dataesr/scientific-paragraphs-categorization huggingface dataset:
Analytical slopes of the linear phase are 0. 3307 for Dirichlet and 0. 2267 for Neumann-Dirichlet case; it is thus coherent that the non linear phase is more advanced on the Dirichlet case (more details are present). We observe same results for the two codes, which permits to validate the curvilinear code. We make a study of the efficiency (a rough measure of the number of points treated) of the code using different degrees for the elliptic solver and different grid sizes and compare it to the polar code; results are shown on Table  1. A "x" symbol is put, when we were not able to run the simulation (UMFPACK raised there an error indicated that not enough memory is available; we have used a local cluster for performing the simulations and the code is sequential). In order to overcome this problem, we can use iterative or parallel solvers; we just checked here on a single Poisson  test (but not on the whole diocotron simulation, as it would last too long) that the conjugate gradient method gives a correct solution, when the UMFPACK solver fails.
Thanks to UMFPACK, we obtain a rather good efficiency of the code. Further work will be continued in order to deal with other geometries (like D-shape or polygonal shape) and finer meshes, using parallel and/or iterative solvers for the elliptic solver. The latter code could be enhanced in order to permit repeated knots in the interior of the domain  [25] , which may be for example more compatible for other interpolations semi-Lagrangian schemes. We are also interested in better computing the characteristics when the mesh is strongly distorted; this could have an impact on the numerical results. Finally, we can also compare with Eulerian approaches which are subject to a CFL condition.
Background: Optimizing the measurement of Continuous Positive Airway Pressure (CPAP) compliance and treatment efficacy is paramount for patients with obstructive sleep apnea syndrome (OSAS). Compliance knowledge is currently based on data coming from CPAP machines; however algorithms and measured parameters vary from one machine to another. This study was conducted to clinically evaluate a novel device, NOWAPI ® , designed to assess compliance remotely in conjunction with any CPAP machine. NOWAPI ® was tested against polygraphy, the gold standard for the measurement of CPAP treatment duration and residual apnea-hypopnea index (AHI). Methods: Single group assignment, open label, non-randomized. Sleep laboratory setting. 22 adult patients with OSAS treated by CPAP were included. Recordings were performed during one night while the patient was treated with his/her usual CPAP and interface.
Data collection Duration of CPAP treatment, number of apneas and hypopneas and mean positive pressure measured by NOWAPI ® were compared to those measured by respiratory polygraphy (Embletta GOLD ® , ResMed), routinely used for the diagnosis and severity evaluation of OSAS conjointly with clinical sleep evaluation  [10]. The two different files the NOWAPI ® stores were collected: the summary data file of mean values recorded by consecutive 15-min periods and the detailed data file including the data recorded every 40 ms. The investigator estimated CPAP treatment duration and the numbers of apneas and hypopneas on the same 15-min recording periods as the NOWAPI ® during the recording night and reported them on the Case Report Form. Visual scoring of apneas and hypopneas from respiratory polygraphic data was performed according to AASM 2007 guidelines  [11].
Discussion Our study shows that, when correctly connected, NOWAPI ® can reliably measure CPAP treatment duration versus respiratory polygraphy, independently of the mask or the CPAP applied, either conventional or auto-CPAPs. Following the bench tests, the study aimed to evaluate the clinical feasibility and accuracy of this new device in a small group of patients treated by different CPAPs and interfaces. We used respiratory polygraphy as a reference to assess NOWAPI ® reliability as recommended in consensus guidelines for the diagnosis and monitoring of OSAS in adults  [10]. Respiratory polygraphy is indeed regularly used under the supervision of qualified sleep technicians for the follow-up of patients with OSAS who have been diagnosed and treated for several months. Treatment duration is a key parameter for the long-term assessment of patients with OSAS treated at home by CPAP, because duration is strongly associated with effectiveness of CPAP therapy.
It is of important note that even with a misconnected NOWAPI ® , the positive pressure was properly administered because the NOWAPI ® measures flow not using a pneumotachograph but with a customized design that minimizes pressure drops (Fig. 2 ). Residual (i. e. under CPAP treatment) AHI is also an important parameter to monitor in patients with OSAS because it is an indicator of quality of the treatment: an AHI lower than 10 should be achieved with appropriate CPAP settings and interface. The bench test  [9]  indicated an AHI difference of 0. 9 ± 1. 6 events per hour between NOWAPI ® and the bench actual values. The clinical study showed that agreement between NOWAPI ® and respiratory polygraphy was within two apneas or less in 94 % of all 15-min records (Fig. 5b ) and within two hypopneas or less in 90 % of all 15-min records (Fig.
However, the study was exploratory and was not designed to demonstrate statistical equivalence with ventilatory polygraphy. Sample size was not estimated and 20 evaluable patients were anticipated enough for this pilot study of first use in OSAS patients. Indeed, even with the limited number of patients, the study allowed to identify that misconnection was possible despite the two different NOWAPI ® ends, a useful information before a wider use. Second, the study allowed testing the novel device with six frequently used CPAP machines, the three available interfaces and with/without humidifier. Third, the study showed that NOWAPI ® tended to underestimate AHI, and this was not identified by the bench tests. Underestimation might not be the best option if the main aim of remote monitoring is to detect patients with high residual events in which CPAP treatment needs adjustment.
Conclusion The aim of the present study was to clinically evaluate a novel add-on device designed to monitor continuously patient compliance and CPAP treatment efficacy in OSAS patients. The device accuracy was tested against respiratory polygraphy. The results of this study suggest that, when correctly connected, NOWAPI ® allows a precise measurement of CPAP treatment duration, the main parameter to be monitored on the longterm to ensure a proper patient follow-up and implementation of adequate patient support in case compliance is decreasing. Although a limited number of patients was included, the study has shown three relevant concerns that were not identified in the bench test and should be corrected in the perspective of a larger use at patient homes: • First, reverse connection of the device on the patient circuit was possible despite different ends.
OSAS is a common condition associated with potentially severe outcomes, to which a chronic disease management model should be applied. NOWAPI ® could be a useful tool to monitor in the early and long term treatment compliance and quality. Because patients are treated with many types of CPAPs and interfaces, and because each CPAP machine has its own event detection algorithm and report  [12] , adding-on a monitoring device such as NOWAPI ® would allow provision of standardized measures and reports and it should be i. e. easier to compare a patient to himself when he had tried different CPAP machines.
During embryonic stages 8-10, the mesoderm is thereby progressively specified into four different tissue primordia, each of which is characterised by the expression of specific lineage transcription factors (Fig  2 )  [27, 32]. Collating all phenotypic data from the literature into a mathematical model allows to formally assess the coherence between the current view of the network with individual published results on single or multiple mutant phenotypes. More specifically, we aim to further characterise the crucial regulatory components and interactions driving mesoderm specification. As we mostly rely on published qualitative molecular and genetic data, we use a flexible logical modelling framework and the software GINsim (cf. Material and methods), which enables the use of multilevel variables whenever justified, along with fully asynchronous updating.
Encoded using the software GINsim (Computational and experimental procedures), the resulting regulatory graph (Fig  3 ) is provided in a computer readable format, along with extensive annotations (text and links to relevant literature and database entries, see S1 File). This regulatory graph encompasses 48 nodes (including 12 input components, representing mainly ectodermal signals) and 82 regulatory interactions. In many cases, the definition of the logical rule associated with each node is straightforward (e. g. when a node is the target of a unique regulator). However, for more complex regulatory relationships, i. e. when multiple interactions converge on the same component, we examine the following scenarios: (i) Is the presence of an inhibitor sufficient to completely or partially block gene expression? (ii) Which activator(s) is (are) sufficient to drive the expression of the target gene? (iii) Can the activators do so in the presence of repressor(s)? After several iterations, we obtained a set of logical rules consistent with all available knowledge on the regulation of each gene in the network, which further enabled the recapitulation of all published phenotypes (see below), demonstrating the robustness of the model.
For each territory, the target values at stage 10 were evaluated based on published data (S1 Fig, right). For example, in the region that will form VM, the initial state (stage 8) is characterised by the presence of Twi, which activates Tin  [40]  and Mef2  [41]  expression. Bap is then activated by Tin at stage 9  [37] , which is followed by the activation of Bin by Bap  [42, 43]  in late stage 9 embryos. Finally (stage 10), Bap is activated at its maximum level (value 3) by Cubitus Interruptus (Ci) and Engrailed (En)  [27]. To recapitulate the formation of each mesodermal tissue derivative in the wild-type situation, we thus ran four different simulations using an asynchronous updating policy (Material and methods).
Although Bap is crucial for VM development, it is also expressed at later stages in H. At this point, we assume that H, SM and FB develop normally in Bap lof mutant, as no other experimental defect has been reported. Finally, Boolean models of embryonic processes generally rely on qualitative expression data from in-situ hybridisation. Our discrete model (as the sea urchin model  [49] ) is therefore limited to qualitative results, such as the presence or absence of a given tissue in a given presumptive territory. Although we cannot reproduce quantitative data, such as an increase or a decrease of specific cell numbers, we can still recapitulate the presence of different cell types. Our logical model could further serve as a scaffold to build more quantitative models when more quantitative and systematic experimental datasets will become available.
Logical modelling software GINsim The software GINsim (for Gene Interaction Network simulation) implements the logical formalism  [52]. It allows the edition, analysis and simulation of regulatory graphs. Freely available (http://ginsim. org), GINsim supports the annotation of components and interactions with free text and URLs. Once a model is defined, the user can select a simulation mode and define a set of initial states. GINsim can then be used to compute state transition graphs and report the stable states. GINsim also enables the definition and the simulation of different types of mutants (loss-of-function, ectopic gene expression, and combinations thereof) by blocking the levels of expression of the corresponding variables in defined intervals. To further ease the analysis of multiple perturbations, we have written a set of scripts in python, which iteratively compute the behaviour of our mesoderm specification model for each region and mutant considered, process the results and generate a synthetic web page (cf.
Model reduction To enable the dynamical analysis of comprehensive regulatory graphs, we take advantage of a novel reduction method implemented in GINsim. This functionality allows the user to select components of a regulatory graph to be made implicit. The software verifies that the proposed reduction does not fundamentally change the network topology (elimination of regulatory circuits) and update the logical rules for the components targeted by reduced nodes. The original and reduced networks have the same stable states (in terms of levels of common variables), while differences may appear as to their reachability  [53].
(PDF) S1 Table. This supporting table (in pdf format) lists the logical rules associated with each node of the model (but the input nodes, for which the initial values are maintained in the absence of external perturbation). (PDF) S1 File. Model. This supporting file contains the full Drosophila mesoderm specification model, which can be opened and simulated using the software GINsim (GINsim is freely available from http://ginsim. org). (ZGINML) S2 File. Web Archive. The content of this supporting web archive folder documents known and novel gene expression pattern simulated with the drosophila mesoderm specification. Open the file "index. html" with a web browser to access this information. (ZIP).
Med-Slp) have defects in heart development (marked by tin expression, red), with the double mutant being more severe. The visceral muscle (VM, marked by bin) develops normally. (TIF) S1 Text. This supporting text (in pdf format) provide textual annotations plus hyperlinks to entries to external database for each node of the model. It also includes information about ChIP data supporting transcriptional regulations of various nodes by master mesoderm transcriptional factors. (PDF).
Acknowledgments We thank  Aurélien Naldi  and  Duncan Bérenguier  for providing Python scripts enabling the automation of GINsim simulations and the interpretation of phenotypes. We further thank  Maria Leptin  and  Manfred Frasch  for kindly providing several Drosophila lines and DNA probes using in this study. Finally, we warmly thank  Christine Brun ,  Elodie Darbo ,  Krzystof Jagla  and  Laurence Röder  for insightful discussions and suggestions on previous versions of this manuscript.
( Med-Slp) have defects in heart development (marked by tin expression, red), with the double mutant being more severe. The visceral muscle (VM, marked by bin) develops normally. (TIF) S1 Text. This supporting text (in pdf format) provide textual annotations plus hyperlinks to entries to external database for each node of the model. It also includes information about ChIP data supporting transcriptional regulations of various nodes by master mesoderm transcriptional factors. (PDF).
Fortunately, with our recent  20-hour, 1. 2-mm ALMA program (2013. 1. 00718. S: Paper I and II in the ALMA Spectroscopic Survey in the HUDF [ASPECS] series  [Walter et al. 2016 and Aravena et al. 2016a ]), we were able to acquire very deep continuum observations over a 1 arcmin 2 region of the sky with high-quality multiwavelength observations. That region was the Hubble Ultra Deep Field (HUDF:  Beckwith et al. 2006; Illingworth et al. 2013; Xue et al. 2011) , containing the most sensitive ultraviolet, optical, near-infrared, X-ray, and radio observations available anywhere on the sky  (Bouwens et al. 2011; Ellis et al. 2013; Illingworth et al. 2013; Teplitz et al. 2013; Xue et al. 2011). Our new observations (12. 7µJy/beam) were sufficiently deep to probe to an almost-constant dust-enshrouded SFR of 4 M ⊙ /yr at 2σ from z ∼ 2 to z ∼ 10 over the field [for T d ∼ 35 K], allowing us to obtain a census of such star formation over a substantial volume (∼2×10 4 Mpc 3 ) in the early universe.
For HST optical ACS/WFC and near-infrared WFC3/IR observations, we make use of the XDF reductions  (Illingworth et al. 2013) , which incorporated all ACS+WFC3/IR data available over the HUDF in 2013. The XDF reductions are ∼0. 1-0. 2 mag deeper than original  Beckwith et al. (2006)  reductions at optical wavelengths and also provide coverage in the F814W band. The WFC3/IR reductions made available as part of the XDF release include all data from the original HUDF09  (Bouwens et al. 2011) , CANDELS  (Grogin et al. 2011; Koekemoer et al. 2011) , and the HUDF12  (Ellis et al. 2013)  programs. In the process of assembling our z = 1. 5-3. 5 samples, we derived our initial source catalogs and performed photometry on sources using our own modified version of the SExtractor  (Bertin & Arnouts 1996)  software.
(2015)  samples were based on the deep optical ACS and WFC3/IR observations within the HUDF. z = 4-8 samples were constructed by applying Lyman-break-like color criteria to the XDF reduction  (Illingworth et al. 2013)  of the Hubble Ultra Deep Field. The  Bouwens et al. (2016, in prep)  z = 9-10 samples were constructed using a Y 105 or J 125 -dropout Lymanbreak color criteria to the available HST data and then splitting the selected sources into z ∼ 9 and z ∼ 10 subsamples. Two sources from that z = 9-10 sample lie within the ASPECS region (see also  Bouwens et al. 2011; Ellis et al. 2013; Oesch et al. 2013). To maximize the total number of star-forming galaxies at z ∼ 2-10 considered in this study, we also applied the EAZY photometric redshift code to the HST WFC3/UVIS, ACS, and WFC3/IR photometric catalogs we had available over our deep ALMA field and included all sources with a best-fit redshift solution between z ∼ 1.
To guard against noise in the modeling process scattering lowermass galaxies into higher-mass bins, we also model the photometry of galaxies forcing the dust extinction to be zero in fitting the observed SEDs with FAST. For sources where the stellar-mass estimates exceed the dustfree stellar-mass estimates by more than 0. 9 dex and the photometric evidence for a particularly dusty SED was weak (applicable to only 6 sources from our total sample of 330 sources), we made use of the dust-free stellar mass estimates instead. We also estimated stellar masses for our sources by using the MAGPHYS software  (da Cunha et al. 2008)  to model the photometry for the 330 z = 2-10 sources that make up our samples. For sources with redshifts z < 3. 8, the stellar masses we estimated were in excellent agreement with our fiducial results, with the median and mean stellar mass derived by MAGPHYS being 0.
02 dex and 0. 04 dex lower, respectively. This points toward no major systematic biases in the results from the present study -which rely on FAST-estimated masses -and the other papers in the ASPECS series -where the reliance is on MAGPHYS-estimated masses. The middle panel of Figure  2  illustrates the effective range in stellar mass probed by our z = 2-10 sample. Most sources from our HUDF z = 2-10 sample have stellar masses in the range 10 7. 5 M ⊙ to 10 9. 5 M ⊙. The most massive sources probed by our program extend from 10 10 to 10 11. 2 M ⊙. Beyond the stellar mass itself, Figure 2 also illustrates the range in U V -continuum slope β probed by our samples (see §3. 1 for details on how β is derived). Since the measured β has been demonstrated to be quite effective in estimating the infrared excess for lower-redshift U V -selected samples (e.
e. , z > 6. 5). Given the small number of detected sources in our deep-continuum probe and the large uncertainties on the IR luminosities of especially faint sources, the ASPECS program represents but a first step. With future ob-servations, we can probe dust emission from galaxies at even lower masses in the high-redshift universe, while also probing much larger areas to improve the overall statistics. fluxes exhibit a broad correlation with the ALMA 1. 2 mm fluxes for z ∼ 1. 5-3. 0 galaxies, and those sources which are detected at > 2σ in the ALMA observations also tend to show significant >2σ detections in the MIPS observations (see Table  10 ). It is clear from this relationship provides an approximate match to the results from each of the three studies (if some allowance is made for modest systematics from one study to another).
We thank an especially knowledgeable referee for their feedback, which greatly improved our paper. RJB acknowledges enlightening discussions with  Corentin Schreiber  while writing this paper. Seiji Fujimoto  provided us with some feedback on the public version of our paper which greatly improved its overall clarity. FW, IRS, and RJI acknowledge support through  ERC grants COSMICDAWN , DUSTYGAL, and  COS-MICISM , respectively. MA acknowledges partial support from  FONDECYT  through grant  1140099. IRS also acknowledges support from  STFC  ( ST/L00075X/1 ) and a  Royal Society / Wolfson Merit  award. Support for RD and BM was provided by the  DFG priority program  1573 The physics of the interstellar medium. AK and FB acknowledge support by the  Collaborative Research Council 956 , sub-project  A1 , funded by the  Deutsche Forschungsgemeinschaft (DFG).
FEB, LI, and JG-L acknowledge support from  CONICYT-Chile grants Basal-CATA   PFB-06/2007. FEB and JG-L acknowledges support from  FONDECYT Regular 1141218  (FEB,JG-L). FEB also acknowledges support from " EMBIGGEN" Anillo ACT1101 (FEB)  and the  Ministry of Economy, Development, and Tourism's Millennium Science Initiative  through grant  IC120009 , awarded to  The Millennium Institute of Astrophysics, MAS. LI acknowledges Conicyt grants Anilo  ACT1417. DR acknowledges support from the  National Science Foundation  under grant number  AST-1614213  to  Cornell University. This paper makes use of the ALMA data from the program  2013. 1. 00718. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  NSC  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile.
The open source environment R is one of the most widely used software for statistical computing. It provides a variety of applications including statistical genetics. Most of the powerful tools for population-based genetic analyses are stand-alone free programs developed by researchers in academics. SOLAR is the de facto standard software program to perform linkage and association mappings of the quantitative trait loci (QTLs) in pedigrees of arbitrary size and complexity. solarius allows the user to exploit the variance component methods implemented in SOLAR. It automates such routine operations as formatting pedigree and phenotype data. It also parses the model output and contains summary and plot functions for exploration of the results. In addition, solarius enables parallel computing of the linkage and association analyses, that makes the calculation of genome-wide scans more efficient.
The motivation to develop the solarius software came from the extensive experience of the group that studies the Genetic Analysis of Idiopathic Thrombophilia (GAIT) project  (Blangero et al. , 2003; Vila et al. , 2010). Both GAIT1 and GAIT2 projects include individuals from a Spanish population in extended pedigrees, an extensive list of clinical phenotypes related to the thrombotic disease and large arrays of both single nucleotide polymorphism (SNP) and microsatellite genetic markers. The first goal of the design was to provide an effortless data manipulation in a polygenic analysis needed to be explored for such large number of phenotypes. The second goal was to conduct the genome-wide scans for both linkage and association mappings in an efficient way by means of parallel computing.