dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to thank  Jean-Marie Michielsen  and  Hyejeong Kim  for the image annotation and  Paul Goedhart  for the statistical analysis. Furthermore, we appreciate the brainstorm sessions we had with our colleagues  Manya Afonso ,  Ard Nieuwenhuizen ,  Janne Kool ,  Keiji Jindo , and  Andries van der Meer. Finally , we would like to thank  Rik van Bruggen  from  Agrifac Machinery B. V. and  Colin Chaballier  from  Exxact Robotics  for their support.
This significantly improved the fit of the ephemerides to the Cassini observations  (Di Ruscio et al. , 2020). With their latest  INPOP version, INPOP21a, Fienga et al. (2021)  proposed a model based on real KBO orbits extracted from the Astorb database as a way to represent the distribution of these outer planet perturbers more realistically. Finally, a circular ring representing the rest of the Kuiper belt, modeled as 36 point masses with an equal mass located at 44. 0 au, was added in DE440  (Park et al. , 2021). The method to calculate the Newtonian gravitational acceleration from hundred asteroids is usually the point-mass method. However the precise calculation of the acceleration with a continuous ring is a fundamental but complicated task  (Fukushima, 2010b; Broucke and Elipe, 2005) , although the perturbations induced by a ring have been extensively studied by Laplace, Maxwell and others.
MBAs description There are about 1. 1 to 1. 9 million MBAs larger than 1 kilometer in diameter, as well as millions of smaller asteroids 1  , but only a limited number of them can be observed from the ground or space telescopes  (Masiero et al. , 2011; Ryan et al. , 2015). These asteroids might come from several planetary embryos with different density parameters. On the additional information and family classification for MBAs, we can read  (Masiero et al. , 2013; Jiang et al. , 2020; Jiang and Ji, 2021). In this study, we analyzed the known asteroids, excluding very small objects. The data sets extracted from Nesvorny (2015) and distributed on https://pds. nasa. gov/ds-view/pds/viewProfile. jsp? dsid=EAR-A-VARGBDET-5-NESVORNYFAM-V3. 0 contain 122 families comprised of about 137,000 asteroids, calculated from synthetic proper elements based on the hierarchical clustering method (HCM)  (Nesvornỳ et al.
, 2015). Based on the proper semi-major elements of the asteroids, MBAs can be classified as, the inner main belt families with the Radius () of 2. 0 <  < 2. 5 au, the central belt families with 2. 5 <  < 2. 82 au, and the outer main belt families with 2. 82 <  < 3. 7 au  (Usui et al. , 2012). This classification does not apply to the high-inclination MBAs, but these asteroids are included in the Nesvorny data sets and can be considered as a special group and included in ring analysis. In the last sub-graph, -axis is given as a count rather than as a normed count in order to pronounce the differences for the closed four groups, because this number for each group asteroids is relatively small.
The solar system integration procedure described here is based on Rebound, an open-source multi-purpose N-body code for collisional dynamics 2. We took advantage of fast integration tools in Rebound to develop a solar system integration procedure  (Rein and Liu, 2012). ReboundX contains a well-developed library for adding conservative and dissipative forces to otherwise symplectic N-body integrations 3   (Tamayo et al. , 2020). As the ring model that we studied can be conservative, thus we adopted the symplectic integrator from the well-developed ReboundX library. This library includes the Wisdom-Holman Fast integrator. This algorithm can be fast and efficient with 1e-3 time-step in this study and very convincing in modeling test A simple random sampling was adopted from the entire asteroid population.
A  ′  test was used to test if  samples have equal variances. If passed, this test can ensure that the sample is not an outlier and that the sample is random. We introduce a Kullback-Leibler divergence to measure the difference in the probability distribution between the sampling and the total population and provide the confidence interval. Furthermore, the computation time and model accuracy loss was also considered in sampling size selection. experiments, but may be not the most precise tool for planetary integration 4   (Tamayo et al. , 2020). The ReboundX library permitted the inclusion of the additional forces caused by asteroid rings in the integration procedure. All the code was written in Python combined with FORTRAN 90.
Acknowledgments We thank  Prof. T. Fukushima  and  Prof. D. Tamayo  for meaningful discussion. The work is supported by  China Scholarship Council  ( 201906270228 ) and the  National Natural Science Foundation of China  ( U1831132 ,  41874010 ,  41374024 ). We thank the  French Space Agency (CNES)  for the support of the INPOP development as well as the  French National Program from Gravitation, PNGRAM.
Images were acquired using a Zeiss AxioImager M1 and images were processed by deconvolution using Zen 2. 3 Pro software (Zeiss). The area of each cell was measured using a pipeline developed on CellProfiler  [40]. Briefly, sample images were acquired at 10X magnification to allow for >100 cells to be captured per image. Image files were uploaded to the CellProfiler workspace and analysis was performed for images taken from 3 replicate experiments.
A multichromaticconjugated secondary antibody, goat anti-mouse IgG (BD Biosciences, cat# 550589) diluted 1:1000 in Stain Buffer was applied and cells were incubated for 1 h at room temperature, keeping samples protected from the light. Cells were eventually suspended in BD Stain Buffer and filtered with a 70 m filter. Cells were analysed using the BD FACSAria (BD) and the data was analysed using FloJo software. Analysis of the stained cell populations was performed by gating on single, live cells.
Synthesis of cDNA and RT-qPCR Ten nanograms of DNA-free total RNA, extracted as described above, was used as a template to prepare 10 μl cDNA using LunaScript ® RT SuperMix Kit (New England Biolabs, cat# E3010L) according to manufacturer's instructions. Luna ® Universal qPCR Master mix (New England Biolabs, cat# M3003L) was employed for RT-qPCR according to manufacturer's recommendations (Initial denaturation 95°C, 1 min; denaturation 95°C, 15 sec; extension 60°C, 30 sec; 39 cycles). Technical duplicate 20 μl reactions were carried out with 1 μl volumes of cDNA as template and triplicate biological samples of cDNA being analysed. Expression relative to the untreated control was calculated as 2 -ΔΔct with  [41]  gapA amplification being employed as a housekeeping control.
Assessment of clbB expression using promoter-gfp fusion reporter The promoter region upstream of clbB was amplified using the PclbB-Fw and PclbB-Rev primers Table  S2 , before digesting with BamHI/KpnI and cloning in frame upstream of green fluorescent protein (gfp) in pAJR70  [42]. Bacteria were cultured in MEM-HEPES for 4 h before recording OD 600 nm, and fluorescence using a BMG Fluostar plate reader. Relative fluorescence was determined by subtracting the fluorescence intensity of an empty vector control from each sample and dividing the corrected fluorescence intensity by OD 600 nm. Data presented are from three replicate experiments.
Absorbance Spectroscopy Aliquots of heme, resulting in final concentrations of 0. 25 to 64 mM, were added to an optical cell containing factor I (CompTech) or PBS as control, and incubated for two minutes in the dark at room temperature. The absorbance spectra in the 350 -700 nm wavelength range were recorded using an Agilent Cary 100 spectrophotometer (Agilent Technologies, Santa Clara, CA). The data on the difference between protein-bound heme and free heme at absorbance maxima (l = 395 nm) in the Soret peak was used to build titration binding curves.
BIAevaluation software (version 4. 1; Biacore) was used to evaluate the kinetic rate constants. In Silico Analyses of Heme Binding to Factor I The atomic coordinates of factor I were taken either from PDB ID 2XRC  (34)  or extracted from the C3b/fH1-4:19-20/FI (PDB ID 5O32) and used for the molecular docking  (35). HexServer (http://hexserver. loria. fr/) with default parameters was used to accommodate the heme molecule to FI  (36) , as described previously for C1q and C3  (2, 37). Criteria for final factor Iheme complex selection were based on the total energy of binding. Visualization of the top ten complexes was done by PyMol (www. pymol. org). To evaluate for potential functional relevance, we visualized the predicted top ten heme-binding positions at the structure of FI alone or at the C3b/fH1-4:19-20/FI complex structure.
Statistical Analysis The SDS-PAGE data was analyzed by Image J (US National Institutes of Health, https://imagej. nih. gov/ij). The statistical analysis and absorbance spectroscopy calculations were performed using GraphPad Prism version 7. 7 for Mac (San Diego, CA). Paired t-test was applied to compare two columns, and either ordinary/repeated measures with Dunnette's multiple comparisons or non-parametric one-way ANOVA were used to compare multiple columns.
Hemopexin-factor I complex formation was evaluated by sandwich-ELISAs where pool 1 was incubated in microtiter-wells coated with antibodies directed against factor I and detected with anti-hemopexin and vice versa (Figure  4G ). Both assays showed that the eluate contained complexes consisting of both factor I and hemopexin. An effort to remove hemopexin from factor I in pool 1 by applying it to a hemeagarose column caused a complete loss of factor I (data not shown). Both pools were evaluated for their ability to degrade fluid phase C3b in the presence of co-factors. Only factor I in pool 1, and the commercial preparations were found to have catalytic activity, whereas no activity was found for factor I in pool 2 (Figure  4H ).
36 SC guidelines list five different IHD sub-types, not directly available in EHR. Based on clinical judgment of two cardiologists (AB and TL), we used available EHR data ('ESC' term) as follows: abnormal coronary microcirculation ('coronary artery aneurysm'), endothelial dysfunction ('vasospastic angina'), unstable angina (UA) ('myocardial stunning'), stable angina (SA) ('epicardial coronary disease') and MI ('myocardial scar'). We developed 36 new phenotypes based on available data and by clinical judgment (AB and TL), using the CALIBER approach, 32 a collaborative, iterative process involving multiple disciplines (e. g. clinicians, epidemiologists, computer scientists, public health researchers, statisticians), using Read codes (Version 2), ICD-10 coding, drugs and procedure (OPCS-4) codes.
The frequency of individuals was analysed by number of risk factors. We compared the observed age-and sex-adjusted and case mix-adjusted 1-year mortality by the 12 most prevalent RFs and the 14 RF groups for HF with Kaplan-Meier estimates and Cox proportional hazards models, adjusted for age and gender. The proportional hazard assumption and model fit was examined by Schoenfeld residuals and c-index. All analyses were performed with SAS (version 9. 3) and R (version 3. 4. 3).
Furthermore, we lacked echocardiographic data as these events rarely get recorded in structured EHRs using ontologies and unstructured data (e. g. clinical text and narrative as not available for research). Second, the validity of the 91 RF phenotypes, while well-established for some (e. g. hypertension, diabetes, obesity, smoking, heavy alcohol), is not known for the new phenotypes. Coding validity is through the use of comprehensive coding lists across linked EHR data, with review by two cardiologists, and prognosis lends some validity. Third, RFs were analysed by 'ever', 'first ever' and 'last ever' but neither every permutation and combination nor duration of RFs could be investigated. Therefore, we concentrated on the most common RFs for secondary analyses.
Funding A. B. is supported by research funding from  NIHR  ( NIHR200937 ),  British Medical Association (TP Gunton award) ,  AstraZeneca  and  UK Research and Innovation. D. K. is supported by grants from the  National Institute for Health Research  ( NIHR CDF-2015-08-074   RATE-AF ;  NIHR HTA-130280 DaRe2THINK ;  NIHR EME-132974 DaRe2THINK -NeuroVascular), the  British Heart Foundation  ( PG/17/55/33087  and  AA/18/2/34218 ), the  European Society of Cardiology  supported by educational grants from  Boehringer Ingelheim/BMS-Pfizer Alliance/Bayer/Daiichi Sankyo/Boston Scientific , the  NIHR/University of Oxford Biomedical Research Centre  and  British Heart Foundation/University of Birmingham   Accelerator Award  ( STEEER-AF NCT04396418 ); and  Amomed Pharma ,  IRCCS San Raffaele and Menarini  (Beta-blockers in Heart Failure Collaborative Group  NCT0083244 ).
H. H. is an  National Institute for Health Research (NIHR) Senior Investigator  and funded by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre. H. H. 's work is supported by:  Health Data Research UK  (grant No. LOND1 ), which is funded by the  UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England) ,  Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government ),  Public Health Agency (Northern Ireland) ,  British Heart Foundation  and  Wellcome Trust. A. B. , L. P. , F. A. , D. E. G. , D. K. , S. D. A. , T. D. , B. T. , S.
D. , R. T. L. and H. H. are part of the  BigData@Heart Consortium , funded by the  Innovative Medicines Initiative-2 Joint Undertaking  under grant agreement No. 116074. This Joint Undertaking receives support from the  European Union 's  Horizon 2020 research and innovation programme and EFPIA ; it is chaired, by D. E. G. and S. D. A. , partnering with 20 academic and industry partners and  ESC.
METHODS The study was registered with the clinical trial registry of India, CTRI, ref no. Before the patients were enrolled in the trial, background data on gender, date of birth, age, eating habits, current medical history, medication, treatment, prior history, and allergies (to drugs and food) were gathered. In addition, functional foods, health foods, foods high inglucans, and foods containing beta-glucans were also considered. Consumption of test foods,. body temperature, intake of food for specific health purposes, functional and health foods, intake of restricted foods, subjective symptoms, visits to medical facilities, therapy, and usage of medications were all noted during the study. Foods that were prohibited included beta-glucan-rich supplements including those formulated with concentrated beta-glucan that has been extracted from yeast, barley, mushrooms, and seaweed.
The adapters were removed once the raw data quality was examined. To exclude reads that were not aligned to the human genome, the low-quality reads were first assembled using the metaSPAdes de novo assembler for metagenomics. After assembly, Prodigal was used to carry out the gene prediction. Using the DIAMOND-MEGAN5 tool, the predicted genes were then compared to genes already present in the NCBI database. Shannon plots for microbial alpha diversity were produced. SEED-based functional assignment was performed to identify the functional differences between the control and treatment groups, followed by subsystem analysis using the linear discriminant analysis (LDA) effect size (LEfSe) method. Based on the taxonomic abundance in the provided samples, the occurrence of the dominant microbial community was investigated at multiple levels (phylum, class, order, family, and genus).
The review of the recent academic social science energy literature helped better understand the key terms, the underlying mechanisms of LEE and P2P trading and the features of particular models/initiatives. Secondly, we conducted 40 original semi-structured expert interviews during the period of January 2020 to June 2021. A list of our interviewees is offered in Table  4. Interviews lasted generally between 60 and 90 minutes and involved a robust mix of different stakeholder types. In terms of analysis and validation, interviews were fully transcribed, analyzed and coded. To be clear, the empirical data derived from the interviews was initially collected in three languages and analyzed by dedicated research teams in each of the three countries using NVivo software. However, this was an iterative process, with the author teams switching back and forth between 'deductive' codes based on our insights from the literature and expert knowledge and the 'inductive' findings from the interviews.
Native code is now commonplace within Android app packages where it co-exists and interacts with Dex bytecode through the Java Native Interface to deliver rich app functionalities. Yet, state-of-theart static analysis approaches have mostly overlooked the presence of such native code, which, however, may implement some key sensitive, or even malicious, parts of the app behavior. This limitation of the state of the art is a severe threat to validity in a large range of static analyses that do not have a complete view of the executable code in apps. To address this issue, we propose a new advance in the ambitious research direction of building a unified model of all code in Android apps. The JuCify approach presented in this paper is a significant step towards such a model, where we extract and merge call graphs of native code and bytecode to make the final model readily-usable by a common Android analysis framework: in our implementation, JuCify builds on the Soot internal intermediate representation.
We performed empirical investigations to highlight how, without the unified model, a significant amount of Java methods called from the native code are "unreachable" in apps' call-graphs, both in goodware and malware. Using JuCify, we were able to enable static analyzers to reveal cases where malware relied on native code to hide invocation of payment library code or of other sensitive code in the Android framework. Additionally, JuCify's model enables state-of-the-art tools to achieve better precision and recall in detecting data leaks through native code. Finally, we show that by using JuCify we can find sensitive data leaks that pass through native code.
INTRODUCTION Android app analysis has been one of the most active themes of software engineering research in the last decade. Static analysis research, in particular, has produced a variety of approaches and tools that are leveraged in a variety of tasks, including bug detection, security property checking, malware detection, or empirical studies. The widely-used state-of-the-art approaches, such as FlowDroid  [5] , develop analyses that focus on the Dex bytecode in apps. Unfortunately, recent studies  [1, 38, 50, 62, 69]  have shown that malware authors often build on native code to hide their malicious operations (e. g. , private data leak) or to implement sandbox evasion  [64]. The need to account for native code within Android apps is becoming urgent as the usage of native code is growing within both benign and malicious apps.
Our empirical investigation on apps from the AndroZoo  [3]  repository reveals that, in 2019, up to 62. 9% of collected apps included native code within their packages. Yet, native code is scarcely considered in app security vetting  [2, 69]. In the majority of static  [5, 13, 16, 25, 34, 47, 78] , dynamic  [6, 49, 79]  and machine learning based techniques  [48, 55] , native code is overlooked since it presents several challenges. When researchers propose techniques to address native code such as with JN-SAF  [69] , DroidNative  [2] , NativeGuard  [60] , Tain-tArt  [61]  and others  [1, 15, 31, 50] , the integrated analyses (e. g. , for taint tracking, native entry-point detection and machine learning feature extraction) are generally ad-hoc. Indeed, these works develop custom techniques to bridge native code and bytecode, typically by combining results of separate analyses of bytecode and native code.
Therefore, they do not yield an explicit unified model of the app to which generic analyses can be applied to explore bytecode and native code altogether. Our work aims to fill the gap in whole-app analysis by researching means to build a unified model of Android code. We propose JuCify, a step toward building a framework that breaks bytecodenative boundaries for Android apps and therefore copes with a common limitation of static approaches in the literature. To the best of our knowledge, JuCify is the first approach that targets the unification of Android bytecode and native code into a unified model and is instantiated in a standard representation  [36]. We target the Jimple  [67]  Intermediate Representation as support for JuCify unified model. Jimple is the internal representation in the widely-used Soot framework and is indeed the representation that is considered in a large body of static analysis works  [36].
By supporting Jimple, JuCify provides the opportunity for several analyses in the literature to readily account for native code. This paper. JuCify is a multi-step static analysis approach that we implement as a framework for generating a unified model of apps taking into account native code. It relies on symbolic execution to retrieve invocations between both the Dex bytecode and the native worlds, pre-computes native call-graph, merges Dex bytecode and native call-graphs, and populates newly generated functions with heuristic-based defined Jimple statements using code instrumentation. The main contributions of our work are as follows: • We propose JuCify, an approach to build a unified model of Android app code for enabling enhanced static analyses. We have implemented JuCify to produce the Jimple code that unifies bytecode and native code within an app package; • We conduct an assessment of the JuCify yielded model.
We show that JuCify can significantly enhance Android apps' call-graphs. JuCify connects previously unreachable methods in Android apps call-graphs; • We evaluate the unified model of app code in the task of data flow tracking. We show that JuCify can significantly boost the precision of the state-of-the-art FlowDroid, from 0% to 82% and its recall from 0% to 100% on a new benchmark targeting bytecode-native data flow tracking; • We evaluate JuCify on a set of real-world Android apps and show that it can augment existing analysers, enabling them to reveal sensitive data leaks that pass through the native code which were previously undetectable. • We release our open-source prototype JuCify to the community as well as all the artifacts used in our study at: https://github. com/JordanSamhi/JuCify The remainder of this paper is organized as follows.
Native to Java. With JNI, developers can create and manipulate Java objects within the native code (e. g. , written in C++). The fields and methods of Java objects are also accessible from the native code and can be invoked using specific JNI Interface functions. Eventually, likewise Java reflection  [14] , i. e. , using strings to get methods and classes, the developer can invoke the Java methods (e. g. , lines 17-19 in Listing 1). Note that Listings 1 and 2 illustrate the interaction between Java and C++. However, JuCify, the approach proposed in this paper, works at the apk level. Therefore, the invocations are between bytecode and compiled native code.
APPROACH For a given Android app, JuCify aims to unify its Dex bytecode and native code into a unified model and instantiate this model in the Jimple representation (i. e. , the intermediate representation of the popular Soot framework). In this section, we will first detail the overall JuCify conceptual approach, and then we will briefly present how we instrument the app to approximate the native behavior. However, due to space constraints, we will not present all technical details related to Jimple. We invite the interested reader to consider all our publicly-shared artifacts on the project Github repository  1. JuCify implementation is fully open-sourced.