dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional Foundational Examples One important and original aspect is that the mwp-flow analysis ignores Boolean conditions, assuming that both if-branches evaluate, and that loops executes an arbitrary number of cycles. This lets pymwp analyze non-terminating programs without complications, and justifies why all conditions will be abstracted as b. Letting C1 ≡ X2 = X1 + X1 and C2 ≡ X3 = X2 + X2, Example 1 established that the iterative composition while b C1; while b C2 has a polynomial growth bound, i. e. , the property of interest. 5  We now elaborate on mwpflow analysis behavior by inspecting two more expository programs, letting C3 ≡ X3 = X3 * X3. Example 2. Consider program while b C3. Even if C3 in itself admits the bound X3' ≤ X3, the value stored in variable X3 will grow exponentially on each iteration.
Comprehensive evaluations of these tools have also been performed recently  [9, 11, 25]. The main distinguishing factor between these tools and pymwp is the program's complexity property of interest: pymwp evaluates the existential of polynomial growth bounds w. r. t. initial values. We illustrate the difference in obtained bounds in Table  1. It is not an extensive comparison but suffices to show that pymwp differs in its aims from the other related techniques. of C programming language presented in Sect. 3. 3. The name alludes to its implementation language, Python, which we selected for its flexibility and use in previous related works  [4, 19, 20]. Our tool takes as input a path to a C program, and returns for each function it contains a growth bound-if at least one can be established-or a list of variable dependencies that may cause the exponential growth.
Usage There are multiple ways to use pymwp. It has a text-based application interface, and can be run from terminal, or it can be imported as a Python module into larger software engineering developments. The analysis is automatic and readonly, therefore it is possible to pair pymwp with other tools and integrate it into compilation or verification toolchains. The online demo provides one example use case. It is a web server application with pymwp as a package dependency. Other derived uses can be developed similarly. The easiest way to install pymwp is from PyPI, using command pip install pymwp. The default interaction command is pymwp path/to/file. c  [args]  where the first positional argument is required. By default, pymwp displays the analysis result with logging information, and writes the result to a file.
Scope of Analyzable Programs The programs analyzable with pymwp are determined by its supported syntax. pymwp delegates the task of parsing C files to its dependency, pycparser  [21] , which aims to support the full C99 specification. Programs that cannot be parsed will expectedly throw an error. Otherwise, analysis proceeds on the generated AST, and pymwp handles nodes that are syntactically supported by its calculus. 7  It skips unsupported nodes with a warning. We decided on this permissive approach, because it allows to obtain partial results and manually inspect unsupported operations. However, to establish a guaranteed of bound, the input program must fully conform to the supported syntax of the calculus. Currently the syntax has limitations, e. g. , arrays and pointer operations are unsupported.
4. 4]. This was achieved thanks to a complex Relation data structure, 9  but extracting finer information from that data structure remained an outstanding problem. In particular, we wanted to provide the following feedback to the programmer: (i) If no bound exists, the location of the exponential growth. (ii) If bounds exist, the value of at least one of them. The current version of pymwp can now provide this feedback, thanks to a long maturation that we now detail.
Exposing Sources of Failure Since pymwp identifies polynomial bounds, it reports failure on programs containing at least one variable whose value grows exponentially w. r. t. at least one of its initial value. Earlier tool versions would indicate that failure was detected without reporting the involved variables. Determining this information is complicated because of our treatment of non-determinism, but it is valuable, as addressing one of those points of failure would suffice to obtain a polynomial growth bound. Even if the program cannot be refactored satisfactorily, then analyzing the exponential growth allows to assess potential impact on the parent software application. Our solution is to record additional information about ∞-coefficient in the Relation data structure, and to list all variable pairs on which failure may occur.
Efficiently Determining Bounds In the alternative case, where bounds are determined to exist, the next step is to evaluate the bounds-step 3(c) in the pymwp workflow (Sect. 3. 1). This is problematic because the calculus can yield an exponential number of bounds w. r. t. the program size, as illustrated in Table  3  with e. g. , benchmark 32. long. As a result, the evaluation phase-e. g. , extracting from this conglomerate of derivations the bounds-is increasingly costly. Handling this task efficiently required to discover a computational solution, and finding a compact format to represent the results in interpretable and memory-efficient manner. For simplicity we describe this process only at high-level, but refer to the implementation for complete details. Determining mwp-bounds requires two separate steps, starting with the Relation data structure generated during analysis phase.
Experimental Evaluation To establish that pymwp is a practical and performant static analyzer, we evaluated it on a benchmark suite of canonical C programs. We ran the analyzer on the benchmarks and measured the results, thus conducting an evaluation of performance and behavioral correctness. We did not perform tool comparison or use a standard suite for two reasons: absence of a representative comparison target (cf. Sect. 2. 3) and syntactic restrictions that limit the scope of analyzable programs (cf. Sect. 3. 3). However, the choice methodology judiciously evaluates pymwp, and facilitates transparency and reproduction of experiments. We actively put heavy emphasis to ensure-with software engineering best practices e. g. , tests, documentation [23], and long-term archival deposits  [7] -that pymwp, and the evaluation presented here, are available and reusable for future comparisons.
Methodology Benchmarks Description The suite contains 50 C programs, written in the subset of C99 syntax supported by pymwp. The benchmarks are designed purposely to exercise various data flows that pose challenges to the analyzer, e. g. , increasing parameters, binary operations, loops and decisions, and various combinations of those operations. The benchmarks are organized into seven categories based on their expected result (∞ vs. non-∞); origin in related publications  [5, 16] , and in this tool paper; and interest (basic examples, others). We omit these categories here, but they are apparent in the benchmarks distribution. The suite is available from pymwp repository [24], and as a release asset on GitHub, and Zenodo  [7]. Metrics For each benchmark, we record 1. benchmark name, corresponding to C file name, followed by ": program name" if a file contains multiple programs.
Experimental Setup The measurements were performed on a Linux x86_64, kernel v. 5. 4. 0-1096-gcp, Ubuntu 18. 04, with 8 cores and 32 GB virtual memory. The machine impacts only observed execution time; other metrics are deterministic. The software environment was Python runtime 3. 8. 0, gcc 7. 5. 0, GNU Make 4. 1, and the dev dependencies of pymwp. Because the measurement utilities of pymwp are not distributed with its release, the experiments must be run from source. We used source code version 0. 4. 2. The command to repeat experiments is make bench. It runs analysis on benchmarks and generates two tables of results.
Materials and methods Seventeen MS patients participated in this study. They were examined at both 3T and 7T. MR imaging examination included a MAGNIMS protocol with an axial T2*GRE-WI sequence (named "optimized MAGNIMS protocol") and a 0. 9-mm isotropic 3D-MP2RAGE sequence at 3T, as well as a 0. 7-mm isotropic and 0. 3-mm in-plane resolution anisotropic 3D-MP2RAGE sequences at 7T. Each dataset was read by a consensus of radiologists, neurologist, and neuroscientists. The number of lesions and their topography, as well as the visibility of the lesions from one set to another, were carefully analyzed.
Population We analysed data collected from a prospective study focusing on the 3 and 7 Tesla multimodal assessment of spinal cord in MS that took place from December 2017 to September 2019. The study was approved by the local ethics committee and written informed consent was collected from each participant prior to the MR examinations. Inclusion criteria were a diagnosis of MS according to the revised McDonald 2017 criteria  4  , patients older than 18 years, clinical symptoms suggesting spinal cord involvement, and MR imaging examination performed at least 3 months after steroid infusion. Patients with uncertain diagnosis, in relapse, or with a standard contraindication to an MR examination such as incompatible implanted equipment, claustrophobia, agitation, intrabody metal fragments, were excluded.
Considering the 3T datasets, Demortiere et al  14  previously reported an improved lesion detection using the 3T MP2RAGE sequence as compared to the conventional MAGNIMS set. In our protocol, an "optimized" MAGNIMS set including an axial 2D T2*GRE-WI sequence, providing high in-plane axial resolution (0. 5 mm) with high white matter / gray matter / lesion contrast, was used instead. Despite being considered as optional according to the latest guidelines  5  , the T2*GRE-WI sequence nonetheless allowed the detection of many lesions not visible otherwise, thus explaining the relative success of our optimized MAGNIMS as compared to the 3T MP2RAGE. The relatively poor performance of 3T sequences at C4 level may be attributed to the acquisition of the T2*-WI sequence in two slabs, where C4 constitutes the inferface between the slabs.
This work was supported by the  ARSEP Fundation (Fondation pour l'Aide à la recherche sur la Sclérose en Plaques)  and  CNRS  ( Centre National de la Recherche Scientifique ). It was performed within a laboratory member of  France Life Imaging network  (grant  ANR-11-INBS-0006 ), on the platform  7T-AMI , a  French "Investissements d'Avenir" programme (grant   ANR-11-EQPX-0001 ).
Fundings This work was supported by the  ARSEP Foundation (Fondation pour l'Aide à la recherche sur la Sclérose en Plaques)  and  CNRS  ( Centre National de la Recherche Scientifique ). It was performed within a laboratory member of  France Life Imaging network  (grant  ANR-11-INBS-0006 ), on the platform  7T-AMI , a  French "Investissements d'Avenir" programme (grant   ANR-11-EQPX-0001 ).
Data and Methods Our data come from the MMP, which includes 27,706 households randomly selected from 174 communities in 24 Mexican states, as well as 1075 households of settled out-migrants in the United States. The data comprise information from randomly sampled households in communities located throughout Mexico. Households are interviewed each year during the winter months, when seasonal migrants tend to return to Mexico. Data on settled out-migrants in the United States come from respondent-driven samples of migrants who no longer return regularly to Mexico. We ran analyses both including and excluding the U. S. sample, considering that the decision to bring savings back to Mexico is closely related to return migration. (Migrants can only bring money back to Mexico if they make a return trip across the border.
) However, considering that the results were very similar, we present results including the U. S. respondents to increase the sample size. The interviews gather detailed social, demographic, and economic information on the household and its members. They further collect basic migration information on each person's first and last trip to the United States. Household heads and spouses are administered a year-by-year labor history and those heads who have migrated are asked detailed questions about their last trip to the United States, focusing on employment, earnings, and the use of U. S. social services. In this way, considerable retrospective life history data are compiled. Communities in Mexico are chosen to provide a range of different sizes, regions, ethnic compositions, and economic bases.
S. migration experience, such as Mexico's 1992 and 1997 National Survey of Population Dynamics, indicate that the MMP data provide a remarkably accurate profile of the U. S. migrant population  (Massey & Capoferro, 2004; Massey & Zenteno, 1999; Munshi, 2003). Detailed information about the MMP is available online (https:// mmp. opr. princ eton. edu). In this study, we examine the behavior of household heads on their last trip to the United States, restricting the sample to trips that occurred in 1970 or later. The median number of years elapsed between the survey and the end of the most recent trip is three years. We also restrict the sample to persons 15 years of age or above at the time of the last trip (losing 72 individuals) and exclude legal temporary migrants given that the terms of their employment are determined by agreements between employers and the U.
Acknowledgements Z. E, Cs. D. , Z. H. , Gy. H. , I. K. , L. S. and M. M. J. were supported by  NKFIH  under projects  TKP2021-NKTA-42 ,  K128947 ,  2021-4. 1. 2-NEMZ_KI-2021-00005and NN128072. T. R. R. acknowledges funding from the  Spanish MICINN  under contract  PID2021-127890NB-I00  and support from GSI-Darmstadt computing facilities. Y. T. was supported by  JSPS   Grant-in-Aid for Scientific Research Grants  No. JP21H01114. K. J. C. acknowledges the  JSPS International Fellowship for Research in Japan , hosted by the  Tokyo Institute of Technology. D. S. A. , K. I. H. and D. K. acknowledge the support from the  IBS  grant funded by the  Korea government  (No. IBS-R031-D1 ). V. P. acknowledges the  KAKENHI Grant-in-Aid for Young Scientists  B No. 16K17719. A. A. acknowledges the  JSPS International Fellowships  for their travel support.
Estrogen activation increases distal enhancer-enhancer interactions, and stabilizes folding of the PGR domain. To investigate the chromatin architecture of the PGR domain in situ, we analyzed the spatial relation between the PGR promoter and its enhancers (ERBSs) by 3D DNA FISH  (Kocanova et al. , 2018). Fosmid probes were selected according to availability corresponding to ERBSs and the TSS of the PGR gene (Fig. 3A ). We performed 3D DNA FISH on MCF7 cells prior to and after 45 min and 3 h of 100 nM E2 stimulation and we measured the inter-probe distances using homemade script (plug in) running on ImageJ (Fig. 3B ). 3D distance measurements between pairs of fosmid probe signals were plotted (Fig. 3C  and S2A ). In general, upon E2 activation, we noted large variations in distances measured.
4A , red colored domain). Restraint-based modeling enabled extrapolating distances (d) between chosen fragments  (Fig 4A). Grey arches in figure 4B are drawn between fragments which were separated by less than 50 nm in at least 50% of the calculated models of the PGR domain in MCF7 cells grown in hormone-starved medium (-E2) for three days. Genomic sites for which distances were shortened in at least 50% of E2 treated cells (+E2) are linked by red arches (Fig. 4B ). This finding was coherent with a reduction in the 3D distances measured by 3D DNA FISH (Fig. 3C ). Distances deduced from 5C-based models and three-way 3D FISH fell within the same distribution (Fig. 2C , 2D, 3E and 4B; Fig. Sup 3A ) although the models tended toward smaller values. Obtaining the proper scale of models is not trivial for restraint-based modeling using 3C datasets  (Trussart et al.
5B ). In contrast, ER did not associate with the TSS of PGR at any of the tested time-points prior to and following hormone addition. However, even in hormone starved cells, the TSS was H3K27 acetylated and bound by MYC (Fig. 5B ) suggesting that the TSS is primed for transcription activation in MCF7 cells. Addition of E2 rapidly led to accumulation of MYC and of RNA Pol2 at the TSS. The binding profile of RNA Pol2 at the TSS and the PGR gene body varied over time. We thus calculated the RNA Pol2 pausing index and quantified mRNA synthesis using RNAseq and GROseq datasets (Fig. 5C ). We observed a 1. 5-fold (GROseq data) and a 6-fold (RNAseq data) increase of RNA within the early response (45 min) of E2 stimulation compared to the control situation. The RNA Pol2 peak declined rapidly from 5 min to 40 min (+E2), a time window during which PGR was modestly transcribed (Fig.
1x SSC at 60°C before being mounted with 4μl of Vectashield® Antifade Mounting Medium with DAPI at 1. 5μg/ml and sealed with transparent nail polish. Image Analysis Software from Molecular Devices). Images were analysed using a home-made script running on ImageJ  (Kocanova et al. , 2018). Inter-probe distances were determined from 80-130 nuclei for each experimental condition and the significance of any difference in the data distributions was assessed using Fisher's test. A p-value ≤0. 05 was considered statistically significant. human genome assembly (hg19/GRCh37) using Burrows-Wheeler Aligner  (Honkela et al. , 2015; Li and Durbin, 2010)  2010) (0. 7. 17) with default parameters. ER peak calling was performed using MACS2  (Zhang et al. , 2008). ERBS were defined according to ER peak calling in +E2 40min condition.
Published Bigwig files were generated and normalized (RPKM) using scripts from deepTools utilities. (3. 0. 2)  (Ramírez et al. , 2016). Paused RNA Pol2 indices were defined as the ratio of RNA Pol2 (total) density in the promoter-proximal region (-30 bp to TSS from + 300 bp) to the total RNA Pol2 density in the transcribed regions (TSS + 300 bp to TES). 5C matrices were visualized using WashU Epigenome Browser  (Li et al. , 2019). ER cofactors and epigenetic marks were defined and analyzed according to published data: Supplementary Table  1.
RNA-seq processing RNA-Seq time course in MCF7 before and after E2 treatment were downloaded from GEO (accession number: GSE62789)  (Honkela et al. , 2015). Raw data were downloaded with fastq-dump from sratoolkit (2. 8. 2-1). The quality of the reads was estimated with FastQC (0. 11. 7). Alignment was performed using STAR (2. 6. 0c) to the hg19/GRCh37 reference human genome assembly. Gene expression values were quantified from RNA sequencing data using HTSeq (0. 9. 1) and were then normalized (RPKM) in both cell lines.
Conversely, a decrease of contacts in at least 20% of models with respect -E2 models indicated a decrease of an arch (blue color). Distance analysis: Euclidean distances in Cartesian space between selected fosmids were calculated for the entire ensemble and represented as box plots. If a fosmid occupied more than one particle, the center of mass of the constituting particles was used as the x,y,z coordinates of the fosmid. Ensemble visualization: the UCSF Chimera package  (Yang et al. , 2012) , a highly extensible program for interactive visualization of molecular structures, was used to produce all images of the models. Models were superimposed and visualized with a wire representation within a transparent molecular density map of the occupancy of all particles in the models (obtained using the molmap function in Chimera).
Acknowledgements We acknowledge support from the light imaging  Toulouse CBI facility (LITC). This work was supported by grants from  HFSPO RGP0044  to JD, MMR,  KB ;  Agence Nationale de la Recherche (ANR) :  SINFONIE AAPG-18-CE12-006  to KB. MAM-R acknowledges support by the  Spanish Ministerio de Ciencia e Innovación  ( PID2020-115696RB-I00 ). JD acknowledges support from the  National Human Genome Research Institute  ( HG003143 ). JD is an investigator of the  Howard Hughes Medical Institute.
(which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. ChIPseq data in MCF7 were downloaded from GEO DataSets (https://www. ncbi. nlm. nih. gov/geo/) and treated as subsequently described. Raw data were downloaded with fastq-dump from sratoolkit (2. 8. 2-1). (http://ncbi. github. io/sra-tools/). The quality of the reads was estimated with FastQC (0. 11. 7). Sequencing reads were aligned to the reference preprint.
Data were fitted with the Langmuir model Eq. (  1 ) here reported: where c is the resonance wavelength at the analyte concentration c, 0 is the resonance wavelength value at the blank, max is the maximum value of Δλ (calculated by the saturation value minus the blank value) and K is a dissociation constant. The Langmuir equation model and the fitting were from OriginPro software (Origin Lab. Corp. , Northampton, MA, United States).
Acknowledgements NC and LZ acknowledge the  VALERE Program  of the  University of Campania "Luigi Vanvitelli " and the support by  EU- ENICBCMED TRANSDAIRY  Project. AMB acknowledges the  Italian Ministry of University  for the Project  DM 1062 2021  " Ricercatori a Tempo Determinato di tipo A (RTDA) Azione IV. 6-Contratti di ricerca su tematiche Green " n. 40-G-15185-5  awarded to MS. MS thanks Optosensing srl for hosting.
Patients were asked at day 28 after the sixth session to take the same photos as before the protocol for follow-up. Statistical analysis was performed using the software SPSS for Windows version 18. 0 (IBM, Armonk, New York, USA) and p-value of < 0. 05 was considered statistically signi cant. Categorical variables were expressed as numbers and percentages, whereas quantitative variables were expressed by measures of central tendency, dispersion and spread. A one-sample paired Student's t-test was used to compare the changes in penile curvature angle within the group.
Results Data was collected from 54 patients who were enrolled into our protocol. All patients completed the protocol and were compliant with penile vacuum therapy. Their median age in years was 47. 5 (Mean 46. 6) ranging from 17 to 73 years old. Duration of PD was between 7 and 132 months with a median of 18 months (Mean 27. 4). The penile curvature was stabilized in 47/54 (83%) patients upon consultation. Median International Index of Erectile Function (IIEF-5) score was 20 (8-25). Patients' past ineffective treatment history are shown in Table  1. At least one risk factor was present in one patient, they are summarized in Table  2. Blood thinners were taken by 9/54 (16. 7%) patients; 7/54 (13%) were receiving a daily dose of 75 mg of acetylsalicylic acid and 2/54 (3. 7%) were receiving a daily dose of 75mg of clopidogrel 2/54 (3.
Adolescent mental health is impacted by a myriad of factors, including the developing brain, socioeconomic conditions and changing social relationships. Studies to date have neglected investigating those factors simultaneously, despite evidence of their interacting effects and distinct profiles for males and females. The current study addressed that gap by applying structural equation modelling to IMAGEN data from adolescents aged 14 years (n = 1950). A multi-group model split by sex was tested with the variables of socioeconomic stress, family support, peer problems, and brain structure as predictors, and emotional symptoms as the main outcome. Findings indicated that, for both sexes, peer problems were positively associated with emotional symptoms, and socioeconomic stress was negatively associated with family support.