dataesr/scientific-paragraphs-categorization huggingface dataset:
We used the values from Table  S1  in  [40]  for the sensing matrix elements. To estimate receptor noise, we used the standard deviation measured for the background firing rates for each receptor (data obtained from the authors). The fly data has the advantage of being more complete than equivalent datasets in mammals. Mammalian sensing matrix When comparing our model to experimental findings from  [22] , we used a sensing matrix based on mouse and human receptor affinity data from  [41]. This was measured using heterologous expression of olfactory genes, and tested in total 219 mouse and 245 human receptor types against 93 different odorants. However, only 49 mouse receptors and 10 human receptors exhibited detectable responses against any of the odorants, while only 63 odorants activated any receptors.
The overall scale of the sensing matrices was set by multiplying all the affinities by 100, which yielded values comparable to the measured firing rates in fly olfactory neurons  [40]. For the robustness results below we also generated random matrices in additional ways: (1) 'gaussian': drawing the affinities from a Gaussian distribution (with zero mean and standard deviation 2), (2) 'bernoulli': drawing from a Bernoulli distribution (with elements equal to 5 with probability 30%, and 0 with probability 70%), (3) 'signed': drawing from a Bernoulli distribution followed by choosing the sign (so that elements are 5 with probability 15%, -5 with probability 15%, and 0 with probability 70%); and (4, 5) 'fly scrambled' and 'mammal scrambled': scrambling the elements in the fly and mammalian datasets (across both odorants and receptors).
For details, see  [68]. The specific procedure used in  [68]  draws the partial correlation values from beta distributions with parameters depending on the number of elements in the conditioning set L. This is done in order to ensure a uniform sampling of correlation matrices. This, however, is not ideal for our purposes because these samples again tend to cluster close to the identity matrix. A simple modification of the algorithm that provides a tunable amount of correlations is to keep the order of the beta distribution fixed α = β = const (see Stack Exchange, at https://stats. stackexchange. com/q/125020). When the parameter β is large we obtain environments with little correlation structure, while small β values lead to stronger correlations between odorant concentrations. The functions implementing the generation of random environments are available on our GitHub (RRID:SCR_002630) repository at https://github.
Figure7: Robustness of optimal receptor distribution to subsampling of odorants and receptor types. Robustness in the prediction is measured as the Pearson correlation between the predicted OSN numbers with complete information, and after subsampling. (a) Robustness of OSN abundances as a function of the fraction of receptors removed from the sensing matrix. Given a full sensing matrix (in this case a 24 × 110 matrix based on Drosophila data [40] ), the abundances of a subset of OSN types were calculated in two ways. First, the optimization problem from eq. (7) was solved including all the OSN types and an environment with a random covariance matrix (see Figure5). Then a second optimization problem was run in which a fraction of the OSN types were removed. The optimal neuron counts K i obtained using the second method were then compared (using the Pearson correlation coefficient) against the corresponding numbers K i from the full optimization.
Acknowledgments We would like to thank  Joel Mainland  and  David Zwicker  for helpful discussions, and  Elissa Hallem ,  Joel Mainland , and  Darren Logan  for olfactory receptor affinity data. During the completion of this project, VB was supported by  Simons Foundation Mathematical Modeling in Living Systems  grant  400425 ,  Aspen Center for Physics NSF  grant  PHY-160761 , and  US-Israel Binational Science Foundation  grant  2011058. TT was.
Verification of Laser Energy The input energy of pulsed laser, equalling to power/repetition, is very important to SEEs testing. Unfortunately, it's hard to directly measure the power under the objective lens continuously during our testing. To solve this problem, a power meter (P1, FieldMaxII, Coherent. ) behind Pockels Cell is set to continuously record the value of power that is going to enter the Microscope. At the same time, another power meter (P2, PM100USB, ThorLab Inc. , from Newton, NJ 07860 USA) is placed under the objective lens to record the value of power that will be injected into the DUT. Apparently, there existing a solid linear relationship between P1 and P2, consequently we can get the value of power under the objective lens (P2) by reading P1 in real time. To verify the relationship between P1 and P2, we will record a look-up table with corresponding data from P1 and P2.
91 µm. In testing, at the beginning, the laser beam is focused at the best position with the threshold energy. After that, we moved the beam upwards with the step size of 1 µm. At each position, the threshold energy would be recorded to monitor the change of energy. When this work was finished, we moved the beam downwards from the best focused position and repeated the same procedure. The data is shown in Table  4. From the results, the active zone along z axis is from -3 µm to +2 µm, which is quite close to the calculated value of ±1. 91 µm. From the data, if we convert the relationship between energy and generated errors, we can get a laser beam profile along z axis, which is shown in Figure  6.
Results and Discussion (L)] (1 opt ), [Dy(tta) 3 (L +• )] (1 +• opt ) and [Dy(tta) 3 (L 2+ )] (1 2+ opt ) The X-ray structure of 1 was refined and described in a previous work  [60]. Briefly, the experimental molecular structure was used as starting point for the DFT optimized structures of [Dy(tta  S2 ). 1 consists in a mononuclear complex of Dy III (Figure  1  and Figure  S3 ). The Dy III ion lies in an N 2 O 6 coordination sphere with a square antiprism symmetry (SAPR-8 at 0. 538 using SHAPE program  [63] ). The six oxygen atoms and two nitrogen atoms belong to three hfac -anions and the L ligand, respectively. The average Dy-N and Dy-O distances were 2. 530 Å and 2. 333 Å, respectively. The central C=C bond length (1. 347 Å) and the non-planar conformation of the TTF core attest the neutrality of L.
X-ray Structures of [Dy(tta)3(L)]⋅C6H14 (1)⋅C6H14, and DFT Optimized Structures of [Dy(tta)3(L)] (1opt), [Dy(tta)3(L +• )] (1 +• opt) and [Dy(tta)3(L 2+ )] (1 2+ opt) The X-ray structure of 1 was refined and described in a previous work  [60]. Briefly, the experimental molecular structure was used as starting point for the DFT optimized structures of [Dy(tta)3(L)] (1opt), [Dy(tta)3(L +• )] (1 +• opt) and [Dy(tta)3(L 2+ )] (1 2+ opt) (Figure  S2 ). 1 consists in a mononuclear complex of Dy III (Figures  1  and S3 ). The Dy III ion lies in an N2O6 coordination sphere with a square antiprism symmetry (SAPR-8 at 0. 538 using SHAPE program  [63] ). The six oxygen atoms and two nitrogen atoms belong to three hfac -anions and the L ligand, respectively. The average Dy-N and Dy-O distances were 2.
Computational Details DFT geometry optimizations and TD-DFT excitation energy calculations of the Y III analogs of the corresponding complexes 1 opt , 1 +• opt , and 1 2+ opt were carried out with the Gaussian 09 (revision A. 02) package  [74]  employing the PBE0 hybrid functional  [75, 76]. The "Stuttgart/Dresden" basis sets and effective core potentials were used to describe the yttrium atom  [77] , whereas all other atoms were described with the SVP basis sets  [78]. The first 50 monoelectronic excitations were calculated. In all steps, a modeling of bulk solvent effects (solvent = dichloromethane) was included through the Polarisable Continuum Model (PCM) [79], using a linear-response non-equilibrium approach for the TD-DFT step  [80, 81]. Molecular orbitals were sketched using the Gabedit graphical interface  [82].
Wavefunction-based calculations were carried out on the experimental molecular structure of 1 and on the DFT optimized structures 1 opt , 1 +• opt , and 1 2+ opt by using the SA-CASSCF/RASSI-SO approach, as implemented in the MOLCAS quantum chemistry package (versions 8. 0)  [83]. In this approach, the relativistic effects are treated in two steps based on the Douglas-Kroll Hamiltonian. First, the scalar terms were included in the basis-set generation and were used to determine the spin-free wavefunctions and energies in the complete active space self-consistent field (CASSCF) method  [84]. Next, spin-orbit coupling was added within the restricted-active-space-state-interaction (RASSI-SO) method, which uses the spin-free wavefunctions as basis states  [85, 86]. The resulting wavefunctions and energies are used to compute the magnetic properties and g-tensors of the lowest states from the energy spectrum by using the pseudo-spin S = 1 2 formalism in the SINGLE-ANISO routine  [87, 88].
Acknowledgments: We thank  Région Bretagne ,  Rennes Métropole ,  CNRS ,  Université de Rennes 1 , the  European Commission  through the  ERC-CoG  725184  MULTIPROSMM  (project n. 725184) and  ERC-AdG  267746  MolNanoMas  (project no. 267746 ) and the  ANR  ( ANR-13-BS07-0022-01 ) for financial support. B. L. G. and G. F. G. thank the French  GENCI/IDRIS-CINES centre  for high-performance computing resources.
Funding: This work was supported by  Région Bretagne ,  Rennes Métropole ,  CNRS ,  Université de Rennes 1 , the  European Commission  through the  ERC-CoG  725184  MULTIPROSMM  (project n. 725184) and  ERC-AdG  267746  MolNanoMas  (project no. 267746 ) and the  ANR  ( ANR-13-BS07-0022-01 ) for financial support. B. L. G. and G. F. G. thank the French  GENCI/IDRIS-CINES centre  for high-performance computing resources.
Propagation laws in several rivers Propagation properties of several rivers were investigated. For some of the rivers, experiments were done at different hydrodynamic conditions (Table  1 ). For the discharge investigated, hydrodynamic conditions were not variable enough to observe major differences in the results. We therefore decided to gather data to propose a unique result for each river. The data set is presented in the Supplement, for all rivers and for different frequency bands. A first result concerns the estimated power of the sound source (P @1 m ) emitted during the experiments (Fig. 7 ). Compared with the measurements made in Lac du Bourget, it can be observed that the estimation of the sound source power is overestimated when using a spherical model and underestimated when using a cylindrical model of the geometric spreading loss.
Acknowledgements. The authors are grateful to all the contributors to this work; particularly abundant during field experiments was  Guy Cellier  (EDF), who designed the equipment to record sounds with a river board. This work has also been carried out with the help of engineering consultants and numerous students. Financial support. This project was essentially founded by  Eléctricité de France  with additional subsidy from  Agence de l'eau Rhone-Alpes , the  Rhone-Alpes region  ( ARC3-Environment ) and  Grenoble University (OSUG). Review statement. This paper was edited by  Jens Turowski  and reviewed by two anonymous referees.
Research activity of the centres The survey collected 122 answers related to the main areas and fields of research in the responding centres  (clin. 42, univ. 45, lab. 35). Figure  1  shows the number of responses indicating high or relatively high activity in a specific field. Research in genetic and pathophysiology is the most active field (108/122, Fig. 1a ), with a strong predominance of genetics (gene identification 95/122, Fig. 1b ) and genomics (high throughput genomics 80/122, Fig. 1b ). These activities rely on sampling and data collection (biobanking 69/122, database for omics 67/122, omics technologies 63/122, Fig. 1b ) and benefit from shared platforms (for biobanking 64/122, for genomic and proteomic 66/122, Fig. 1f  ), and to local bioinformatics facilities (60/122, Fig. 1b ) rather than shared bioinformatics platforms (36/122, Fig.
Acknowledgements. Authors thank the  Centre National d'Etudes Spatiales (CNES)  and  Jean-Jacques Berthelier  (PI of the  ICE instrument)  for the use of the data. The authors are grateful to the anonymous reviewers, whose comments helped them to improve the article. Financial support. This research has been supported by the  Austrian Academy of Sciences  and the  Russian Academy of Sciences. Review statement. This paper was edited by  Yoshizumi Miyoshi  and reviewed by two anonymous referees.
Consequently, we analyzed individual energy needs corresponding to four heat transfers. These are shown in Table  1 , with the parameters in Eqs. (  2 )-(  4 ) varied to fit the metamodel. Moreover, an external shading device was included. It provides a shading factor Fom = 0. 2 when the solar irradiance Isw is higher than 300 W m -2. The metamodel was fitted from dynamic simulations performed with TRNSYS software. To this end, the individual energy needs were varied using upper and lower levels of the physical parameters, as shown in Table  2. The Box-Behnken experimental design was used to plan the simulations Hence, 25 dynamic simulations were needed to fit the metamodel compared with 81 when using a full factorial design. In addition, the metamodel coefficients were obtained by multiple regression analysis.
Metamodel validation The metamodel fit was checked for each value of the internal heat gains pig,o by comparing the results with those of TRNSYS software. The RMSE of the fits are shown in Fig. 7  for both climates and both thermal masses. As expected, the metamodels were more accurate for the hot climate of Athens than for the cold climate of Helsinki and for the lightweight thermal mass. For Helsinki, the RMSE was relatively high when the internal heat gains were low, i. e. when the cooling energy needs were close to zero. However, when these gains were high, the RMSE was low, comparable to the RMSE obtained for Athens. In addition, the RMSE became stable, with approximately the same values both thermal masses. Finally, the RMSE can be accurately expressed by a power regression, especially for the heavy thermal mass (R 2 = 0.
To visualize the correlation between MFM experimental data and simulation data, we present additional movies. Supplementary Movie files 1-3 show simulated junction dynamics during one period of tip oscillations. Supplementary Fig. 2  shows one frame of the video with clarifying comments. There are three panels in the video and parameters. The top panel shows spatial variation of the supercurrent density J s ðxÞ=J c ¼ sinðφÞ. The vortex is seen as an up(+1)-down(-1) variation of sinðφÞ with zero in the center of the vortex. Horizontal grid spacing is 0. 5 and vertical grid spacing is 2λ j (in all panels). The middle panel shows spatial distribution of voltage VðxÞ / dφðxÞ=dt. The total vertical scale is equal to the plasma voltage from -V p to þV p ¼ Φ 0 2π ω p. The entering vortex leads to appearance of a negative voltage peak marked in the Figure (positive vortex moving in positive direction generates negative voltage because dφ=dt<0).
Acknowledgements We thank  V. Ryazanov  for fruitful discussions and advice. The MFM experiments were carried out with the support of the  Russian Science Foundation  (project No. 18-72-10118 ). The samples were elaborated owing the support of the  Ministry of Education and Science of the Russian Federation in the framework of Increase Competitiveness Program of NUST MISiS  (No. K3-2018-032 ). This work was performed using e-beam lithography of  MIPT Shared Facilities Center , with financial support from the  Ministry of Education and Science of the Russian Federation  (Grant No. RFMEFI59417X0014 ). D. R. , C. B. , and T. C. acknowledge  COST CA16218-Nanoscale Coherent Hybrid Devices for Superconducting Quantum Technologies ,  French ANR  grants SUPERSTRIPES and MISTRAL. V. M. K. and A. A.
G. acknowledge support by the  European Union   H2020-WIDE-SPREAD-05-2017 - Twinning  project " SPINTECH"  under Grant Agreement No. 810144. V. M. K. is grateful for the hospitality during a visiting professor semester at  MIPT , supported by the  Russian Ministry of Education and Science  within the program "5top100". I. A. G. acknowledges the partial support by the  Program of Competitive Growth of Kazan Federal University.
Drugs are widely prescribed for anorexia nervosa in the nutritional, somatic, and psychiatric fields. There is no systematic overview in the literature, which simultaneously covers all these types of medication. The main aims of this paper are (1) to offer clinicians an overview of the evidence-based data in the literature concerning the medication (psychotropic drugs and medication for somatic and nutritional complications) in the field of anorexia nervosa since the 1960s, (2) to draw practical conclusions for everyday practise and future research. Searches were performed on three online databases, namely MEDLINE, Epistemonikos and Web of Science. Papers published between September 2011 and January 2019 were considered. Evidence-based data were identified from meta-analyses, if there were none, from systematic reviews, and otherwise from trials (randomized or if not open-label studies).
B. , N. G. ). First, we defined a search in order to identify all reviews and meta-analyses published on the topic of drug treatments in AN with the following terms and algorithm, which, among the algorithms tested with different key words and combinations, proved to be the one that retrieved the largest corpus of papers: "anorexia nervosa and pharmacotherapy or drug treatment or medication or nutrition or enteral nutrition or weight restoration". The search on the three databases previously mentioned selected 289 papers, 45 of which were reviews or meta-analyses on the topic (see Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow-chart Figure  1 ). Two further papers were identified by manual research. Among the 45 reviews or meta-analyses, 26 were excluded for the following reasons: 16 were overviews and/or not systematic reviews with no explicit methodology for the selection of the papers mentioned  [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] , one review  [25]  predated the World Federation of Societies of Biological Psychiatry review, one  [26]  reviewed the same eight studies as a meta-analysis included  [27] , five focused on unpublished data, or studies that were reviewed in later papers included here  [28] [29] [30] [31] [32] , one mainly focused on emerging treatment research or perspectives  [33] , two concerned oxytocin but with no specific results in AN  [34, 35].
Methodological Issues The reviews and meta-analyses selected in this overview reviewed from six  [51]  to 66 studies  [45]  (see Table  3 ). This wide range results from numerous factors (aim of the review; types of study considered: ranging from only RCTs to all types of study; period; methodology for selection of studies: ranging from all published studies to a selection based on quality guidelines; type and number of databases used). For example, Brockmeyer et al. used the Cochrane Handbook and the National Institute of Heath Criteria for a quality assessment of controlled intervention studies  [51]  between October 2011 and the end of 2016 and selected only three good quality RCTs on psychotropic medication. Conversely, Franck et al. [45]  were exhaustive and included all studies (except case reports) published in the past 50 years.
et al. [40] Systematic review and Applied Health; Educational Resources Information Center; 32 RCTs All 1960 October 2011 Cyproheptadine; Recombinant human growth hormone (rhGH); Risedronate; National Agricultural OnLine; Testosterone; Nasogastric tube Embase Scopus Access; Cochrane Collaboration libraries 18 studies = (a) RCTs and (b) comparing pharmacotherapy with a placebo De Vos et al. [41] Meta-analysis PubMed; PsycINFO; Embase; Cochrane Library controlled condition and reported on (c) patients with Anorexia Nervosa and an All No limitation October 2012 Antidepressants; Antipsychotics; Hormonal therapy age minimum of 12 years. Outcome was measured in (d) terms of weight gain Rocks, T. et al. [42] Systematic review PubMed; Scopus; Web of Science 7 observational studies ≤19 years No limitation May 2012 Nutrition therapy ClinicalTrials.
gov; Clinicaltrialsregister. eu; 7 RCTs second generation antipsychotics Dold et al. [43] Meta-analysis Cochrane Central Register of Controlled Trials (CENTRAL); efficacy, acceptability, and tolerability in comparison to placebo/no treatment, All No limitation August 2014 Atypical antipsychotics Embase; PubMed/MEDLINE; even unpublished studies PsycINFO.
K. et al. [46] Systematic review PubMed; Scopus; PsycINFO; Clinical trials database 27 studies = 1 RCT, 6 prospective, 14 retrospective, 6 observational 13-38 years 1960 15 March 2015 Refeeding approaches Kells, M. et al. [47] Systematic review (integrative) PubMed; CINAHL Embase; Cochrane 18 studies = 2 RCTs, 6 retrospective, 5 cohort, 1 observational, 4 case reports 11-57 years No limitation May 2016 Tube feeding Miniati, M. et al. [48] Systematic review MEDLINE; PsycINFO 41 studies = 17 RCTs, 9 open trials, 12 case series and case reports, 2 retrospective observations, 1 single-blind RCT Adults January 1966 January 2014 Antidepressants; Antipsychotics (typical and atypical); Lithium; Clonidine; Cyproheptadine Misra, M. et al. [49] Systematic review PubMed 20 studies = 10 RCTs, 8 prospective observational studies, 1 retrospective cohort study, 1 prospective study 11-45 years 1995 2015 Weight gain and restoration, Estrogen replacement therapy, recombinant h-GH, recombinant h-IgF1, DHEA, Biphosphonates, Teriparatide DHEA, various OC (EE or EE/levonorgestrel or EE/progestin or 19 studies =10 double-blind RCTs, 2 EE/Norgestimate), various oestrogen Robinson, L.
et al. [50] Systematic review MEDLINE; PsychINFO; Embase; Cochrane Database prospective observational studies, 1 retrospective cohort study, 1 case-control study and 5 non-randomised control All No limitation 3 March 2017 replacement treatments (transdermal 17ßPE/progesterone or oral EE/progesterone), Teriparatide (TPt), trials Alendronate, rhIgF1, Menatetrenone (MED) (vitamin K2), risedronate, transdermal testosterone Brockmeyer, T. et al. [51] Systematic review PubMed; Scopus; Web of Science 6 RCTs on medication (including one unpublished study) All October 2011 (post Watson 2012) 31 December 2016 Antipsychotics; Dronabinol; Tube feeding PubMed; PsycINFO; CINAHL; 19 open, prospective RCTs, Hale, M. D. et al. [52] Systematic review Web of Science; Cochrane Library; Dissertations and Theses non-randomized controlled trials, prospective cohort studies, retrospective All No limitation September 2017 Tube feeding (ProQuest); Google Scholar chart reviews Rizzo, S.
Phasers build on the clock construct from the X10 programming language  [5]  and are implemented in Habanero Java  [4]. They can be added to any parallel programming language with a shared address space. Conceptually, phasers are synchronization entities to which tasks can be registered or unregistered. Registered tasks may act as producers, consumers, or both. Tasks can individually issue signal, wait, and next commands to a phaser they are registered to. Intuitively, a signal command is used to inform other tasks registered to the same phaser that the issuing task is done with its current phase. It increments the signal value associated to the issuing task on the given phaser. The wait command on the other hand checks whether all signal values in the phaser are greater than the number of waits issued by this task, i.