dataesr/scientific-paragraphs-categorization huggingface dataset:
Matthews - Nuffield Department of Medicine ,  University of Oxford OX3 9DU ,  United Kingdom Nicole Stoesser -Nuffield Department of Medicine  and  NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance , in partnership with  Public Health England ,  University of Oxford ,  Oxford OX3 9DU ,  United Kingdom   Derrick W. Crook   -Nuffield Department of Medicine ,  NIHR   Oxford Biomedical Research Centre , and  NIHR Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance , in partnership with  Public Health England ,  University of Oxford , Oxford OX3 9DU, United Kingdom Complete contact information is available at: https://pubs. acs. org/10. 1021/acsnano. 2c10159 Funding This research was supported by a  Royal Society Dorothy Hodgkin   Research Fellowship   DKR00620  and  Research Grant for Research Fellows   RGF\R1\180054  (N.
Statistical methods Data are presented as median and 95% confidence interval (CI). Changes in hormone concentrations over time were analysed by repeat measures ANOVA with Bonferroni correction for multiple comparisons. Receiver-operator characteristic (ROC) curve analyses were performed, reporting area under the curve (AUROC). Univariate analysis investigated simple relationships between detectable and undetectable AMH as binary category and later ovarian function as a binary category defined by a threshold level. Multivariate analysis was also performed to assess the predictive performance of baseline and treatment characteristics (endocrine and non-endocrine) and post-chemotherapy endocrine factors in terms of later ovarian function. Multivariate analysis was performed in three stages. First, individual variables were assessed for prediction of undetectable AMH at 30 months' post-treatment.
Second, suitable candidate variables from the first stage were used in multivariate linear regression models (PRISM version 9, GraphPad Software LLC, San Diego USA) to provide estimates of AUROC, PPV and NPV. Third, and to guard against the multivariate linear regression models over-or underfitting the data (i. e. supplying estimates that are unlikely to generalize to new data instances) a full machine learning workflow was performed using scikit-learn version 0. 46 within Python version 3. 9. 2. The workflow stages were: shuffling and splitting data into 70% train and 30% validation subsets; fivefold cross-validated grid search of 420 options for optimal hyperparameters for the random forest algorithm applied to the test data; cross-validated application of the optimal model on the training data; application of the model to the validation data subset that mimics new data instances.
2a ). AUROC for estradiol at 30 months by undetectable AMH at 6 months was 0. 75 (95% CI 0. 67-0. 82, P < 0. 0001; Fig. 2c ), with sensitivity 19. 7% and specificity 95. 1% at estradiol concentration of 34. 4 pmol/L, at which likelihood ratio peaked at 4. 0. The positive predictive value of undetectable AMH at 6 months for a menopausal estradiol level (< 110 pmol/L [30]) at 30 months was 0. 77. Supporting this, AMH at 6 months for prediction of undetectable AMH at 30 months was explored. AUROC was 0. 76 (CI 0. 68-0. 83, P < 0. 0001), with PPV of undetectable AMH at 6 months for unpredictable AMH at 30 months of 0. 78. As both FSH and estradiol may be impacted by tamoxifen treatment, data were additionally analysed separately in the 48 women not taking tamoxifen. AMH was undetectable in 29 (60%) of these women at 6 months, and also in 29 (60%) women at 30 months.
Methods Our survey method did not aim to collect a representative sample of the population's opinions, but to identify ecologically sound views, which could be in the minority. We conducted 31 semistructured interviews with 29 people involved as experts or spokespersons in the governance of biodiversity in the area under study and working in government departments (4), for the canal company or the Voies Navigables de France public agency (5), in chambers of commerce, industry or agriculture (2), at local authorities (  9 ) and for non-profit associations (10). The four sociologists of the project used the same interview guide. We used the Nvivo software to analyze and code contents. The first interviews served as a trial for a reflexive analysis to eliminate bias between interviewers. We collected and analyzed public studies and documents related to the project (legal environmental assessment submitted to reviews, opinions of the authorities, public inquiry report) and to its environment (State map of issues, environmental planning documents).
From the European newspaper database Europresse, we identified 91 published articles which mentioned concrete characteristics of the canal project that were under debate, and we kept the arguments related to ecological issues mentioned in 10 of them. These written documents allowed us to identify an initial list of environmental ontologies (forms and motives) that exist in the project's social environment. The list served as a reference to ensure that we did not forget any elements in the interviews. We used the Nvivo software to identify and group together the environmental motives  (Bouleau 2019 ) cited in the interviews, i. e,. environmental ontologies associated with motivations (fears, expectations, interests, values). These categories of analysis were constructed incrementally and iteratively, according to three main inputs: the biophysical functioning of these environmental objects, their politicization, and the policy instruments used to govern them  (Howlett 1991).
Formal thought disorder (FTD) is a multidimensional syndrome mainly occurring along the psychosis continuum. Cannabis use is known to increase symptoms of psychosis, particularly positive symptoms. However, the impact of cannabis use on FTD in individuals presenting symptoms along the psychosis continuum remains unclear. To address this knowledge gap, we conducted a meta-analysis examining the association between cannabis use and FTD in those individuals. We hypothesized that cannabis would worsen FTD. We conducted a systematic search of the PubMed, ScienceDirect, PsycINFO, Web of Science, Embase and Google Scholar databases up to July 2022. The results were collated through a random-effects model using the statistical software R. Reference lists of included studies were searched for additional relevant publications.
Search strategy PubMed, ScienceDirect, Web of Science, PsycINFO, Embase and Google Scholar were used as sources to retrieve publications. The search syntax was as follows: ["thought disorder" OR "thought disturbance" OR "cognitive disorganization" OR "formal thought disorder" OR "disorganized speech" AND "cannabis" OR "marijuana" OR "THC" OR "pot" OR "hashish" OR "bhang" OR "ganja"] (see supplementary materials "SearchSyntax" for details). A filter was applied on ScienceDirect to only retrieve "research articles". In addition, only the first 200 relevant sources were retrieved from Google Scholar, as recommended  22. Each database supports spelling variations, such as the terms "disorganization" and "disorganized" were automatically searched with British and American spelling. Each source was last searched in July 2022.
Data extraction Data used for statistical analyses were extracted from the Results section or the supplementary material part of selected studies. To allow for a comparison between groups, data had to be extracted into two groups: one group of participants using cannabis and one of participants not using cannabis. In addition, both extracted groups of participants had to present symptoms pertaining to the same stage of the psychosis continuum (i. e. patients with schizophrenia and not using cannabis compared to patients with schizophrenia using cannabis). In cases where study designs included participants pertaining to multiple groups in terms of their drug use (i. e. polysubstance users, cannabis mainly users, alcohol mainly users, non-users), only the relevant groups were extracted (i. e.
cannabis mainly users and non-users). Similarly, in the event that multiple groups of participants were formed in terms of the disorder severity (i. e. first-episode psychosis compared to healthy controls), only data of the relevant group  was extracted (i. e. first-episode psychosis using and not using cannabis). In cases where insufficient data was present for the calculation of our primary effect size, the principal investigators were contacted via email  13, [23] [24] [25]. Two of the contacted authors provided the requested data  13, 23.
Meta-analysis methods Analyses were conducted using the statistical software R version 4. 1. 1 (R Core Team 2021)  28. Mean scores, standard deviations and binary outcomes (FTD present or absent) of each study were extracted for cannabis users and for non-users; they were subsequently converted into between-groups Standardized Mean Differences (SMD) using the {esc} package, so that the results obtained from different scales could be pooled together. In cases where studies reported the result of a two-sample t-test  29  , the t-statistic was converted into a SMD. Hedge's g correction was applied to all SMDs to correct for small-sample size. In cases where studies categorized groups into types of cannabis used (i. e. skunk, herbal, mixed)  30  , or level of use (i. e. use, harmful use, Substance Use Disorder)  13  , we regrouped these cannabis users into a single group for each study by calculating their mean scores and standard deviations.
RESULTS The flow diagram of the study selection process is provided in Fig. 1. Searches yielded 1822 results, which were then refined by the screening of titles and abstracts. After the screening process, 201 publications were assessed for eligibility based on a full-text reading, leading to the selection of 21 relevant studies. Details regarding the reasons for study exclusion are provided in Fig. 1. We finally included 19 publications in the meta-analysis because data extraction could not be performed for two studies  24, 25. Across the 19 studies, a total of 5191 participants were included: 1840 in the cannabis-users group and 3351 in the nonusers group.
Serum triglycerides and cholesterol. Serum total triglyceride and cholesterol levels were determined using Infinity reagents (TR22421 and TR13421, Thermo Fisher Scientific), according to the manufacturer's instructions. Lesion analyses in the aortic root. Serial 10-μm cryosections were cut distally from the aortic root. The sections (0, 200, 400, 600 and 800 µm after the appearance of the aortic cusps) were stained with lipid-reactive Oil Red O and counterstained with hematoxylin. The sections were then evaluated by a blinded observer. We used morphometric analysis (BioPix Software) to determine the area of the atherosclerotic lesions and length of the external elastic lamina (EEL) after manual delineation. As an estimate of atherosclerotic lesion size, atherosclerotic lesion areas were normalized to EEL length and these values were integrated to quantify the total plaque burden.
Acknowledgements The authors thank  Annelie Carlsson  for excellent research assistance. This work was supported by the  Swedish Research Council  (Grant  2021-01856  to Å. T. ), the  Swedish Heart-Lung Foundation  (Grant  20210492  to Å. T. ), the  Novo Nordisk Foundation  (Grant  NNF18OC0034464  to Å. T. ) and Grants from the  Swedish state  under the agreement between the Swedish government and the county councils (the ALF-agreement;  ALFGBG-721451  to Å. T. ).
Data collected All data was collected during a face-to-face interview and review of medical records in a single clinical visit conducted by a trained investigator: • demographics included date of birth, sex, smoking status, weight, height, socio-educational level; • disease-specific information included SpA characteristics, family history, date of diagnosis, date of symptom onset, treatment history; • presence of enthesitis was established by a 'yes' answer to 'Do you consider that this patient has ever suffered from at least one episode of Spondyloarthritis-related enthesitis?'. The locations of first and subsequent sites of enthesitis were collected. Confirmation of enthesitis by imaging was not required. Data regarding previous imaging to confirm enthesitis was collected. Data regarding specific treatment for enthesitis was also collected.
Logistic regression was performed. Separate models were developed with EFM as the dependent variable using (1) FM by FiRST criteria and (2) clinical diagnosis of FM. Variables with P-value < 0. 1 in univariable analysis were retained for each model. Age, sex and exposure to biologics were considered clinically relevant variables and controlled for in every model. Odds ratios (OR) with confidence intervals (CI), adjusted for the presence of all variables in the model, are presented. Sensitivity analysis was additionally performed using the variable 'enthesitis present on exam' in place of 'enthesitis ever'. A P-value of < 0. 05 was considered statistically significant. IBM SPSS Statistics Version 26 was used for statistical analysis.
UV Absorption spectroscopy UV experiments were performed using a Thermo Scientific TM Evolution TM201 UV-Vis spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and recorded between 220-340 nm. Spectra acquisition was conducted with a 600 nm/min scanning rate, 1 nm data intervals, and 0. 05 s of integration time. The rG4-let-7e sequence was diluted to 5 μM in lithium cacodylate buffer (10 mM, pH = 7. 2). Thermal difference spectra (TDS) were carried out at 95 °C and 25 °C, corresponding to the unfolded and folded states, respectively. The TDS spectrum was calculated by subtracting the 25 °C spectra from the obtained at 95 °C. The difference spectrum was normalized relative to the maximum absorbance. The experiment was performed in 20 mM phosphate buffer pH 7. 1 supplemented with 100 mM KCl.
Circular dichroism spectroscopy CD spectra were acquired in a Jasco J-815 spectrometer (Jasco, USA), using a Peltier temperature controller (model CDF-426S/15). rG4-let-7e sequence was annealed as previously described. A 1 mm path-length quartz cuvette (Hellma, Germany) was used with rG4-let-7e at 10 µM in 10 mM lithium cacodylate buffer (Sigma-Aldrich, USA) at pH 7. 2, supplemented with KCl (Thermo Fisher Scientific, USA). The required volume for the titrations was added directly to the quartz cell. The CD melting experiments were performed in the temperature range of 20-100 °C, with a heating rate of 2 °C/min by monitoring the ellipticity at 262 nm. Spectra acquisition was performed in the absence and presence of increasing concentrations of KCl or ligands. Data was converted into fraction folded (f) plots.
The biotin-labeled rG4-let-7e (25 nM) was immobilized at 1 µL/min until it reached approximately 250 RU. For kinetic/affinity analysis, each ligand was serially diluted in running buffer, ranging the concentration from 1 nM to 1 µM. All experiments were performed in triplicate at 25 °C. Each ligand was injected from low to high concentrations during 75 s with a flow rate of 50 µL/min, followed by dissociation of 600 s. Surface regeneration was achieved by injecting two pulses of 30 s of 10 mM glycine/HCl pH 1. 5, and the next three 60 s injections of running buffer to remove any trace of regeneration solution. BiaEvaluation Software was used for data analysis and the likelihood of fittings was assessed through the statistical parameters of Chi2 and U-value. All sensorgrams were double corrected for non-specific binding and refractive index changes (bulk effect) by subtracting the signals of an equivalent injection across the reference flow cell 1.
Thereafter, cells were washed 3 × with fresh serum-free medium and incubated with rG4-let-7e labelled at the 5' terminus with Cyanine-3. 5 (Cy3. 5) (1 μM) for 1 h. Then, cells were washed 3 × with fresh serum-free medium and stained with Hoechst 33342 ® nuclear probe (Thermo Scientific, USA; 2 μM) for 15 min. Then, cells were imaged using a Zeiss LSM 710 confocal laser scanning microscope (Carl Zeiss, Germany) and processed using the blue edition of ZEN 2012 software (Carl Zeiss, Germany).
Acknowledgements This work was supported by  PESSOA program ref. 5079 and project  " Projeto de Investigação Exploratória " ref. IF/00959/2015 entitled "NCL targeting by G-quadruplex aptamers for cervical cancer therapy" financed by  Fundo Social Europeu e Programa Operacional Potencial Humano. This work benefited from access to the Cell-Free platform of the Grenoble Instruct-ERIC center (ISBG;  UMS 3518 CNRS-CEA-UGA-EMBL ), an Instruct-ERIC centre, within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and GRAL, financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ). Financial support was provided by  Instruct-ERIC  (PID:  10168  "Production of the full-length nucleolin for structural studies").
Thanks are due to  FCT/MCT  for the financial support to  CICS-UBI UIDB/00709/2020  research unit,  POCI-01-0145-FEDER-022122  research unit  PPBI-Portuguese Platform of BioImaging , and to the  Portuguese NMR Network  ( ROTEIRO/0031/2013-PINFRA/22161/2016 ), through national funds and, where applicable, co-financed by the  FEDER  through  COMPETE 2020, POCI, PORL  and  PIDDAC. Tiago Santos acknowledges Fundação para a Ciência e Tecnologia (FCT)  for the doctoral fellowship  PD/BD/142851/2018  integrated in the Ph. D. Programme in NMR applied to chemistry, materials and biosciences ( PD/00065/2013 ) co-financed by  Fundo Social Europeu. André Miranda  acknowledges to the research fellowship " Rede Nacional de Ressonância Magnética Nuclear "  ref. PINFRA/22161/2016-B4  funded by " Programa Operacional Competitividade  e.
Statistical Analysis Qualitative data were described in terms of numbers and percentages, and quantitative data were described in terms of the mean and standard deviation. Respondent characteristics were compared by the major tests cited using Student's t-tests for quantitative data and Fisher's exact tests for qualitative data, followed by logistic regressions. Statistical analyses were performed with SAS software (version 9. 4, SAS Inst. , Cary, NC, USA). The significance level was set at 5%.
| Measurement A combined MRI of liver and heart was performed at yearly intervals according to current clinical practice. In brief, the protocol consisted of dedicated liver and electrocardiographically triggered cardiac T2*weighted sequences to assess liver and myocardial iron concentrations. All images were acquired on the same commercially available 1. 5 Tesla MR imager using a predefined and previously validated imaging protocol. A detailed description of the MRI protocol, including a thorough description of the R2* method is available in the review paper by Henninger et al. 33  and on https://imagemed. univ-rennes1. fr/. Upon completion of the acquisition, images were sent to a dedicated workstation for further analysis using the MRQuantif web application. MRQuantif is a dedicated software package that can be used to quantify LIC from T2* MRI acquisitions.
| Baseline characteristics of the patients From March 2018 through April 2019, 30 patients were enrolled (Figure  1 ). Four treatment periods were excluded based on transfusion requirements (more than four units in one treatment period). The intention-to-treat population included all participants despite those excluded based on the set transfusion threshold. A total of six patients dropped out of the study at patient request of whom three completed the first treatment. Data assembled until dropout were included in the intention-to-treat analysis. Overall, data of 29 patients were included in the intention-to-treat analysis. As a consequence of drop-out, and one MRI not obtained according trial protocol, the analysis included 47 completed years of treatment (24 esomepazole treatments and 23 placebo treatments).
The intention-totreat population compromised 30 patients who underwent randomization, to receive either esomeprazole followed by placebo, or placebo followed by esomeprazole. *The two treatment periods referred to were the two treatment phases of one patient, one placebo period, and one esomeprazole period. † One placebo period and one esomeprazole period were excluded. Tx based on transfusion requirements. T A B L E 1 Baseline demographic and disease characteristics. Data on all baseline demographics and disease characteristics are shown for all patients who underwent randomization. Characteristics are tabulated against treatment allocation. Iron intake was quantified with the IRONIC-FFQ questionnaire at baseline and after each treatment period. Changes in iron (heme or non-heme) intake were negligible over time (resp.
Neutralizing antibody titers measured on Day 0 pre-vaccination and Day 28-post vaccination show that there is a clear and significant difference between pre-vaccination antibody levels and vaccine-induced antibody levels (P<0. 0001, P<0. 0001, P = 0. 0078). This suggests that the increased antibody titers were in fact, the result of immunization rather than pre-existing levels of nAb at pre-vaccination (Fig 2M  , 2N,  and 2O ). Together, this data suggests that this vaccine candidate triggers a robust serotype specific immune response in which seropositive individuals generate significantly high neutralizing antibody titers against the serotype that they were previously exposed to.
Ethics statement This study was approved by the local ethics committee of the Medical School of the University of São Paulo, the Brazilian Research National Ethics Council, the Brazilian National Technical Biosafety Commission, and the Brazilian Health Regulatory Agency. The study was done in accordance with the Declaration of Helsinki, International Conference of Harmonization guidelines, and Good Clinical Practice guidelines. All patients provided written informed consent. An independent data safety monitoring board oversaw the study. This trial is registered with ClinicalTrials. gov, NCT01696422.
Cytokine and chemokine analysis Donor plasma was analyzed for chemokine/cytokine levels using ProcartaPlex (ThermoFisher Scientific). The following human premixed chemokine/cytokine panels was used: Cortisol, BLC, CD30L, CD40-L, C-TACK, D-Dimer, EDA-1, ENA78, Eotaxin, Eotaxin-2, FGF-23, Fibrinogen, GDF-15, Ghrelin, Granzyme B, HGF, HVEM, IL-1beta, IL-16, IL-18, IL-33, IL-37, IP-10, MCP1, MDC/CCL22, MIF, MMP-1, Nectin-2 (CD112), NT5E (CD73), OPG, PD-1, Perforin, sFas-Ligand, TIM-3, TNFRI, TNFRII, Trail-R1, TSLP, Tweak, ULBP-1, ULBP-3, and VEGF-R3. The manufacturer's protocol was followed. Data were acquired on a Bio-Plex 200 System (using bead regions defined in thermo fisher Scientific protocol) and analyzed with the Bio-Plex Manager 6. 1 software from Bio-Rad or a Lumine FLEXMAP 3D System using bead regions defined in the protocol and analyzed using Belysa Curve Fitting Software (Sigma Aldrich).
t-distributed stochastic neighbor embedding (t-SNE) Flow cytometry Individual FCS files generated by the BD FACS Diva software were imported into FlowJo Software for pre-gating on CD4+ live cells for the T cell panel and live cell cells for the innate panel. Selected events were then exported: from which an identical number of events per participant were then randomly subsampled into R. For bioinformatic analysis of flow cytometry data, a custom script was made for unsupervised clustering of cells based on similarity of their marker expression. Batch correction was applied to account of variations across experiments, using the following methodology: a spline-model was built for each fluorescent marker on the quantile transformed data of a selected representative batch (oe experiment). These models were then used to correct fluorescence values of quantile transformed expression for other batches.