dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding The Dementia Research Centre is supported by  Alzheimer's Research UK, Alzheimer's Society, Brain Research UK , and The  Wolfson Foundation. This work was supported by the  National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Unit  and the  University College London Hospitals Biomedical Research Centre , the  Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility , and the  UK Dementia Research Institute , which receives its funding from  UK DRI Ltd , funded by the  UK Medical Research Council (MRC) ,  Alzheimer's Society and Alzheimer's Research UK. This work was also supported by the  Medical Research Council (MRC) UK GENFI  grant ( MR/M023664/ 1 ), the  Italian Ministry of Health  ( CoEN015  and  Ricerca Corrente ), the  Canadian Institutes of Health Research  as part of a  Centres of Excellence in Neurodegeneration grant , a  Canadian Institutes of Health   Research operating grant , the  Alzheimer's Society  grant ( AS-PG-16-007 ), the  Bluefield  Project and the  EU   Joint Programme-Neurodegenerative Disease Research (JPND) GENFI-PROX  grant ( 2019-02248 ).
MB is supported by a  Fellowship award  from the  Alzheimer's Society, UK  ( AS-JF-19a-004-517 ). MB's work was also supported by the  UK Dementia Research Institute  which receives its funding from  DRI Ltd , funded by the  UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. MB acknowledges the support of  NVIDIA Corporation  with the donation of the Titan V GPU used for part of the analyses in this research. JDR is supported by the  Miriam Marks Brain Research UK Senior  Fellowship and has received funding from an  Medical Research Council (MRC)   Clinician Scientist Fellowship  ( MR/M008525/1 ) and the  NIHR Rare Disease Translational Research Collaboration  ( BRC149/NS/MH ). JEI is supported by the  European Research Council  (Starting Grant  677697 , project  BUNGEE-TOOLS ),  Alzheimer's Research UK  ( ARUK-IRG2019A003 ) and the  NIH  ( 1RF1MH123195-01  and  1R01AG070988-01 ).
JBR is funded by the  Wellcome Trust  ( 103838 ) and the  National Institute for Health Research Cambridge Biomedical Research Centre. This work was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's  Excellence Strategy  within the framework of the  Munich Cluster for Systems Neurology  ( EXC 2145 SyNergy-ID 390857198 ). RV's work is supported by the  Mady Browaeys Fonds voor Onderzoek naar Frontotemporale Degeneratie. Several authors of this publication (JCvS, MS, RSV, AD, MO, RV, JDR) are members of the  European Reference Network for Rare Neurological Diseases (ERN-RND )-Project  ID No 739510.
Recruitment and sampling procedures The recruitment process has already been described in detail  [7, 8]. In brief, eligible participants were adult daily smokers who lived in Switzerland or France, had decided to quit smoking, and had set a quit date within one month before enrolment in the study. Participants were recruited in 2019-2020 via advertisements on the internet and registered online. After downloading the app for free from the Apple App store or Google Play store, they clicked on a link that led to an online consent form and an online screening and baseline questionnaire. Eligible participants were then randomly assigned to receiving either a code to unlock a full version of the Stop Tabac smoking cessation app or a code to unlock a control version of the app that contained only a few features (both versions were in French).
The eligibility assessment was automatic. The full version of the Stop-tabac app includes information pages, calculators (number of cigarettes not smoked, days of life gained and money saved since quitting smoking), customised (automated) counselling reports, a quiz, a discussion forum, telephone numbers of quitlines, and an interactive module on nicotine replacement therapy and e-cigarettes. The app was a stand-alone intervention and there was no human involvement in this smoking cessation intervention. The full version of the Stop-tabac app is available for free from the Apple App store and the Google Play store. The control version contained the above calculators and 5 pages of 100-300 words each on the addictive potential of nicotine, reasons to quit and money savings.
The dataset for analysis was anonymized, the only person who could access the code linking participants' names and their responses to questionnaires was a computer expert under the supervision of the first author. There was no risk associated with the app itself and with the data collection procedure. We used appropriate security measures to protect the data. Participants in the control group were able to access to full Stop-Tabac app after the end of data collection. Results were published  [7]. The original data set (anonymized) is available publicly at Yareta. unige. ch.
The more accuracy suffers, the more important the variable is for successful prediction (Table  2 ). To answer question 2 (i. e. relationship between each predictor and smoking cessation and association between each covariate and smoking cessation), we built a multinomial logistic regression model using SPSS software (version 28. 0) (Table  3 ). To answer question 3 (i. e. predictors of change in cigarette count/day), we built a machine learning regression model using the RF algorithm  [11, 12] , with the following setting parameters: "ntree" = 500, meaning that each random forests model was constructed from 500 regression trees, and we left "mtry" -the number of predictors available for allocation at each tree node -at the default value of one third of the total number of predictors. RF regression models provide, among other results, the significance of each predictor based on a measure called %IncMSE (percentage increase in mean squared error).
We therefore applied generalised additive modelling (GAM)  [13] [14] [15]  using the "mgcv" package for R. We then selected the best model based on the R 2 and AIC metrics. The selected model had the following tuning parameters: function = gam, method = REML, family = exponential, k = 3, select = TRUE, optimizer = newton. In addition to the GAM model (Table  5 ), we ran an ANOVA test (using SPSS software version 28. 0) after removing a dozen outliers from the dataset and normalising the outcome variable (Table  6 ). To answer question 5 (i. e. predictors of smoking relapse), we built a machine learning model using the RF classification algorithm  [11, 12]  (Table  7 ). To answer question 6 (i. e. relationship between each predictor and relapse to smoking and association between each covariate and smoking recidivism), webuilt  a multinomial logistic regression model using SPSS version 28.
0 (Table  8 ). Some of follow-up measures had missing values. For all random forest analyses, the missing values were handled with the "rfImpute()" function of the RF algorithm, which uses a nearest-neighbour machine learning approach to either impute values or weight their absence  [11, 12]. The dataset used for the regression analysis and for ANOVA had the missing data imputed also by the "rfImpute()" RF function. In addition, for each analysis, we controlled whether imputation had an impact on the analysis results, which it did not. Finally, we used the RF machine learning algorithm instead of traditional methods because this algorithm is one of the most efficient tools for imputing missing data, has parameters that can be adjusted to improve specific performance metrics, and thus creates a model that best fits the data  [11, 12].
Limitations and strengths As this study was not originally intended for the present secondary data analysis, the dataset included only a limited number of predictor variables, which limited our ability to predict smoking cessation, reduction and relapse. The response rate at 6-month follow-up was low (27%), and respondents to the 6-month survey differed from non-respondents (respondents were older, less depressed, they used the app more frequently), and this may limit he generalizability of our results. The low response rate at follow-up can possibly be explained by the fact that the participants had never met the researchers personally and therefore may not have felt connected to them. Strengths of the study include a large sample of people of all ages who lived in all departments of France and all cantons of French-speaking Switzerland, and the collection of data on app use from 100% of participants.
Acknowledgements This study was supported by the  Swiss National Science Foundation , grant  32 003-179 369 ,  CHF 194942  ( EUR 182200 ,  USD 200700 ). J. F. E. 's salary is paid by the  University of Geneva ; Y. K. 's salary is paid by the  Lausanne University Hospitals  G. V. C's salary is paid by the  University of Picardie. Vincent Baujard  developed the software for data collection and managed and maintained the on-line data collection system. Evelyne Laszlo  supervised the development of both versions of the application (Stop-tabac and control) and moderated the discussion forum 'The Tribe'.
In this latest review, the authors summarize and compare the various 2D and 3D biomaterial systems that have been applied to liver differentiation, highlighting their roles in presenting biochemical and physical cues to different stem cell sources. Contributions to this Special Issue take readers on a journey into topical research activities in the specific area of interactions between cells and biomaterials, covering new and different aspects, within different tissues and cells, as well as during tumor progression processes. As guest editor for this Special Issue, I am optimistic on the fact that this specific area of research will again spark inspiration and ideas for further research and development in the field. In this way, more data will be collected, highlighting significant aspects that can be used in therapy, and at the same time improving the application of these advanced methods in terms of economy and quality.
e. , presence or absence of virus in saliva as determined by inoculation and subsequent testing of recipient mosquitoes) and fit using the shape-constrained additive model (scam) package  [41]. In this, the response variable was thus 0 (virus-negative saliva) or 1 (virus-positive saliva). All other models were evaluated using summarized experiment-level data; i. e. , the fraction of tested mosquitoes that had virus-positive saliva for a particular participant-incubation combination. As such, the response variable was equivalent to k infectious mosquitoes out of n, where k and n were specific to a particular participant-incubation combination. Any model that did not contain a shape-constrained term, but did contain a smooth non-linear term (e. g. , the potential for a non-linear relationship between day of illness and transmission probability) was fit using generalized additive models with the mgcv package  [42].
Models that had no smooth terms, but did consider repeated observations from the same participant, were fit using generalized linear mixed models with a random effect by individual with the lme4 package  [43]. Treatment of covariates. Investigations were initiated with the simplest model and the most complex model and increased or decreased complexity as a function of model fit, parsimony, and biological feasibility, with the goal to arrive at a single model that balanced these three components. For each covariate considered, the simplest and most complex models considered are detailed below, as well as, in one case, justification for constraining the functional form to adhere more closely to biological plausibility. When possible (i. e. , when comparing nested models), model comparison was done using a likelihood-ratio test.
In total, our analysis was conducted on 50 feeding events involving 27 participants (44. 4% female) that had detectable DENV-2 viremia at the time of feeding. Of these feeding events, 58% were on participants �17 years of age, 30% on participants 18-31 years of age, and 12% on participants 40-80 years of age (S1 Fig). Corresponding primary data are provided in supplementary information (S1 File) and their descriptive statistics are shown in Table  1. Variation in the number of mosquitoes tested reflects variation in the number of mosquitoes available, variation in the blood feeding rate and/or variation in the mosquito survival rate during the incubation period. We analyzed the probability of a mosquito becoming infectious (i. e. , a direct measure of vector competence) using logistic regression models.
The effect of human blood factors on vector competence could be due to their direct influence on mosquito-virus interactions or indirectly mediated by changes of the mosquito gut microbiota  [48]. A limitation of our study is the small number of human participants that contributed to the analysis and the diminishing number of volunteers who participated in repeated feedings over time. We excluded 2 participants infected with DENV-3, all 9 participants infected with ZIKV, and 16 of the 43 participants with a DENV-2 infection from the final dataset because they did not have detectable viremia at the time of mosquito feeding. Due to the low number of participants retained in the analysis (n = 27), we could not account for a random effect of participant and our statistical power was limited. In addition, participants in our study did not represent the entire spectrum of disease manifestations.
Acknowledgments We thank the residents of  Iquitos  for their support and participation in this study. We are grateful to the  Loreto Regional Health Department , including  Drs. Hugo Rodriguez-Ferruci ,  Christian Carey ,  Carlos Alvarez ,  Hernan Silva  and the  Lic. Wilma Casanova Rojas , who all facilitated our work in  Iquitos. W. Lorena Quiroz  and  Jhonny Cordova  conducted dengue positive case follow-up and interviews and acknowledge the NAMRU-6 Iquitos febrile surveillance and laboratory teams in the rapid capture and diagnosis of dengue and  Zika  cases for direct mosquito feeding studies. We acknowledge the  NAMRU-6 Virology and Emerging Infectious Disease and Command  during the execution of this project. A special thanks to  Gloria Talledo  for her ongoing support with the preparation of protocols and reports for this project.
Funding: The study was primarily funded by a grant from the  U. S. National Institutes of Health/ National Institute of Allergy and Infectious Diseases  ( P01 AI098670  to TWS) with some additional salary support provided by  U01AI151814  to ACM. The study was also supported by the  European Union 's  Horizon 2020 research and innovation programme  under  ZikaPLAN  ( 734584  to LL) and the  French Government 's  Investissement d'Avenir program   Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  ( ANR-10-LABX-62-IBEID  to LL). Further support was provided by a sub contract from the  Bill and Melinda Gates Foundation  to the  University of Notre Dame  ( OPP1081737  to TWS/ACM), the  Defense Threat Reduction Agency, Military Infectious Disease Research Program  (MIDRP,  S0520_15_LI  and  S0572_17_LI ), and the  Armed Forces Health Surveillance Division, Global Emerging Infections System Branch  ( ProMIS ID :  20160390169 ,  P0090_17_N6_1.
The impact of cancer interventions has been conducted in several research due to the significant burden of this noncommunicable disease. The interventions that played an important role in the improvement of the patient's quality of life (QoL) and health-related quality of life (HRQL) can be classified into two main groups: pharmaceutical and nonpharmacological methods. However, studies so far often analyze a specific group of interventions for specific types of cancer. Thus, in this systematic review and meta-analysis, we synthesized the overall impact of cancer interventions on patients' quality of life in several cancers. In this research, we followed the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) to search the longitudinal original research on the Web of Science (WOS) database.
After that, the Newcastle-Ottawa Scale (NOS) and Jadad Scale were used to assess the quality of non-randomized control trials and randomized control trials, respectively. Then, the characteristics of the included studies were described in the six main fields table and the random effect model with robust estimation was applied to analyze the impact of interventions on the health utility of patients. From the database, 122 longitudinal original research were included in the meta-regression, with most of them having high or fair quality. The European Organization for the research and treatment of cancer scale for quality of life (EORTC-QLQ) was the most used health utility measurement at 65. 15%. In the adjusted effect models, the Visual Analogue Scale (VAS) had significant statistics in all models when we compared it with the EQ-5D Scale (p < 0.
Database The Web of Science (WOS) was chosen as the only database because it has more advantages than other resources of scientific articles such as Pubmed, Scopus, and Google Scholar  [21]. To be more specific: Fig. 1  The process of study selection for systematic review and meta-analysis • Publications since the early 21st century were included in this database. • A high number of full-text paper was included, some of which cannot be found in the other sources. • Several high scientific impact journal from all over the world was included in this database. The trends in the literature can be explored easily by some tools in the WOS.
Eligibility criteria Data were searched in June of 2022, and the limited publication date was the 31st of December 2021. The reason for this date is the 2022 data cannot show the full general trend and information for this year. Three main keyword terms were applied to search the database Web of Science in Appendix 1, and we used four steps of the searching strategy: First, the combination of the quality of life and wellbeing terms was used to search all included studies that mentioned the health utility measurement in topics, abstracts, and keywords. Then, we used the keyword terms for different cancer and interventions to filter one more time. After that, we reviewed all included titles and abstracts of these articles and removed papers with inclusion and exclusion criteria. Finally, the extraction data to conduct the meta-regression of longitudinal studies are in Table  1.
Data analysis The titles, abstracts, and full text were read carefully by two reviewers and checked with the inclusion and exclusion criteria. The data will be extracted from each included study and added to the Microsoft Excel (Microsoft Corporation 2013) Software. The Scale of Newcastle-Ottawa was chosen to assess the quality of the non-randomized control trial study and the Jadad Scale is an Oxford scoring system to check the quality of randomized control trial study design  [22] [23] [24]. As regards the Newcastle-Ottawa Scale, this scale was chosen because it may include some vital aspects to judge a study such as selection, comparability, exposure, and outcome. At this point, each part of this scale has four, two, and three maximum points, respectively. Besides, this scale has been developed for a long time by reputable experts and it was used widely in several studies  [22, 23].
General information Authors Finally, as for the 4821 utility measurements from the 122 selected longitudinal studies, the random effect models with robust estimation  [25]  were applied to synthesize the impact of these interventions on the QoL point of cancer patients when conducting the meta-regression. We did not use restricted maximum or maximum likelihood because we collected all data in different research. However, each research they used dissimilar methods and various scale to assess the quality of life points. Therefore we can only group these scales in different groups and applied the random effect models analysis. There are several research-related factor groups that need to be analysed, so when we consider using the funnel plot and forest plot, it may infeasible. Moreover, the amount of collected data was numerous, then we believed that the random effect models with meta regression can be used to evaluate these factors systematically.
Specifically, these models can synthesize the relationship between several feature groups such as types of cancer, types of intervention, and types of country, health utility measurements, and study design to assess the changes in health utility points over time. The meta regression analysis by using STATA software version 16. 0 (Stata-Corp LLC, College Station, TX) was applied to evaluate the change in the health utility scores in each model. Elements are divided into different models: measure, types of cancer, types of interventions, country types and study designs. Within each group, we compared factors with each other and assessed the change in different models. In particular models, the change between quality of life scales is evaluated to find statistical significance. The P-value in this meta regression analysis was designed at the threshold of 0.
The Jadad scale Table  3  depicts the data of studies conducted based on the design of randomized control trials. These data were obtained using the Jadad Scale to analyse the quality of the studies. According to the results of this scale, the studies were divided into two groups namely High and Low quality, with over two third of them being Highquality. There were 30 High-quality studies, taking up 68. 18% of the total number of studies on the topic of randomized control trials. The mean point of this group was 3. 43 with 0. 77 points of SD. For the Low-quality studies, 31. 82% were classified into this group with 14 studies. The mean point of this group was lower than the former group at only 1. 57 with an SD of 0. 67. Regarding the measurement metrics, there were several scales used to quantify the quality of life of cancer patients in the analysed research.
Study setting {9} The study will be conducted in one academic hospital (Universitair Ziekenhuis Brussel) and four regional (non-academic) hospitals (AZ Turnhout, AZ Delta, Jessa Ziekenhuis and AZ Sint-Maarten). All study sites are located in Belgium. Details on study sites can be found at ClinicalTrials. gov with Identifier: NCT05169047 registered on December 23, 2021.
Data management {19} Data will be collected via web-based self-reported questionnaires. To avoid missing data, an error will appear in case a question is not filled in. Collected data will include answers to validated questionnaires related to the patients' overall pain intensity, disability, medication use, health-related quality of life, global impression of change, work status, competencies for self-management, healthcare expenditure, anxiety and depression. In addition, patients will be asked about the number of previous spine surgeries and general demographic data. Written informed consent of the patients will be collected and provides the basis for the legal ground for the data management. During the research, the unblinded researcher is responsible for data management and storage. Following the research, the PI will be fully responsible for data management and storage.
During the research, all obtained data will be stored on a dedicated page on Vrije Universiteit Brussel SharePoint (system-encrypted) with access limited to the TRADITION researchers. A back-up will be foreseen on a secure external hard drive. Following the research, all data will be relocated to the Vrije Universiteit Brussel Archive where it will be archived for 25 years. All possible personal identifiable data (vide infra) will be removed from the archived data. Personal data will be processed in accordance with the ongoing European Union's Data Protection Directive and regulation, the relevant Belgian legislation concerning data protection of July 30, 2018, and good clinical practice. As we collect personal identifiable data, following steps are taken to limit unauthorised access. Informed consents will be preserved at a secure location at the Vrije Universiteit Brussel.