dataesr/scientific-paragraphs-categorization huggingface dataset:
We were, therefore, unable to compare the incidence of anti-bodies directed against Lyme disease based on the geographical area of the patient. Besides, we considered only variables that could be extracted from clinical databases in our analysis. Additional unmeasured confounding factors (tobacco, alcohol, atopy history) contributing to CI-IRs may exist. Furthermore, patients may live or have lived in areas where Lyme disease incidences are different from the area where they were treated. An exhaustive data collection of patients' current and past locations may be needed. Verylow-risk area Amiens (N=252)   French map with estimated mean annual regional incidence rates of Lyme disease (per 100 000 inhabitants) and incidence of cetuximab-induced hypersensitivity infusion reactions (CI-IRs) in each of the four French areas.
from Septfons A, et al. Epidemiology of Lyme borreliosis through two surveillance systems: the national Sentinelles GP network and the national hospital discharge database, France, 2005 to 2016. 5 -20 4 4 5 3 0 8 (3. 2) 8 (3. 2) 16 (1. 6) (1. 6) (2. 0) (1. 2) (6. 4) Overall study population 18 31 47 21 0 49 68 117 (N = 1392) (1. 3) (2. 2) (3. 4) (1. 5) (3. 5) (4. 9) (8. 4) CI-IRs, cetuximab induced hypersensitivity infusion reactions; N, number of patients. [41]osurveillance. 2019;24(11). doi:10. 2807/1560-7917. ES. 2019. 24. 11. 1800134[41]. b de ned as grade 1 or grade 2 infusion reactions. c de ned as grade 3 or grade 4 infusion reactions.
Temperature Monitoring During each trial, compost pile temperatures were recorded every 8 hours using HOBO data loggers (MX2303, Onset Inc. , Bourne, MA) with sensors positioned parallel to the dorsal region of each equine carcass at 46-and 91-cm depths ( Fig. 1  )  [27]. Two additional HOBO data loggers (8k Pendant, Onset Inc. , Bourne, MA) were positioned and set as previously described to be used as an alternate temperature recording. Temperature data was retrieved weekly to assess pile functionality.
The E3N cohort The E3N study is a large ongoing prospective cohort of women set up in France in 1990. The detailed protocol has been described elsewhere  (Clavel-Chapelon, 2015; Clavel-Chapelon et al. , 1997). Briefly, 98 995 women born between 1925 and 1950 and insured by the French national health insurance plan for people working for the national education system, the Mutuelle Générale de l'Education Nationale (MGEN) were included. Women were followed every-two or three years by selfadministered questionnaires. Over time, a good participation rate has been maintained (around 83%. The study was approved by the French National Commission for Data Protection and Privacy; all participants gave written informed consent.
Assessment of food consumption Dietary data were collected in 1993 using a previously validated semi-quantitative food frequency questionnaire including 208 food items  (Van Liere et al. , 1997). This questionnaire assessed the habitual diet of the previous year, collecting information concerning food and drink consumption for eight occasions (breakfast, morning snack, aperitif before lunch, lunch, afternoon snack, pre-dinner aperitif, dinner and after dinner snack). Information on vegetable oil consumption (in g/ day) was derived from this questionnaire and included peanut, sunflower, olive, corn and unspecified vegetable oil consumption. Participants' mean daily nutrient intakes, such as lipids intake (in g/day), were then estimated using a food composition table derived from the French food composition table of the French Information Center on Food Quality (CIQUAL).
Assessment of dietary exposure to PBDEs Food contamination data were obtained from the 2nd French Total Diet Study (TDS2) conducted by the French Agency for Food, Environmental and Occupational Health and Safety (ANSES) (French agency for food, environment and occupational health & safety, 2011a; French agency for food, environment and occupational health & safety, 2011b;  Sirot et al. , 2009). In short, a total of 20 280 different food products were collected between June 2007 and January 2009 in eight French regions of the metropolitan territory, reaching 1 352 composite samples of foods prepared as consumed. Eight PBDEs were measured in food corresponding to the main known or supposed sources of exposure. For the present study, PBDEs values below the limit of detection were replaced by 0 (lower-bound scenario) or by ½ of the limit of detection (middlebound scenario).
The E3N databases on food consumption and the ANSES database on food contaminants concentrations have been merged, as described in detail elsewhere  (Mancini et al. , 2020). For each participant, the daily mean dietary intake to each PBDE (ng/day) was obtained by multiplying the mean daily quantities consumed of each food component by the levels of contamination of the corresponding food components. Then, the total PBDEs dietary intake was obtained by summing the intake of each PBDE  (BDE 28, BDE 47, BDE 99, BDE 100, BDE 153, BDE 154, BDE 183 and BDE 209).
Identification of breast cancer cases and death Breast cancer cases were identified mainly through self-administered questionnaires sent every-two or three years. Additional cases were identified from next-of-kin spontaneous reports or through information from the national cause of death registry. Pathology reports or medical records were obtained for 93% of cases, allowing diagnosis validation. Non-validated cases were also retained, because the proportion of falsepositive self-reports was low in our study population (<5%). Cases were validated up to November 2014, which was therefore used as the date of end of follow-up in statistical analyses. Information on death was available from the MGEN health insurance database, from reports by family members, and from the national cause of death registry.
Discussion This study has highlighted a positive linear trend, borderline with statistical significance, between dietary intake of PBDEs and all breast cancer risk. A significant interaction between PBDEs dietary intake and vegetable oil consumption on breast cancer risk has been observed both in additive and multiplicative scale, with a larger effect size of PBDEs intake for high consumers of vegetable oil, and almost no effect in low consumers. The median exposure to PBDEs estimated in the present study (0. 67 ng/kg/day) was slightly higher than that reported by ANSES in the French TDS2 using to the lower-bound scenario (0. 54 ng/kg/day) (French agency for food, environment and occupational health & safety, 2011b). This latter study was based on the same food contamination levels as those used in the present study, but on consumption data derived from the second Individual and National Study on Food Consumption (INCA2), including 1918 adults from 18 to 79 years between 2005 and 2007.
6 years on average) has enabled to investigate long term health effects of PBDEs. In addition, prevalent breast cancer cases at baseline have been excluded, so that dietary consumptions have been assessed before any breast cancer diagnosis, preventing reverse causation bias. Furthermore, good quality data were used, the food frequency questionnaire being previously validated, and the breast cancer diagnosis being validated for 93% of cases. Finally, the richness of data of the E3N cohort has enabled to adjust for many covariates, which were selected using a DAG. The present study has highlighted for the first time a positive association between dietary exposure to PBDEs and breast cancer risk. Moreover, the results suggest an enhancer role played by vegetable oil consumption with regard to this association.
Background: The opioid use disorder is an international public health problem. Over the past 20 years it has been the subject of numerous publications concerning patients treated for chronic pain other than cancer-related. Patients with cancer-related pain are also at risk of opioid use disorder. The primary objective of this literature review was to determine the prevalence of opioid use disorder in patients with cancer-related chronic pain. Its secondary objective was to identify the characteristics of these opioid users. Methods: This is a literature review of studies published over the last twenty years, from 1 January 2000 to 31 December 2020 identified by searching the three main medical databases: Pubmed, Cochrane, and Embase. A meta-analysis took account of between and within-study variability with the use of random-effects models estimated by the DerSimonian and Laird method.
Search Strategy We searched three major medical databases: Pubmed, Cochrane, and Embase. We used the following keywords: chronic cancer-related pain (and synonymous terms: cancerassociated pain, tumor-associated pain, oncology pain), inappropriate drug use, abuse, overdose, addiction, non-medical use, opioid analgesics (and equivalents: full opioid agonists, partial opioid agonists, opioid agonist-antagonists). We specified three search equations (Supplementary Materials Table  S2 ) using the keywords, excluding non-cancer pain and non-opioid analgesics. We extracted articles dealing with the following: noncancer pain, acute pain with no mention of chronic pain, under-prescription of strong opioid analgesics owing to prescribers' fear of possible misuse, general practitioners' opinions on opioid prescribing, patients' opinions on opioid use, articles limited to urine tests to detect misuse, literature reviews, and documentary articles.
Selection Criteria We drew up a data collection table to record the information we needed to pursue our objectives: prevalence of observed of opioid use disorder and prevalence of risk of opioid use disorder, population characteristics: age, sex, marital status, social precariousness, type of cancer, stage of cancer, pain intensity, psychiatric history, history of addiction, history of sexual abuse, other prescribed treatments: benzodiazepines, other analgesics, type of use disorder, clinical impact, pain intensity, inpatient or outpatient management, prescribed substance, and route of administration. The prevalence of risk of opioid use disorder, not initially searched for, was finally recorded because it occurred frequently in the selected articles. Each article included in the analysis was read by two independent readers (CP and MB) who filled in data collection tables independently.
Statistical Analysis Statistical analysis was performed with Stata software (version 15 StataCorp, College Station, TX, USA). The prevalence of opioid use disorder (i. e. , effect-size noted ES) was estimated using the metaprop command of Stata software. This routine provides procedures for pooling proportions in a meta-analysis of multiple studies and displays the results in a forest plot. The confidence intervals (95% CI) are based on score (Wilson) or exact binomial (Clopper-Pearson) procedures. Our meta-analysis took account of betweenand within-study variability. To address the non-independence of data due to study effect, random-effects models were preferred to the usual statistical tests to assess the prevalence of opioid use disorder, using the method of DerSimonian and Laird  [16]  with the estimate of heterogeneity being taken from the inverse-variance fixed-effect model.
A test of whether the summary effect measure was equal to zero is given, together with a test for heterogeneity, i. e. , whether the true effect in all studies was the same. For stratified analyses by screening scale (CAGE, ORT, and SOAPP) and by sex, the analogous statistical approach was applied. The results are expressed as prevalence with 95% confidence intervals. Data were not sufficiently robust to conduct meta-regression analysis. Heterogeneity in the study results was also assessed by forest plots and the I 2 statistic, the most common metric for measuring the magnitude of between-study heterogeneity, and which is easily interpretable. I 2 values range between 0% and 100% and are typically considered low for 25%, moderate for 25-50%, and high for 50%  [17]. Publication bias was assessed by funnel plots and confidence intervals, and Egger's test.
Statistical Analysis Statistical analysis was performed with Stata software (version 15 StataCorp, College Station, TX, USA). The prevalence of opioid use disorder (i. e. , effect-size noted ES) was estimated using the metaprop command of Stata software. This routine provides procedures for pooling proportions in a meta-analysis of multiple studies and displays the results in a forest plot. The confidence intervals (95% CI) are based on score (Wilson) or exact binomial (Clopper-Pearson) procedures. Our meta-analysis took account of between-and within-study variability. To address the non-independence of data due to study effect, random-effects models were preferred to the usual statistical tests to assess the prevalence of opioid use disorder, using the method of DerSimonian and Laird  [16]  with the estimate of heterogeneity being taken from the inverse-variance fixed-effect model.
Discussion This systematic review and meta-analysis identified a total of 15 studies reporting rates of opioid use disorders among patients with cancer-related pain. Ours is the first literature review on the strong opioid use disorder to manage chronic cancer-related pain. We selected our articles from the three main medical databases (PubMed, Cochrane and Embase). Our inclusion and non-inclusion criteria were sufficiently precise to obtain a satisfactory percentage agreement between the reviewers (CP, MB and AG) (90%). Our analysis found the prevalence of opioid use disorder in patients with cancer-related chronic pain to be 8% (1-20%). However, the very high statistical heterogeneity I 2 = 97. 8% rules out any conclusive interpretation. Although prevalence of the risk of use disorder was not initially searched for, it was found as a data item in the studies.
It was 23. 5% (19. 5-27. 8%) (I 2 = 88. 7%). Certain limitations hinder the interpretation of our results. First, we chose a broad definition of opioid use disorder that included all the terms used and so increased the number of studies selected at the expense of their homogeneity. We found several terms in the literature that could meet the definition of use disorder  [11]. The definition used varied according to the article. Some authors consider only abuse  [18] , misuse  [31]  or 'chemical coping'  [22]. Others take a broader definition integrating misuse, aberrant behaviors linked to strong opioid analgesics  [20]  and even addiction and dependence  [21]. How data were collected varied among studies. In retrospective studies, records were used (Table  1 ), often leading to a marked bias because records were incomplete.
Second, there is a paucity of data for analysis, in part because we did not include articles in which the type of pain was not specified, and studies based on urine tests for toxicants. These qualitative tests are counted positive if they detect the presence of other substances such as alcohol or benzodiazepines. These tests are carried out at the request of physicians, especially when there is doubt about of opioid use disorder. We concluded that there was too great a risk of overestimating the prevalence of opioid use disorder. We hoped to identify the characteristics of opioid disorder users, but again lacked data. Certain characteristics (such as a history of addiction, psychiatric disorders, and sexual abuse) were absent in the studies that found opioid use disorder. They were present in the studies that assessed the risk of opioid analgesics use disorder, because they were obtained from the screening questionnaires.
However, this is not necessarily simpler in the Eulerian framework, where most studies classify plastics by their buoyancy instead of by their size, as for instance  Mountford and Morales Maqueda (2019). So far, most Lagrangian studies have focused on the floating plastics-the tip of the iceberg-, for which a global observational dataset is becoming available  (Cózar et al. , 2014)  and allows some validation. However, the behavior of very small microplastics, and even more the nanoplastics, is much less affected by buoyancy because of their large surface/volume ratio  (Poulain et al. , 2019). Other types of plastic debris like fibers, that are extremely abundant in waste waters, also behave as neutral particles in the ocean, and are advected by the three dimensional currents  (Gago et al.
The Ariane Lagrangian Analysis Software The Ariane software  (Blanke and Raynaud, 1997)  allows the exact calculation of 3D trajectories of numerical particles in stationary and non-divergent transport fields, defined on a C-grid  (Arakawa and Lamb, 1977). The starting positions, in space and time, of the particles are defined by the user. Then the application sequentially reads the current fields of a global ocean circulation model to advect the particles and thus it computes their trajectories. The Ariane application takes advantage of the properties of the currents discretization on a C-grid to analytically compute the particle trajectories. Between two updates of the velocity field (here at daily frequency), velocities are assumed to be stationary, such that the algorithm computes the actual trajectories through the exact calculation of the three-dimensional streamlines.
, 2019; Pattiaratchi et al. , 2022). These data were downloaded from the global model inputs for monthly midpoint estimates  (Lebreton and Reisser, 2018). A first step was to convert the mass input into a number of particles, such that a total of about 10 million particles were released annually (all particles represent the same amount of plastic). The rounding to an integer number of particles released each month at source points reduces the number of effective sources to 5,676 (compared to 40,760), still representing 99. 9% of the total input. Particles were released at the nearest ORCA 1/12 °coastal ocean grid cell of each catchment, based on its latitude and longitude provided in the input data. Their initial positions were determined randomly within the coastal ocean grid cell, such that each trajectory is unique.
DISCUSSION AND CONCLUSION We have investigated herein the fate of particles released continuously at the coast according to river plastic emissions, assuming their density is equal to the density of seawater (i. e. , neutral buoyancy). About 200 million particles were released in total over a 20 years long simulation, at the monthly rate of 327,014-1,619,932 particles according to the seasonal cycle. The 5,676 effective release points (river mouths), i. e. , emitting more than one particle per year, represented 99. 9% of the total input estimated by  Lebreton et al. (2017). The particles trajectories are computed according to daily currents of a state-ofthe-art global ocean model at 1/12 °resolution over the period 1991-2010 using the Lagrangian software Ariane. The 3D dispersion of these particles shows very non-isotropic features shaped by the ocean currents, in close connection with the processes of ocean ventilation and water masses formation.
FUNDING This work was supported by  ISblue  project , Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ), and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ". This work was granted access to the HPC resources of the french supercomputing center IDRIS under the allocations  2021-AP010113079  for the Ariane experiments and  A0090101279  for the NEMO eORCA12 simulations made by  GENCI.
Direct searches for H → inv decays have already been performed by the ATLAS  [20, 21]  and CMS  [22] [23] [24]  collaborations using data collected at ffiffi ffi s p ¼ 7, 8, and  13  TeV and combining the three main Higgs boson production modes, namely gluon-gluon fusion (ggH), production of a Higgs boson in association with vector bosons (VH, with V ¼ W AE or Z), and vector boson fusion (VBF). Assuming SM production of the Higgs boson, the best observed (expected) 95% confidence level (C. L. ) upper limits on BðH → invÞ are set at 0. 19 (0. 15) by CMS, using data collected at ffiffi ffi s p ¼ 7, 8, and  13  TeV, and at 0. 26 (0. 17) by ATLAS, using data collected at 13 TeV. In both cases, the data at 13 TeV were collected in 2016. Combining the latest CMS constraints on both visible and invisible decays within the κ framework  [25] , the upper bound on BðH → invÞ is 0.
22 at the 95% C. L. , using only the dataset collected at  13  TeV in 2016. Thanks to its large production cross section  [26]  and distinctive event topology, the VBF production mechanism drives the overall sensitivity in the direct search for invisible decays of the Higgs boson. This paper focuses exclusively on the search for H → inv in the VBF production mode using the LHC proton-proton (pp) collision dataset collected during 2017-2018, corresponding to an integrated luminosity of up to 101 fb -1 , and on the combination of this search with analyses performed on previous datasets  [22, 27]. Employing a strategy similar to the one used in the previously published analysis  [22] , the invariant mass of the jet pair produced by VBF, m jj , is used as a discriminating variable to separate the signal and the dominant backgrounds arising from vector boson production in association with two jets (V þ jets).
III. DATA AND SIMULATED SAMPLES Data were recorded by several triggers, as detailed in Sec. VA, during 2017 and 2018, for maximum integrated luminosities corresponding to 41. 5 and 59. 8 fb -1 , respectively. The signal and background processes are simulated using similar Monte Carlo (MC) generator configurations as described in detail in Ref. [22]  and summarized below. Separate independent samples were produced for each datataking year. The same generator settings were used for the 2017 and 2018 samples. The Higgs boson signal events, produced through ggH, VBF, VH and in association with top quarks (ttH), are generated with POWHEG2. 0  [32] [33] [34] [35] [36]  at next-to-leading-order (NLO) approximation in perturbative quantum chromodynamics (pQCD). The signal yields are normalized to the inclusive Higgs boson production cross sections, calculated in Ref.
Any correction applied to individual objects is propagated correspondingly to the p miss T  [60]. Specific event filters have been designed to reduce the contamination arising from large misreconstructed p miss T from noncollision backgrounds  [60]. For the analysis of the 2018 data, the missing HE section affects the PF reconstruction. The inability to distinguish electrons and photons from jets leads to spurious p miss T in the corresponding ϕ region as a result of the suboptimal reconstruction of charged and neutral hadrons. Consequently, events with -1. 8 < ϕðp miss T Þ < -0. 6 are rejected in data for the part of this dataset, around 65% of the total, that is affected. Simulated events in this region are reweighted accordingly to model the efficiency loss. It has been observed during data taking that the HF detector can, on rare occasions, give rise to unphysical high-energy signals.
The data collected by these triggers in 2017 and 2018 correspond to integrated luminosities of 41. 5 and 59. 8 fb -1 , respectively. The second category uses a combination of p miss T and jet information and is referred to as the "VBF jets triggered region" (VTR) category in the following. After the upgrade of the L1 trigger system  [65] , it was possible to develop an algorithm targeting jets originating from VBF production. This trigger was added after the start of data taking in 2017 and collected a dataset corresponding to an integrated luminosity of 36. 7 fb -1 during that year. The L1 VBF algorithm requires the presence of at least two jets passing p L1 T thresholds of 115 (110) GeV for the leading jet and 40  (35)  GeV for the subleading jet in 2017 (2018). Pairs are formed from the selected jets, and the pair with the highest invariant mass m L1 jj must satisfy m L1 jj > 620 GeV.