dataesr/scientific-paragraphs-categorization huggingface dataset:
In consistence with the previous parametric studies  [2, 10] , a large panel of topologies was swept by massive computing to generate a database of readily available solutions. Within this database, it is possible to identify different behavior groups (shear friendly or tensile friendly behavior). The reverse use of this database is a powerful tool for mechanical design to select the optimal structure for local targeted anisotropic properties. Moreover, the group combining both shear and tensile friendly behaviors could be considered as near-isotropic. This generic group may be used for general replacement of bulk isotropic materials, where no preferential orientation of the properties is required, while preserving the capability to tailor the stiffness  [16]. The goal of this paper is to systematically estimate and compare stiffness constants and anisotropy of a large panel of topologies, and for a large range of relative densities.
Lattice structures generation The method to generate a database of architectures with a continuous variation of two parameters was implemented from Fig. 1. Investigation scheme for the control of stiffness and anisotropy, a. a hip implant with an architecture core and associated stress field  [11] , b. homogenized materials with equivalent elastic properties, c. example of lattice structures. crystallographic rules  [2]. Using this method allows to sweep a range of architectures in a continuous way, while taking the advantage of symmetries to decrease the complexity of the network geometrical description. Main idea is to use a set of symmetry operations associated to the m3m point group that combines different space groups associated to the conventional cubic Bravais lattices (BCC (body centered cubic), FCC (face centered cubic) as well as the PC (primitive cubic)).
All of these structures were meshed using Tetgen software  [24]. The elements were set with a maximal face area of 0. 005 mm 2 and a maximal volume of 0. 01 mm 3. It resulted in a mean number of elements per section between 12 and 20. These parameters were selected after a series of tests, in order to determine the convergence of the mesh for some topologies. Tetgen file format was converted to INP file format by a home-made Python script. Relative density was computed by adding up the volume of each tetrahedron on the 3D meshes. For this study the calculations were run on a single unit-cell. It is common to perform FE computation on a single cell as some publications show that for strongly periodic microstructure, a representative elementary volume limited to one unit-cell was acceptable  [25, 26].
These conditions consist in three normal uniaxial strains ε x , ε y , ε z , and three shear strains γ xy , γ xz , γ yz , which respectively correspond to ε 11 , ε 22 , ε 33 , ε 12 , ε 13 , ε 23. It was shown in other studies that this procedure gives well accurate prediction results for the macroscopic mechanical properties for lattice structures  [16, 18] , but also for other kind of materials such as composite materials  [22, 27]. The homogenization procedure was implemented in Python language, and finite element analysis were performed in Abaqus 6. 14, in a fully automatized procedure from the generation of structures to the post-treatment of FE analysis. For this study, an isotropic bulk material was used with a Young Modulus of 1 GPa and a Poisson coefficient set at 0. 4. In a previous paper, authors have shown that the variations of stiffness with the relative density was material independent if a suitable normalization is applied  [2].
Conclusions In this study, a large database of cubic lattices structures has been generated to produce a large panel of topologies. The following relevant conclusions were drawn: • We proposed here to determine lattices connectivity based on a weighted average of the nodes network. Some specific families of topologies with different levels of connectivity were identified and correlated to the mechanical properties. • The use of a homogenization procedure allowed us to determine the different elastic constants of the homogenized material. The modeling work has revealed that the stiffness matrix always preserves the symmetry of the point group that was used to generate the structures. It was shown that the dependence of elastic constants to the relative density follows a power law. The domains corresponding to a low value of m 11 , m 12 , m 44 on color maps are directly correlated to the domains of high connectivity.
Pharmaceutically approved complexes suspected to be cocrystals There are numerous examples within the scientific literature of drugs identified as 'complexes', although their exact structural characteristics are yet to be determined. Indeed, it was only in 2016 that the crystallographic structure of chloral betaine was uncovered after 100 years of clinical use  [56]. This prompted a search of the PubChem database using the terms 'complex' AND 'molecular complex' and manual searches of the British Pharmaceutical Codex 1907 and British National Formulary 74. We thereby identified several potential pharmaceutical cocrystals through this approach (Table  2 ), but suspect there might be others. Interestingly, King's American Dispensatory 1898 described how some of these complexes can be generated.
Cocrystals of commonly prescribed drugs The burst of literature activity since 2003 has resulted in an eclectic mix of cocrystals using pharmaceutically relevant compounds. A search of the Cambridge Structural Database was conducted using ConQuest (v1. 2. 2, 2018) to determine the number of cocrystal forms of drugs that are commonly prescribed in the UK and Ireland. Figure  3  displays the results and, evidently, some compounds generate greater interest than others. Unsurprisingly, paracetamol, aspirin, and caffeine are among those with the greatest research activity, perhaps because of their ubiquity in academic labs as opposed to crystal novelties. It is clear that there is much scope for cocrystal research, and the benefits here are not just limited to trouble-some compounds, because they can improve the utility of even the most widely prescribed medicines.
Study sample and data collection The study protocol was approved by the Research Ethics Committees of Laval University Medical Center (DR-002-136). All patients signed a written, informed consent prior to surgery. Breast adipose tissue was obtained from women undergoing partial or total mastectomy for treatment of breast cancer (n = 17) or reduction mammoplasty (n = 6). Fresh tissue specimens were acquired from residual resected breast tissues that were not required for clinical diagnosis at least 1 cm away from the tumor margins. Information on clinicopathologic and anthropometric factors was collected from in-person interviews, phone call interviews, and/or medical records.
Oligoprimer pairs were designed by GeneTool 2. 0 software (Biotools Inc. , Edmonton, Canada) and their specificity was verified by blast in the GenBank database. The synthesis was performed by IDT (Integrated DNA Technology, Coralville, IA) (Table  1 ). Normalization was performed using the following reference genes: ATP synthase O subunit (ATP5O), hypoxanthine guanine phosphoribosyl transferase 1 (HPRT1), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH). Quantitative real-time PCR measurements were performed by the CHU de Québec Research Center (CHU) Gene Expression Platform, Quebec, Canada, and were compliant with the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines.
Limitations of our study are the relatively low number of participants which is counterbalanced by the wide range of BMI and prognostic marker values of our cohort. Of note, our total sample number is in the same order of magnitude as previous literature on breast adipose tissue steroid measurements  (41) (42) (43) (44) , representing the difficulty in obtaining those samples for research purposes. Moreover, we have included in our analysis, control samples, namely samples from healthy women. Data on steroid concentration in breast adipose tissue from healthy control and women with cancer are scarce in the literature. Most publications investigated this relationship in postmenopausal women  (41, 42, 44)  with the exception of Hennig et al. (45) , who did include premenopausal women (n = 6 out of 51) in their analyses.
Acknowledgments We would like to acknowledge the collaboration of the study participants. The authors thank  Kaoutar Ennour-Idrissi  and  Lucie Tellier  for data collection from the medical records. We are also grateful to  Scott Denham  from the  Mass Spectrometry Core, Edinburgh Clinical Research Facility , and to  Serge Simard, MSc , for technical and scientific expertise. Financial Support: This work was supported by an intercenter grant from the  Cardiometabolic Health, Diabetes and Obesity Research Network (CMDO)  to A. T. , C. D. , and F. D. S. L. is the recipient of PhD scholarships from  Fonds de recherche du Québec-santé (FRQS)  and the  Canadian Institutes of Health Research (CIHR). S. L. 's internship in R. A. 's lab was made possible thanks to the support of the  Institute of Nutrition and Functional Foods  and the scholarship from the  International internship program  of the  Fonds de recherche Nature et Technologies du Québec (FRQNT ) as well as a Michael Smith Foreign Study Supplement travel scholarship.
Introduction: The present study aimed to indirectly compare apalutamide and enzalutamide with respect to tolerability and healthrelated quality of life (HRQoL) among men with non-metastatic castration-resistant prostate cancer (nmCRPC). Methods: Patient-level data from the SPARTAN study [apalutamide ? androgen deprivation therapy (ADT) versus placebo ? ADT] and aggregate published data from the PROSPER study (enzalutamide ? ADT versus placebo ? ADT) were used. Anchored matching-adjusted indirect comparison (MAIC) was conducted by weighting patients' baseline characteristics from SPARTAN to match aggregated baseline characteristics in PROSPER. Odds ratios (ORs) of reported adverse events (AEs) and baseline-tofollow-up least squares mean differences in HRQoL [measured with Functional Assessment of Cancer Therapy-Prostate (FACT-P) score] with 95% credible intervals were re-estimated for SPARTAN arms using weighted population and indirectly compared with those in PROSPER through a Bayesian framework.
In SPARTAN, MFS was significantly improved with apalutamide ? ADT versus placebo ? ADT (hereafter referred to as the apalutamide and ADT arms, respectively) in patients with nmCRPC  [2]. Similarly, enzalutamide ? ADT significantly improved MFS relative to placebo ? ADT (hereafter referred to as the enzalutamide and ADT arms, respectively) in PROSPER  [1]. The efficacy and tolerability of apalutamide and enzalutamide have not been directly compared in a randomized controlled study. Two previous studies concluded that both drugs probably have similar efficacy and tolerability profiles by indirectly comparing the results of the SPARTAN and PROSPER studies using a method called the Bucher technique  [12, 13]. This method relies solely on comparisons of aggregate data between studies without considering differences in baseline characteristics  [14].
Data Source Individual patient data from SPARTAN  [2, 23]  and aggregate data from PROSPER  [1, 25]  were used. The SPARTAN intention-to-treat (ITT) population included 1207 patients (apalutamide: 806; ADT: 401)  [2]. The PROSPER ITT population included 1401 patients (enzalutamide: 933; ADT: 468)  [1]. AE and HRQoL analyses were based on the ITT populations from the two studies and included patients with available baseline and follow-up measurements. Review boards at participating institutions approved the SPARTAN and PROSPER studies, and they were conducted in accordance with the current International Conference on Harmonisation guidelines for Good Clinical Practice and the principles of the Declaration of Helsinki.
Endpoints The following AEs, reported in both studies, and of special interest as they are included within regulatory summaries, were compared: any AEs, serious AEs (SAEs), fatigue, hot flush, nausea, diarrhea, hypertension, fall, dizziness, decreased appetite, arthralgia, asthenia, and headache. SAEs were events that resulted in death, were life-threatening, resulted in or prolonged hospitalization, resulted in inability to conduct normal life functions, or led to a congenital anomaly or birth defect. HRQoL was compared using Functional Assessment of Cancer Therapy-Prostate (FACT-P) data available in both studies. FACT-P is a disease-specific and validated instrument to measure HRQoL in patients with prostate cancer  [26]. This validated questionnaire includes the original subscales of the 27-item FACTgeneral (FACT-G)  [27]  supplemented by a 12-item prostate cancer-specific subscale (PCS) that aims to assess potential issues with sexuality, bowel/bladder function, and pain  [26, 28].
Patients from SPARTAN missing any of the matched characteristics were excluded from the sample. Weighted odds ratios (ORs) were estimated to compare AEs for apalutamide versus ADT based on MAIC-weighted data from SPARTAN. Weighted LS mean differences with 95% confidence intervals (CIs) were estimated to compare baseline-to-follow-up change in FACT-P scores (total and subscales) between apalutamide and ADT using MAIC-weighted data from SPARTAN. This process was performed at a study level, thereby preserving the randomization of patients in the original studies. Step 2: Bayesian Network Meta-analysis Bayesian network meta-analysis with non-informative prior distributions was used to indirectly compare both treatments with respect to AEs and HRQoL  [16, 32]. The ORs (for AEs) and LS mean differences (for HRQoL) of the reweighted SPARTAN population from step 1 were compared with those reported in PROSPER to estimate ORs and LS mean differences for apalutamide versus enzalutamide, with ADT as the common comparator for both studies.
ACKNOWLEDGEMENTS Funding. This study was funded by Janssen Scientific Affairs, LLC. All authors had full access to all of the data in this study and take complete responsibility for the integrity of the data and accuracy of the data analysis. The journal's Rapid Service Fee and Open Access publication were purchased using funding provided by the study sponsor. Medical Writing Assistance. Medical writing assistance was provided by Samuel Rochette and Sara Kaffashian, who are employees of Analysis Group, Inc. , which provided paid consulting services to Janssen Scientific Affairs for the conduct of the present study. Authorship. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.
During the event, the MWR observations at both altitudes show a decrease in ozone followed by a local peak of ozone amount of the order of hours. This local recovery is observed thanks to the high time resolution of the MWR mentioned. The advected potential vorticity (APV) calculated from the MI-MOSA high-resolution advection model (Modélisation Isentrope du transport Méso-échelle de l'Ozone Stratosphérique par Advection) was also analysed at two isentropic levels (levels of constant potential temperature) of 675 and 950 K (∼ 27 and ∼ 37 km of altitude, respectively) to understand and explain the dynamics at both altitudes and correlate the ozone rapid recovery with the passage of a tongue with low PV values over Río Gallegos. In addition, the MWR dataset was compared for the first time with measurements obtained from the Microwave Limb Sounder (MLS) at individual altitude levels  (27, 37 and 65 km)  and with the differential absorption lidar (DIAL) installed in the OAPA to analyse the correspondence between the MWR and independent instruments.
, 1997a). A similar reduction was observed from a differential absorption lidar at Río Gallegos  (Wolfram et al. , 2006). Together with the banning in the use of ODS set by the 1987 Montreal Protocol, the general expectation was that the TOC would recover as the amount of ODS decreased in all regions. Recent studies showed a recovery of the stratospheric ozone column during September (statistically significant) and October (statistically insignificant) for the southern polar region  (Solomon et al. , 2016; Weber et al. , 2018; Pazmiño et al. , 2018). Ground-and space-based observations and models have shown an increase in the total ozone since 2000. Nevertheless, this increase is not significant for the period  2000 -2013 (WMO, 2014)). Ball et al. (2018)  extended this period from 1998 to 2016 and concluded that there are non-significant changes in the total amount of ozone from merged ozone datasets.
Finally, the APV field from the MI-MOSA model was used to analyse the air-mass transport during the event. In addition, the MWR ozone mixing ratio retrieved in Río Gallegos is compared for the first time with ground-based measurements from the ozone DIAL/NDACC instrument and satellite measurements from the MLS onboard the AURA/NASA. This paper is organized as follows: Sect. 2 describes the ground-and satellite-based ozone instrument and the MI-MOSA model used to calculate APV to determine the origin of air masses over Río Gallegos. In addition, this section describes the instrumental datasets used in this research and the methodology to analyse the correspondence of the MWR with respect to the ground-based DIAL instrument and the MLS ozone profile at the analysed altitudes. The results of the comparisons are detailed in Sect.
(1) The method adopted here for the ozone profile retrieval is the optimal estimation method (OEM) described by  Rodgers (2000). The forward model comparable to the measurement is calculated by means of the Atmospheric Radiative Transfer Simulator (ARTS). Detailed documentation can be found in http://www. radiativetransfer. org/docs/ (last access: August 2013). The Qpack2  (Eriksson, 2005)  is a package of Matlab routines used to set up the ARTS model, and it has included the OEM calculation for general cases. The input pressure and temperature for the forward model were obtained by combining the NCEP reanalysis data up to ∼ 30 km with the CIRA climatology above, interpolated for the MWR measurement time and site location. As an input a priori ozone profile, we used monthly zonal daytime and night-time MLS O 3 climatology between ∼ 15 and 75 km, completed with zonal climatology below and above  (McPeters et al.
AURA satellites: MLS and OMI The MLS (Microwave Limb Sounder) was launched on 15 July 2004 onboard the AURA satellite and began to operate on 13 August 2004. Since then, this instrument has been able to observe the thermal emission of the atmosphere in the range of submillimetre and millimetre waves. The Earth's limb viewing allows the MLS to achieve a higher altitude resolution (∼ 3 km in the stratosphere and ∼ 5 km in the mesosphere) compared to MWR. A full description of the MLS instrument can be found in  Waters et al. (2006). MLS ozone profile data versions 3. 3 and 3. 4  (Livesey et al. , 2013)  were used (http://mls. jpl. nasa. gov, last access: January 2016). The OMI, onboard the AURA satellite, started TOC measurements in 2004, with the aim of continuing the TOMS satellite record.
It was launched in July 2004 in the frame-work of the Earth Observing System (EOS) project. In addition to ozone, the OMI retrieves atmospheric components such as total contents of NO 2 , SO 2 , and aerosols. This instrument measures the reflected and backscattered solar radiation by a UV-VIS spectrometer with a spectral resolution ranging from ∼ 0. 45 to ∼ 0. 63 nm in nadir view and provides nearly global coverage in 1 day with a spatial resolution ranging from 13 to 24 km  (Levelt et al. , 2006). In this work, we used the TOC overpass product from the OMI (OMDOAO3). The dataset can be downloaded from https://avdc. gsfc. nasa. gov/ index. php?site=2045907950 (last access: July 2019).
Numbers below crosses indicate the number of each group of MLS measurements in each location. The time differences between MWR and MLS measurement inter-comparison pairs were less than 30 min. Given that MLS measurements are not collocated with the MWR, some differences between instruments could be due to the distance between instruments, mainly during spring when the AOH may influence and produce large difference of ozone mixing ratios in short distances. While the MWR and the MLS operate automatically, DIAL requires manual operation in clear-sky nights. DIAL monitoring in 2014 and 2015 was intensive during spring (October, November, December) and it was possible to obtain a few measurements if we compare with the high quantity of measurements provided by the MWR and the MLS. The DIAL dataset is not shown in Fig.
2 , but in Fig. 6  in the next section. For the MWR-DIAL inter-comparison pairs we take the MWR measurements in the middle of the ∼ 3 h integrated interval of the DIAL measurement. The ozone measurements obtained by DIAL are in molec m -3 , while the MWR and MLS measurements are in volume mixing ratio. Thus, temperature and pressure  from NCEP reanalysis data are then used to convert from molec m -3 to volume mixing ratio. Due to the fact that MLS and DIAL have a better vertical resolution than the MWR, the MLS and DIAL profiles were degraded to the MWR resolution taking into account the averaging kernel functions A, which represent the response of the retrieved ozone profile to the "true" one. The following expression was used to degrade the vertical resolution  (Palm et al. , 2010) : x LR = x a + A (x HR -x a ) , (2) where x HR is the MLS or DIAL ozone profile (depending on which instrument is inter-compared with the MWR) with the original vertical resolution.
225; σ τ = 0. 041) for the analysed period and studying the correlation between MLS and MWR. We noted that the correlation between MLS and MWR at different altitudes increases when opacity decreases (not presented here). To evaluate the correspondence between instruments, the mean bias error (MBE) was calculated between the MWR and the DIAL and MLS measurements (x LR A ) for the considered altitudes (27, 37 and 65 km): MBE = 100 × MWR A -x LR A x LR A , (3) www. ann-geophys. net/37/613/2019/ Ann. Geophys. , 37, 613-629, 2019 where MWR A is the average of the MWR ozone mixing ratio at each altitude and x LR A is the average of the DIAL or MLS measurements at the same altitude. Finally, a linear regression analysis between each dataset pair at each altitude was performed and the correlation coefficient R was analysed.
MWR-MLS comparison Figure  5a, c, e  show the time series of the MLS and MWR ozone mixing ratios for 27, 37 and 65 km for measurements when the opacity was less than 0. 29 (see Sect. 2. 3). The behaviour of both data series is similar for all altitudes considered. Figure  5b, d, f  present the scatterplot between both instruments at different altitudes and the linear regression together with the correlation coefficient (R). Table  1  summarizes the results of the comparisons between datasets. We compared N = 84 MWR-MLS measurement pairs, taking into account the spatial selection criteria according to the location of the MLS measurement with respect to the location of the MWR (LatOAPA ±0. 2 • ; LongOAPA ±5 • ). The linear regression analysis at 27 km presents a slope of 1. 01 and an intercept value of 0.