dataesr/scientific-paragraphs-categorization huggingface dataset:
Our data show that selective ablation or blockade of P2RX7 ameliorates the mdx dystrophic process both short and long term and does not cause detectable side effects in this DMD mouse model. Given that specific P2RX7 antagonists have been in human trials for other conditions  [75] , these could be readily repurposed for treatment of this lethal disease. Furthermore, nucleoside reverse transcriptase inhibitors (e. g. , zidovudine) have been shown to block P2RX7 activation in a number of disease models  [76]. These drugs, with an extensive pharmacological and safety record for human use, including in children, appear ready for trials in DMD. (Volume Graphics). The calculation of the morphometric parameters was carried out by importing the images into ImageJ software. A region of interest (ROI) containing trabecular bone only was defined, and for each specimen the following morphometric parameters were determined: BV/TV, trabecular thickness (Tb.
Th) and Tb. Sp. Measurements were averaged over ten consecutive slices just below the femoral head. (TIF) S1 Table. Depiction of the qPCR data comparing relative gene expression levels in gastrocnemius-derived mRNAs from WT, mdx, Pf-mdx/P2X7 -/-, and G-mdx/P2X7 -/-mice. Statistically significant differences in ANOVA with Tukey's post hoc test at p < 0. 001 are depicted in red and green for up-and down-regulated genes, respectively, and values (2 -ΔΔCT ) shown. Not included were the following genes, where no statistically significant differences in qPCR analyses were found: Bcl10, C1ra, C2, Casp3, Casp9, Cd163, Cebpa, Csf1, Cxcr3, Cxcr4, Fas, Fos, Foxo3, Gpx4, Hif1a, Hspa1b, Hspd1, Ifngr1, Igf1, Il6ra, Il10rb, Irf1, Irf3, Irf9, Jun, Mmp2, Nfkb1, Nos2, P2ry12, Prdx5, Ptges, Rbpj, Rplp0, Socs1, Socs3, Sod1, Stat1, Stat3, Tgfbr1, Tnfrsf1a, and Twist1.
The following genes gave expression values below the detection threshold or ill-reproducible results in repeated experiments: Adora1, Bdnf, Ccl5, Il1a, Il6, Il17a, Il18, Pla2g5, Serp-ing1, and Tnfrsf1b. (PDF) S2 Table. Expression of fibrosis-related and collagen genes estimated using RNA-Seq. Fragments per kilobase per million fragments mapped (FPKM) values as calculated by Cuffdiff and Cuffnorm (Cufflinks suite, http://cole-trapnell-lab. github. io/cufflinks/) are given in the tables cuffdiff_fpkm and cuffnorm_fpkm. The results of the differential expression analysis performed using Cuffdiff are provided in the table cuffdiff_diff_expression. (XLSX).
Acknowledgments We thank the  CBU (University of Portsmouth)  staff for their expert assistance in in vivo experiments,  Dr. Ravikumar Muthuswamy  for help with Taqman analyses,  Dr D. Blake  for dystrophin antibody,  GlaxoSmithKline  for the P2RX7 knockout mice, and  Pietro Ghezzi ,  Peter Sicinski , and  Matt Guille  for helpful discussions.
For stable atoms and molecules, these properties are easily obtained by measurements in reference gas cells, where species densities (ideal gas law calculation) and absorption length (cell length measurement) are perfectly controlled. In uniform plasma environments, density precision below 1% is attainable for such species. For atomic and molecular radicals or metastable species, the precision is limited by the accuracy of the spectroscopic properties provided in the databases. Secondly, absorption-based techniques provide in situ diagnostics for atmospheric plasmas. These techniques can offer direct knowledge on space-and time-resolved species densities and temperatures, except for cases where the low sensitivity imposes the necessity to study species concentrations by gas extraction followed by analysis in multipass cells.
g. OES measurements, where excited states are probed. In the latter, a kinetic model is required to deduce the densities of molecules in ground electronic states from the measured densities of excited states. Furthermore, measured absorption data can be fitted in real-time using molecular parameters such as line strengths and pressure-broadening coefficients listed in for example the HITRAN database. No models or further analysis after the measurements is necessary. Not only ground state densities can be gained from absorption spectroscopy. In plasma diagnostics, for example, the gas temperature is an often-sought parameter. A large number of experiments provide temperature measurements from the ro-vibronic distribution of the electronic excited states using OES (see e. g. [24] ). However, these are based on assumptions about pumping and loss mechanisms of the excited states, which mainly include excitation by direct electron impact and loss by spontaneous emission.
Absorption spectroscopy: what do you need to know? In this chapter, a brief overview of the most important fundamental knowledge required to perform absorption spectroscopy is described. As this paper does not intend to be a textbook on absorption spectroscopy, for a more in-depth description of the basics of absorption spectroscopy the reader is referred to various books  [26, [29] [30] [31]  and papers  [12, 15, 32, 33]. Throughout the review, fundamental equations have been listed according to the SI system. Equations explaining a mechanism are listed according to their original publication and the reference is given. It is very common in the field of spectroscopy to use CGS units. In view of the 'best practice' approach of the special issue cluster, the respective equations are given in the system used within the respective database and approaches typical for the spectroscopic research field.
Wavelength calibrated absorption spectra and thus absolute absorption line or absorption band positions are determined by various techniques: Usually in the VUV to NIR spectral domains, calibrated spectrometers or wavemeters are employed, while in the MIR, Fabry-Pérot etalons and reference gas cells are used. The unique set of transition wavelengths for each species is generally available in spectroscopic databases (see section 2. 5). This allows the distinct identification of a particular species present in a plasma. Limited instrumental resolution and overlapping of multiple lines may hamper plasma species identification. This is relatively easily solved by the identification of several absorption lines of the same species. Note that a particular absorption transition in plasmas is observed only if there is a significant population density in the lower state.
The bracket term in the right side of equation  (7)  accounts for stimulated emission. In atmospheric pressure plasmas, the population of the upper state of most atoms is a very small fraction of the lower state, because of the large energy separation between levels. Therefore, the bracket term in equation (  7 ) normally equals to one. For simplicity, the above expressions are using atomic oscillator strengths. This spectroscopic property can be found for instance in the NIST Atomic Spectra Database  [34]. We recommend this database, because it includes the statistical weights of the two levels, so it is easy to apply. Notice that in equations (  6 ) and (  7 ) the absorption is described in wavelength space (as this is mainly being used for UV and VIS atomic transitions in literature). For practical purpose, equation (  7 ) can be written as ∫ λ λ λ ( ) = × - k f N d 8.
R the weighted transition-moment squared. The statistical weight and the total internal partition function include the electronic, vibration, rotation and nuclear spin terms (see for details  [36, 37] ). The term in the squared brackets accounts for stimulated emission. While for electronic transitions this term can be neglected (large ∆E), in the case of rotational-vibrational transitions probed in the electronic ground state an important population is present in the upper state. This occurs even in room temperature plasma environments. Therefore stimulated emission is often significant and needs to be considered. Most of the above molecular parameters can be found in the HITRAN database  [38, 39]. While the theoretical calculation of the line strength is complex, its measurement is easily obtained for stable molecules.
Experimentally, a reference cell is filled with a molecular gas at ambient temperature and controlled pressure. The gas density is calculated based on the ideal gas law. Knowing the length of the cell, the integrated absorption coefficient is then measured and the S(T) at room temperature is obtained using equation  (10). For radicals, however, this procedure becomes very challenging. Due to their short lifetimes, a complex apparatus is needed to generate and maintain high radical densities, and calibration methods are then used to measure absolute radical population (e. g. [37] ). Therefore, much less spectroscopic parameters of radical species are found in databases, strongly limiting absolute radical density measurements in plasmas nowadays.
This parameter is transition dependent  [35]. The broadening coefficient is often tabulated in literature in spectroscopic databases (see section 2. 5). If the broadening coefficients are not tabulated in the literature, which is often the case for atoms, then they have to be calculated using the analytical expressions of resonance and van der Waals broadening as one can find in the book by  Griem ([30] , p 97 and 99). Resonance broadening occurs if either the lower (l) or the upper (u) state of the radiative transition under consideration is the upper level of a resonance transition, i. e. if the level is connected to the ground state (g) by an allowed dipole transition  [31]. When including all three perturbing transitions, the resonance broadening can be expressed as  [14]  λ π λ λ λ λ Δ = + + ε ⎡ ⎣ ⎢ ⎢ ⎤ ⎦ ⎥ ⎥ e f g g n f g g n f g g n 3 16.
Databases and tools To obtain absolute species densities in plasmas using light absorption diagnostics, it is essential to know the spectroscopic properties of atoms and molecules. Firstly, data on energy levels together with selection rules provide spectral line positions observed in spectra. This is a key parameter for identification of species present in plasmas. Secondly, oscillator strengths, line strengths, absorption cross-sections and broadening coefficients are key properties for absolute densities and temperature measurements in plasmas. Nowadays, free-access spectroscopic databases contain these properties for most atoms and many molecules. Nonetheless, there is a lack of these properties for a large number of radical species. Furthermore, for atmospheric plasmas, accurate data on the pressure broadening coefficients of transitions is necessary to accurately be able to use fitting routines to obtain values for the densities and line broadening mechanisms.
NIST atomic spectra database. The NIST Atomic Spectra Database  [34]  of the National Institute of Standards and Technology (NIST) is particularly adequate if densities of atoms and atomic ions in ground and excited states (e. g. resonant, metastable) are to be probed by absorption spectroscopy. Energy levels and transitions are represented in a Grotrian diagram and the associated oscillator strengths are provided. This is useful for the choice of transition to use and detection sensitivity calculations. The NIST Atomic Spectra Database includes observed transitions of 99 elements and energy levels of 89 elements, with in total more than 90 000 energy levels and 180 000 lines. A key point for species density measurements in plasmas is the availability of 73 000 transitions for which oscillator strengths are given.
HITRAN database. HITRAN is an acronym for HIghresolution TRANsmission molecular absorption database  [38, 39]. HITRAN is a compilation of spectroscopic parameters of molecules relevant for the prediction and simulation of the transmission and emission of light in the atmosphere including many trace constituents. Initially concentrating on the infrared spectral region, it also contains nowadays data in spectral regions beyond the infrared. The database is freely accessible through the HITRAN website run by the Harvard-Smithsonian Center for Astrophysics. For plasma spectroscopy, the traditional line-by-line spectroscopic parameters, such as the line strength, the Einstein-A coefficient and the pressure broadening coefficients required in fitting routines of spectra to obtain species densities are of most interest.
Molecule / transition X-X A-X B-X C-X D-X B′-X E-X A-B C-B D-A O 2 X NO X X X X X X X N 2 X X + N 2 X OH X X X NH X CO X X C 2 X CN X X Table 3. Molecules and electronic bands covered by LIFBASE  [49]. Molecule/ transition A-X B-X C-X D-X OH X OD X NO X X X X CH X X X CN X CF X X SiH X + N 2 X CO, NO, NO 2 , HNO 3 , and OH. A helpful tool for analysis of absorption spectra is the commercially available software Q-MACSoft-HT that visualizes the HITRAN database  [51].
PNNL database. Although the acronym 'PNNL database' is often used in the spectroscopic community, its full name is the PNNL Northwest-Infrared vapor phase infrared spectral library  [52]. The database is freely accessible and contains information about infrared spectra of over 400 compounds. Although it does not contain information on the line-by-line spectroscopic parameters, the data give enough information to calculate effective absorption cross-sections.
The MPI-Mainz UV/VIS spectral atlas of gaseous molecules of atmospheric interest. A further example for a cross section database is the MPI-Mainz UV/VIS Spectral Atlas  [53, 54] , which is a comprehensive collection of cross sections for gaseous molecules and radicals, primarily relevant to earth atmosphere research. These molecules often are the same that are relevant to atmospheric pressure plasma community. The database lists absorption cross-sections and photon yields for photochemical reactions. It is a collection of data sets from original publications. In addition, several spectra were obtained from databases of several research centers and via private communication. As of February 2015, the database contains 5722 cross section data files for 962 species, 151 quantum yield data files for 30 reactions, 1629 references, and 2536 graphical representations.
2. 5. 7. PGOPHER. PGOPHER  [55]  is a software package to simulate rotational, vibrational and electronic spectra. It has been developed at the University of Bristol in the UK. With PGOPHER molecular spectra can be simulated from the basic spectroscopic parameters generally available in the literature to give line positions and line strengths. In this way, the lack of line-by-line spectroscopic parameters of radicals can be overcome as it has been reported in literature  [56, 57].
UV absorption spectroscopy In the UV-spectral range, many molecular species have high absorption cross-sections-usually several orders of magnitude higher than in the infrared spectral range. This makes UV absorption spectroscopy a relatively sensitive method for species detection. However, the spectral overlap between the species is drastic and measures have to be taken to avoid misinterpretation or over-estimation of species densities. Figure  10  shows absorption cross-sections for 6 different species relevant in many applications of cold plasma jets. The data was taken from  [53]  and citations within the database. These are namely  [152] [153] [154]  for ozone  [155, 156] , for N 2 O 5  [157, 158] , for H 2 O 2  [159] , for NO 3  [160] , for NO 2 , and  [161]  for HONO. In  [162] , the authors studied a broad gap DBD discharge for inactivation of microbes.
Longer treatment times or higher power density result in higher gas volume temperature yielding a reduction of ozone and an increase in NO derived species. FTIR spectroscopy has also been applied to jet diagnostics. While Moravej used IR absorption to indirectly measure atomic oxygen by titration  [141] , Pipa et al  [234]  performed a first study directly on the RF atmospheric plasma jet generated species with FTIR absorption. The studied plasma source was similar to the kinpen. Production rates of molecules NO, NO 2 , N 2 O, HNO 2/3 as well as CO and CO 2 (from impurities or wall reactions) have been detected (see figure  22 ). Spectral data were taken from a compilation of H x N y O z spectroscopic experiments in a review  [235] , and for the case of HNO 2 from the ATMOS  [236]  database as well as HITRAN  [39]  and the commercially available QASoft database  [237].
Special focus was set on lineshape and broadening mechanisms as these can yield valuable information on the studied species. This review on absorption spectroscopy of atmospheric plasma jets in the special issue cluster 'Best practices for atmospheric pressure plasma diagnostics' presents a best practice approach and introduces databases and tools as well as techniques for spatial and time resolution and for sensitivity enhancement. The present status of atmospheric plasma jet absorption spectroscopy is described in an extensive example section. Additionally to absorption spectroscopy on jet-like atmospheric pressure plasmas, other special techniques are introduced that have been used on non-jet-like or low pressure plasmas but are also excellently suitable for plasma jet diagnostics. Absorption spectroscopy was presented from VUV to MIR spectral range.
2) Datasets: Table  I  outlines the characteristics of the three traces we use. ML-100k 1  and ML-1M 1 are traces from the MovieLens  [18]  online movie-recommendation service. They contain 100,000 and 1,000,000 ratings respectively. Jester-1-1 2  is a trace for the Jester  [19]  online joke-recommendation service. It is the first third of Jester's dataset-1. 3) Evaluation metrics: We evaluate recommendation quality in terms of precision and recall. The former evaluates whether peers like the recommendations they receive. The latter evaluates if recommendations cover all the interests expressed by the ground truth in the test set. To evaluate H&S's ability to protect privacy we consider both neighborhood quality, and a privacy metric. Neighborhood quality evaluates how much the neighborhoods provided by H&S resemble the optimal neighborhoods, that is those obtained with the standard cosine similarity metric.
4) Default parameters: The subsequent results correspond to simulations using neighborhood and RPS view sizes of 10 peers. Compact profile sizes depend on the dataset used: 660 and 1473 bits for ML-100k and ML-1M respectively (roughly 40% of the number of items), and 99 bits for Jester. When the number of landmarks is not explicitly mentioned, H&S uses 50 landmarks. This represents a good trade-off between recommendation quality and privacy. For all the metrics except set score, we plot values averaged over all the peers.
precision(user) = recommendedItems ∩ likedItems recommendedItems recall(user) = recommendedItems ∩ likedItems likedItems We consider an item as liked when its rating is greater than or equal to a dataset-dependent threshold (rating ≥ 3 for MovieLens and rating ≥ 0. 0 for Jester). Using userdependent threshold values such as the average rating, the median rating, or the half of the rating range for each peer results in similar or lower precision/recall values. This suggests that users tend to use the available rating range similarly. Peers recommend the r most liked item in their neighborhoods, not including those they have already rated. We check whether a recommended item is liked by looking at the rating given to this item by the recipient in the test set. Figures  5  and 6  show precision and recall values for several values of r.
The former shows the results with the MovieLens datasets, and the latter shows the results with the Jester dataset. For each dataset, we compare the results of the H&S-based system (triangle-shaped) with a lower bound (square-shaped) and a cleartext baseline (circle-shaped). The lower bound consists of a CF system that uses completely random neighbors. The baseline consists of the reference model with full profiles in cleartext, as described in Section II. The absolute values of recall and precision are quite high even with random neighborhoods because we do not consider items for which a user has no rating in the original dataset as potential recommendations. More generally, absolute values of precision and recall depend on the predictability and regularity of the dataset, and their acceptable levels depend on the application.
C. Neighborhood quality In order to evaluate the extent to which neighborhoods are different from the optimal neighborhoods, we use the neighborhood quality measure as defined in Section IV-A3. Figure  7  shows the evolution of neighborhood quality with the size of neighborhoods. For each dataset, it compares the H&S-based system (triangle-shaped) with a CF system using random neighbors as a lower bound (square-shaped) and a variant of our system model using compact profiles (starshaped). Our reference model from Section II by definition achieves a neighborhood quality of 1 and compact profiles provide neighborhoods that are almost identical in the ML datasets. In the case of Jester, they lower neighborhood quality by 50% because the Jester dataset contains only a few items. This makes it more sensitive to the collisions in the Bloom filters.
C. Upper Bound on Information Leakage We conclude our analysis by computing the value of the bound on information leakage in the test configurations of Section IV. To this end, let η be the probability of an element in the M matrix's being 1, and let r be a row vector in matrix M. We can compute the probability of having a non-zero row vector in M as follows. This allows us to rewrite Equation (  13 ) to obtain: ρ 1 = p( r = 0) = 1 -(1 -η) m (14) L ≤ n- 1 2 n D(M ) n D(M ) (ρ 1 ) D(M ) (1-ρ 1 ) n-D(M ) T (D(M )). (15) For the configuration of Section IV, we obtain η = 0. 05. Depending on the dataset and number of landmarks, this leads us to the values of F1 score and leaked-information bound shown in Table  II. The results show that 5, and 3 landmarks allow H&S to provide good similarity scores while leaking respectively no more than 51% and 43% of the information in the compact profiles.
VII. CONCLUSION We have presented Hide & Share (H&S for short), a novel peer-to-peer similarity computation protocol for decentralized KNN computation. We have demonstrated both formally and experimentally that H&S protects the privacy of users in the context of a peer-to-peer recommender. This protection is provided while preserving the system's effectiveness. H&S introduces landmarks, random data structures that are shared between peers comparing their profiles. It leverages a combinations of cryptography and security mechanisms to ensure that these landmarks cannot be diverted to attack the privacy of users. We have shown using three real-world datasets that H&S maintains a strong level of privacy while providing recommendations close to that of an open system with no particular privacy protection mechanism.
ACKNOWLEDGMENTS This work was partially funded by the  Region of Brittany, France , by the  French ANR  project  SocioPlug  ( ANR-13-INFR-0003 ), by the  DeSceNt  project granted by the  Labex Comin-Labs excellence laboratory  ( ANR-10-LABX-07-01 ), and by the  Google Focused Research Award Web Alter-Ego.
Sequencing data analysis Data analysis was performed on a Linux platform using GMAP and GSNAP  [33, 34]  for indexing and alignment, respectively, and Trimmomatic  [35]  for quality control and removal of singletons. Reads were aligned to the AgamP3. 22 genome build (VectorBase, http://www. vectorbase. org, Anopheles gambiae PEST, AgamP3. 22) [36]. Low expression tags were filtered using a threshold of 100 counts per million (CPM). Statistical analysis was performed using the edgeR package  [37]  for R software  [38]. Counts were first fit to a negative binomial generalized log-linear model (glmFit procedure) and the dispersion was estimated by plotting the Biological Coefficient of Variation (BCV) for each biological replicate (Additional file 1: Figure  S1 ). Differential expression (DE) analysis was then performed using the glmLRT procedure.