dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis Categorical data were described by frequency and percentage, and continuous data were described by mean +/-SD or median (Interquartile Range). The McNemar test was used to compare the proportions of symptoms before and after the intervention. The Wilcoxon signed-rank test was used to compare preoperative and postoperative values of the different SF-36 dimensions. The Z-score test was used to compare the means of the different SF-36 dimensions of our study population and the values in general population  [12]. A p-value of less than 0. 05 was considered statistically significant. All analyses were performed using SAS version 9. 4 (SAS Institute, Cary, North Carolina).
DISCUSSION The hysteroscopic tubal implant sterilization by Essure was widely chosen in western countries because it was effective, as well as less invasive and less expensive than laparoscopic tubal ligation. Some of these women later requested removal of the Essure devices due to attributed adverse effects. This study reveals that prior to surgical removal, these patients had a lower QoL compared to the general population of women in the same age group. Three months after removal, QoL improved up to a level similar to the general population. During the first decade following its market release, the safety profile of the Essure system appeared to be good  [2, 3]. In 2013, the number of voluntary patient-reported adverse events submitted to the Manufacturer and User Facility Device Experience database rose sharply.
The difference in the frequency of repetition between the two lasers, or the beat frequency, is set to Δf = 500 Hz. Both pump and probe beams are focused, using a 50× microscope objective lens, on the free surface of the gold coated sample. Only the second harmonics of the laser sources are used. The wavelength of the probe (leader laser) is λ probe = 535 nm, while that of the pump (follower laser) is λ pump = 517 nm. To monitor the relative optical reflectivity change ΔR/R caused by the propagating CAP, the backscattered probe light is directed to a photodiode where it interferes with static reflections of the probe beam on the sample surfaces [Fig. 1(b) ]. The automatic scan and signal acquisitions are realized using NETA's built-in software. More details on the ASOPS-based setup can be found in  [62].
e. considered homogeneous) for the depth vector reconstruction. Finally, to estimate and subtract the remaining contribution of the thermal processes, we apply a local linear regression method (LOESS from MATLAB) over 500 points (0. 24 ns) to the filtered peak-free signal. This treatment is applied to all the 3600 signals of the scan presented in this paper. The signals coming out of these three steps of filtering, referred to as the "acoustic signal", are considered to contain exclusively the acoustic contribution of the signals and are further processed as discussed in the following.
Acknowledgments This research is supported by the  Public Education Service and Research of France , the  French National Centre for Scientific Research , the  Agence Nationale de la Recherche  [project  <ANR18-CE42-I2T2M> ], the  Acoustic Hub® program , and the  Région Pays de la Loire  via  RFI Le Mans Acoustique (LMAc)  [project  OPACOP 2018 ]. D. H. H. and Z. H. acknowledge support from the  Center for Thermal Energy Transport , an  Energy Frontier Research Center  funded by the  US Department of Energy, Office of Science, Office of Basic Energy Sciences. The authors wish to thank  C. MECHRI  for suggesting the use of wavelets and L. SIMON for discussions and references on the wavelet synchro-squeezed transform. We thank our colleagues from  NETA  who provided insight and expertise that greatly assisted the research.
3. The step value is computed as the average consumption of the isotonic fit values within each of the K clusters obtained in the previous point. The procedure above yields a monotone stepwise function with K pieces that is a feasible solution to LSP. This methodology is depicted in Figure  1 , with each subfigure representing one of the actions described above, from left to the right. We implement the calculation of the so-obtained upper bound on Python, using the isotonic regression and the agglomerative clustering functions of package Scikit-learn, see Pedregosa et al. [22].
Given the efficiency of this method, we generate lower bounds for any partial path π by computing one lower bound for each vertex v i. In other words, we calculate LB(v i , k(π), st(π)) as LB(v i ) for each partial path π. The total time to compute this lower bound for all v i is in O(I 2 ). We use the isotonic regression function implemented in the Python package Scikit-learn to this end (Pedregosa et al. [22] ). Relaxing the monotonicity constraint. As mentioned in Section 3, another lower bound can be obtained by relaxing the monotonicity constraint on the step values. Then, for a given partial path π, a lower bound can be computed by solving a shortest path problem with at most K -k(π) arcs from v i to v I+1 in G. One can determine the fitting error associated with the shortest paths from all vertices v i to the sink v I+1 and containing at most k arcs, for all k = 1,.
Numerical experiments Next we run a series of numerical experiments to test the effectiveness and performance of the proposed algorithm under different settings. To this end, we first use synthetic data sets to assess the sensitivity of the algorithm performance to the maximum number of steps, the noise level in the input data and the sample size. Subsequently, we consider a realistic data set consisting of price-power measurements at the main substation of a distribution power grid that includes distributed energy resources. This data can be download from  [21]. All the numerical experiments have been conducted on a laptop equipped with a Intel Core i5-4200 CPU processor, with 2. 80 gigahertz 2-core, and 8 gigabytes of RAM memory. The operating system is 64-bit Windows 8. 1. Codes were implemented in Python 3.
Both figures also include the true function  (9)  to compute the response variable x. Next, we run our shortest path algorithm using datasets of 1000 points that are randomly generated from (8) for a noise level σ taking the values of the natural numbers between zero and ten. Besides, the number of arcs K is set to six, which is the true number of blocks of the function that relates   the response variable x to the covariate p. Results for all these cases are collated in Table  2  and include the aggregated square error (Error) and three different computational times (with a maximum value of 12 hours): k-th block [p * k , p * k+1 ) u * k 1 [0, -T ISO : Computational time of the proposed shortest path algorithm including the upper bound discussed in Section 4. 1 and the lower bound per layer provided by the isotonic fit.
Acknowledgments This research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 755705 ). This work was also supported in part by the  Spanish Ministry of Economy, Industry and Competitiveness  and the  European Regional Development Fund (ERDF)  through project  ENE2017-83775-P. Martine Labbé  has been partially supported by the  Fonds de la Recherche Scientifique -FNRS  under Grant(s) no PDR  T0098. 18.
Reference population Reference data on sociodemographic and economic factors were retrieved from the European Social Survey (ESS, http://www. europeans ocialsurvey. org), Batch 7 (2014). Using frequency matching in 5-years age groups, age-matched female participants from Germany, France, The Netherlands, Poland, Sweden, and The United Kingdom were included, in order to achieve similar age distributions between the studyand the reference population. In total, 6577 women from the same countries as the study population were eligible for inclusion. After frequency matching for age, the reference population consisted of 1911 European women.
Statistical analysis Statistical analysis was performed using SPSS version 25. Descriptive statistics were used to describe baseline characteristics of the study-and the reference population. Visual inspection of e. g. skewness, kurtosis and shape of the histogram was used to determine normality of the continuous variables. The following statistical analyses were executed: (1) To compare the SES of the study population with the reference population, binary logistic regression was used to calculate Odds Ratios (OR) with 95% confidence intervals (95%CI). Thereafter, the OR was adjusted for country of residence (OR adj ), to correct for the differences in country of residence between the study-and the reference population. For continuous variables, linear regression was used to correct for country of residence leading to p-values from the adjusted analyses (P adj ).
Acknowledgements The dsd-LIFE group are:  Birgit Kohler  and  Uta Neumann , Berlin;  Peggy Cohen-Kettenis ,  Baudewijntje Kreukels and Annelou de Vries, Amsterdam; Wiebke Arlt, Birmingham ;  Claudia Wiesemann ,  Gottingen ;  Jolanta Slowikowska-Hilczer ,  Lodz ;  Ute Thyen  and  Marion Rapp ,  Lubeck ;  Aude Brac de la Perriere, Lyon ;  Charles Sultan  and  Francoise Paris , Montpellier;  Nicole Reisch , Munich;  Annette Richter-Unruh , Munster and Bochum;  Hedi Claahsen -van der Grinten ,  Nijmegen ;  Claire Bouvattier  and  Lise Duranteau , Paris;  Anna Nordenstr€  om and  Agneta Nordenskj€  old, Stockholm;  Catherine Pienkowski , Toulouse;  Maria Szarras-Czapnik , Warsaw. We are grateful to the persons who participated in dsd-LIFE and to all of the study centers for their enthusiasm and dedication to contact potential participants and collect high-quality data.
After removal of toluene, the residue was extracted into hexane and filtered through a frit using Celite. The brownish-green hexane extract was dried in vacuo, and taken in 10 mL of THF and heated at 60 °C in presence of [Co 2 (CO) 8 ] (0. 03 g) for 16 h. The solvent was evaporated in vacuo followed by the extraction of the residue into hexane/dcm and passing it through Celite. After removal of solvent from the filtrate, the residue was subjected to chromatographic workup using silica gel TLC plates. Elution with a hexane/CH 2 Cl 2 (70:30 v/v) mixture yielded black 2 (0. 013 g, 12%), brown 3 (0. 019 g, 18%)   1985 , 1841 , 1798 (CO) (CO). Computational details. Optimization of all the molecules were carried out using the Gaussian 16 program  [32]. Optimizations were performed employing the gradient-corrected B3LYP  [33]  functional along with a Def2-SVP basis set taken from EMSL (EMSL Basis Set Exchange Library https://bse.
pnl. gov/bse/portal ). The model compounds were fully optimized in the gaseous state without any solvent effect starting from the coordinates obtained from X-ray crystallography. To reduce the computational effort, Cp analogues were chosen to mimic compounds 1, 3 and I. Frequency calculations were carried out to check the nature of the stationary geometries and to confirm the absence of any imaginary frequency. Wiberg bond indices (WBI)  [28]  were obtained from a natural bond orbital analysis (NBO)  [29]. All the optimized structures and orbital pictures were plotted using Chemcraft (Chemcraft -Graphical Software for Visualization of Quantum Chemistry Computations. https://www. chemcraftprog. com ) visualization programs. Two-dimensional electron density and Laplacian electronic distribution plots were generated using the Multiwfn package.
[34]. X-ray structure determination. Suitable X-ray quality crystals of 2 and 3 were grown by slow diffusion of a hexane-CH 2 Cl 2 solution. The crystal data of 2 and 3 were collected and integrated using a Bruker D8 VENTURE diffractometer with a PHOTON II detector with graphite monochromated Mo-K α ( λ = 0. 71073 Å) radiation at 297(2) K. The structures were solved by dual-space algorithm using SHELXT program  [35]  and refined with full-matrix least-squares methods based on F 2 (SHELXL program)  [36]. The molecular structures were drawn using Olex2  [37]. The nonhydrogen atoms were refined with anisotropic displacement parameters. Except for the bridging ones, hydrogen atoms were introduced through Fourier difference maps analysis. H atoms were finally included in their calculated positions and treated as riding on their parent atom with constrained thermal parameters.
Sampling and Data Data for this paper were obtained from a Behavioral Surveillance (BSS) Survey that collected information on several health issues, including breastfeeding practices and factors that might influence these practices. We used the data to explore the socioeconomic status (SES) related inequalities in the population distribution of exclusive breastfeeding ideation and exclusive breastfeeding practice at the household level. The BSS data were obtained through a two-stage cluster-sample, a crosssectional population survey of over 3000 women with a child under two years in wards from Kebbi, Sokoto, and Zamfara states. The survey sample size was based on the BR-N evaluation design  [24] , allowing for a 10% non-response rate, a power criterion of 0. 80, an alpha coefficient of 0. 05, and varying intra-cluster correlations and minimal detectable differences for priority outcomes of the evaluation.
At the first stage, 108 enumeration areas (EA) (36 in each state) were selected from BR program wards using digital maps and a grid sampling methodology. At the second sampling stage, all households within each sampled Enumeration Areas (EA) were randomly sampled to select households with a resident childbearing age (14-49-year-old) woman who had a child under two years. Responses from the sampled women were obtained through face-to-face interviews using the household and female pilot-tested questionnaires by trained interviewers. Information was obtained on usual resident household members and household assets and characteristics. The female questionnaire was used to collect data on respondents' demographics, reproductive history, contraceptive use, media exposure, gender norms, exclusive breastfeeding and ideations related to breastfeeding while conducting the interviews in the local (Hausa) language.
The analysis was based on the bivariate approach to measuring inequalities  [23, 44]. This approach looks at the subset of breastfeeding behavior and ideation factors inequality occurring across the distribution of households or individuals (ranked by SES) by typically comparing the cumulative proportion of households or individuals ranked by ideation or SBC behavior against the cumulative proportion of SES  [25, [45] [46] [47] [48]. The five-scale Likert responses were dichotomized to fit the bivariate regression model for estimating the Lorenz curve and the CIx index. The Lorenz estimate, Lorenz curve, and the Foter-Greer-Thorbecke (FGT_CI) routines of STATA 16 © (STATA Corporation, College Station, TX, USA) statistical software were used for all analyses. Also, Microsoft® Excel spreadsheet was used to construct the diverging bar graph (Figure  1 ).
The purpose of this study was to assess the impact of measures designed to mitigate the spread of coronavirus disease 2019 (COVID-19) on worldwide cancer screening. We systematically searched PubMed, Ovid MEDLINE, the Cochrane COVID-19 Study Register, ClinicalTrials. gov, and EMBASE without language restrictions for studies published between January 1, 2021, and February 10, 2021. Studies selected for full-text review contained data on patients screened for any type of cancer during the COVID-19 pandemic and comparison data from a time interval just prior to the pandemic. Data were obtained through dual extraction. All the included studies were assessed for quality and risk of bias. A meta-analysis was performed on 13 studies: 7 on screening mammography, 5 on colon cancer screening, and 3 on cervical cancer screening.
Search Strategy, Study Selection, and Inclusion Criteria We began our systematic review by writing a study question and protocol (Supplemental Appendix, available at https://www. mcpiqojournal. org), which was registered through PROSPERO (ID: CRD42021241831) on March 11, 2021. Our systematic review is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. 7 e developed our search strategies using medical subject heading terms and text words that were selected based on common indexing practices. Search terms were compiled and tested repeatedly to produce sensitive searches to capture potentially relevant publications. We searched the following databases: PubMed, Ovid MEDLINE, the Cochrane COVID-19 Study Register, ClinicalTrials.
Data Collection and Quality Assessment We collected initial references in citation files (using Covidence software), removed duplicates, and began our screening process for titles and abstracts against eligibility criteria. Two reviewers (M. M. and R. A. S. ) independently reviewed abstracts for inclusion in the initial screening phase, followed by the fulltext screening phase of our systematic review. Studies were selected for full-text review if they contained data on patients screened for any type of cancer during the COVID-19 pandemic and contained comparison data from a time interval just prior to the pandemic. Disagreements among reviewers in the initial abstract screening phase and fulltext review were resolved by consensus by 2 reviewers (M. M. and R. A. S. ). Disagreements among reviewers in the full-text screening phase were reconciled by discussion and consensus with a third reviewer (B.
P. ). Two reviewers (M. M. and R. A. S. ) evaluated all selected studies from phase 2 of screening independently for inclusion in data extraction. Data extracted from studies included study description (research setting), methods used to record screening rates, and comparison data of screening rates before 2019. Two reviewers (M. M. and R. A. S. ) also independently extracted data from the included studies and performed an assessment of the quality and risk of bias of all included studies using the Newcastle-Ottawa Quality Assessment Tool for the Observational, Cohort and Cross-Sectional Studies available from the National Institutes of Health. 8 The quality of evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) approach. 9 tatistical Analyses A meta-analysis was performed for 3 outcomes: changes in screening rates for breast cancer, colon cancer, and cervical cancer.
Data on lung cancer screening were insufficient to be included in our meta-analysis. The comparison groups were the time periods before and during the COVID-19 pandemic. The screening outcomes were reported as incidence rates, and the pooled effect size reported in this analysis is the incidence rate ratio (IRR). The IRR was calculated from each study using the number of patients screened in time intervals (days) before and after the start of the COVOD-19 pandemic. The individual study IRRs are unadjusted, and both the IRRs and the pooled IRR were calculated using the default assumption form the meta::metainc function in R statistical software, version 4. 9-6 (R Foundation for Statistical Computing). The pooled IRR statistic for breast cancer screening was based on 7 studies; data for colon cancer screening was based on 5 studies; and the analysis of cervical cancer screening was based on 3 studies.
Two of our studies reported on more than one type of cancer screening. The pooled IRRs were calculated using the inverse variance method, and random effects models were presented. Random effects models were used because the intention of our meta-analysis was to generalize the results beyond the included studies given the universality of our data and the heterogeneity of our study of several patient populations. For consistency, the same model was employed for all 3 outcomes. Heterogeneity between studies was assessed using the c 2 test and I 2 statistic. Leave-one-out analyses were performed to calculate pooled estimates to determine if studies with high influence were impacting the significance of the results. (Figure  1 ). We identified 748 articles for our systematic review with 113 identified as eligible for full-text review.
22 It seems reasonable to expect an increase in cancer-specific mortality because of the decrease in cancer screening rates; however, it remains unclear to what degree this may occur. The abrupt decrease in cancer screening resulting from the lockdowns during the pandemic was unprecedented. Global effects on future cancer mortality due to the pandemic have widespread public health ramifications that are yet to be determined. Another important feature of our analysis is the consistent results across a wide spectrum of health care settings. We analyzed studies performed in multiple countries, including the United States, Italy, Taiwan, the Netherlands, France, and Romania. The data that researchers collected came from a range of sources as well. For instance, some examined hospital records from single or multiple hospitals.
12, 15, 16, 19 Others mined regional or national health care databases. 10, 13, 14, 17, 18, 20 ne study used data from insurance company claims for screening procedures. 17 Notably, the reported decreases in cancer screening rates were consistently large within each cancer type; the range in the decline of screening mammography rates was 51% to 77%, screening colonoscopies decreased to 1% to 55%, and cervical cancer screening was 5% to 18% of the preeCOVID-19 pandemic rates. The consistency of these results is support for generalizability of our findings across various health care settings. Limitations of our study are reflective of our study design. Our study question could only be answered by collecting and analyzing data from retrospective observational studies of cohorts of patients or data collection from patient registries.
database search and study selection process are outlined in the flow diagram Identification Records identified through database searching (n=966) Additional records identified through other sources (n=0) Duplicates removed (n=218) Screening Records screened (n=748) Records excluded (n=635) Full-text articles assessed Full-text articles excluded, for eligibility with reasons Eligibility (n=113) Studies included in qualitative synthesis (n=13) (n=100) 69 wrong study design 22 wrong outcomes 2 duplicates 3 review articles 2 wrong setting 1 wrong patient population Included Studies included in quantitative synthesis (meta-analysis) (n=13) FIGURE1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) flow diagram. CANCER SCREENING DURING COVID-19.
Spiridon Emergency Retrospective Colonoscopy 199 199 et al, 19 Hospital electronic records observational 2021 study Cervical cancer Gorin University of Michigan, US, Retrospective Cervical cytology (Papanicolaou 52 52 et al, 11 ambulatory medicine clinic observational testing) and HPV testing 2021 study Mantellini Italy, 20/21 regions database Retrospective Cervical cytology (Papanicolaou 151 152 et al, 13 observational testing) 2020 study Miller Southern California, integrated Retrospective Cervical cytology (Papanicolaou 78 85 et al, 20 health care system observational testing) and/or HPV testing 2021 study COVID-19, coronavirus disease 2019; HPV, human papillomavirus; US, United States.
52), ubiquitin I ([M+H] + 8565. 76) and myoglobin ([M+H] + 16952. 31). Spectra were processed with flexAnalysis software (Bruker Daltonics). Sample preparation was performed with the dried droplet method using a mixture of 0. 5 µl of sample with 0. 5 µl of matrix solution dried at room temperature. The 2-[(2E)-3-(4-tert-butylphenyl)-2-methylprop-2-enylidene]malononitrile (DCTB) matrix was prepared at 20 mg/ml in dichloromethane.