly in the somatosensory cortex of APPPS1xVGLUT1 Venus mice.
RNA-Seq Data Mapping and Quantification of the TOGE Data A quality check of the raw sequences was made with FastQC 1. Trimming was performed with PRINSEQ (version 0. 20. 3) with the option -trim right. Reads were aligned with a spliced alignment tool to the genome of Solanum lycopersicum (SL2. 40. 22 and ITAG. 2. 3 GFF3 annotation file) with TopHat 2 (version 2. 0. 14) (Kim et al. , 2013). On average, between 80 and 90% of the reads were aligned to the reference genome. We randomly down-sampled the reads to generate data sets of 2. 5, 5, 7. 5, 10, 15, and 20 M reads using the python script get_subset. py before alignment on the reference genome 2. We used SAMtools view option -s for down-sampling the reads after the alignment on the reference genome  (Li et al. , 2009). Raw counts were generated on each gene by using HTSeqcount (version 0.
DE Analysis of the TOGE Data All DE analyses of the Number of DE genes (section Number of DE Genes of the TOGE Data) and Power (section Power Analysis of the TOGE Data) were performed with R software (version 3. 2. 0) and the dedicated edgeR package (version 3. 6. 8) (Robinson et al. , 2010; R Core Team, 2015). No filtering was applied. The TMM normalization method was performed to normalize the counts among the different samples  (Robinson and Oshlack, 2010; Maza et al. , 2013; Maza, 2016). The dispersion parameter was estimated in two different ways, depending on the number of replicates, to enable a more robust estimation: if the number of replicates was less than or equal to 4, we estimated the dispersion by the CommonDisp function; otherwise, the dispersion was estimated using the TagwiseDisp function  (Robinson et al.
bioinformatics. babraham. ac. uk/projects/fastqc 2 https://github. com/happykhan/nfutil/blob/master/get_subset. py power. Obviously, for eight replicates and each library size, we only had one data set and then one indicator. For the calculation of the power, we needed a reference list of DE genes. For this purpose, we chose the DE genes that were found with all available information (i. e. , with eight replicates and 20 M reads) and with a very stringent adjusted p-value = 0. 0001. Then, for a given DE gene list, the power was calculated by the ratio of the number of true DE genes (i. e. , genes that are considered to be DE and that belong to the reference list above) to the total number of genes in the previous reference list (see also the section "Sensitivity and Specificity" below). Moreover, to calculate the stability of each indicator, we retained, for each combination of replicate number and library size, the DE genes that were common to all 45 data sets.
Gene Ontology (GO) Analysis of the TOGE Data We performed an enrichment analysis with the goseq R package (version 1. 20. 0) (Young et al. , 2010). As tomato is not referenced in goseq, we manually built the list of GO biological process (BP) identifiers and lengths of genes. The tomato GO terms were downloaded from the UniProtKB database 3. The goseq tool is suitable for RNA-Seq enrichment analysis, since it allows an adjustment for gene selection thanks to differences in gene lengths, which are known to affect the variance of gene expression estimates. BP categories with p-values less than 0. 05 were considered to be significantly enriched. For each combination of depth and replicate number, lists of the common BP categories obtained for the 45 essays were extracted and analyzed (in the same way as described above for the stability of the number of DE genes and the power).