dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Considering the immediate need for the data to improve the patient care process and due to limitations in conducting face-to-face interviews during different waves of the disease in Iran (four waves), the data was collected via semi-structured in-depth telephone or face-toface interviews from April 1, 2020 to the end of March 2021. Patients with a positive PCR test and a history of hospitalization were identified by referring to the infection control offices of hospitals. The outpatients seeking health services were also identified by visiting clinics and physician offices. By referring to the staff providing health care to these patients (hospitals, clinics, or doctor offices), the patients who had rich information relevant to the purpose of the study were purposefully selected.
The data gathered was analyzed simultaneously with the interviews. For this, the interviews were immediately transcribed verbatim, and the written data were read for several times to arrive at a general understanding of their content. The initial codes were extracted from the data. A thorough interview transcript was regarded as the unit of analysis, and meaning units included words, sentences, and paragraphs. The initial codes were compared with each other to merge related ones and form categories and subcategories based on their similarities and differences. The categories were also compared with each other and grouped into higher-level main theme.
Results Overall, 34 patients with COVID-19, including 17 (50%) females and 17 (50%) males, participated in this study. The participants' mean age was 49. 79 ± 15. 03 years, and 24 of them were married. The data were collected through 14 face-to-face interviews and 20 phone interviews (Table  1 ). Qualitative data analysis led to the emergence of one theme, seven categories, and seven sub-categories (Table  2 ).
Our data showed that patients may experience postrecovery anxiety. In parallel, other studies showed that more than half of patients with SARS-COV infection still suffered from psychological problems even months after recovery  [35, 36]. Considering that the COVID-19 disease in many aspects is more severe than SARS  [37] , it seems that more measures should be implemented to prevent its psychological consequences in affected people. Sleep pattern disturbance was noted as another problem of COVID-19 patients. In line with this, sleep disorders were reported in another study  [38]. Sleep plays an important role in regulating the cellular and humoral immunities, and insufficient sleep can weaken immune responses to the virus  [39]. Our findings showed that physical problems may be in part involved in patients' sleep disturbance, which may be resolved by using appropriate drugs  [40].
The reporting format in this study (supplement 1) includes calculation of the radiological PCI and other details like the sites and structures bearing the largest tumor nodules and a description of the morphology in each region. Similarly, the surgical findings will be documented in a systematic pre-specified manner. To ensure uniformity in the morphological description, a list of morphological features has been made for each for the radiological, surgical and pathological evaluations. This description has to be provided with the lesion score for each region of the PCI. The first 6 months of the study comprise a test phase in which teams will see the feasibility and compliance of this form of data capturing. Based on the inputs from the participating centers, if required, some modifications will be made in the format of data collection.
End points Primary end points: The primary end point is disease distribution in the peritoneal cavity. Disease distribution in the peritoneal cavity will be captured in detail on imaging, during surgery and on pathology. The confirmation of disease on pathology will be considered as confirmatory for presence of disease in each region. If only debulking is performed, for regions which are not addressed during surgery, the surgical evaluation will be considered. The abdominal cavity is divided into 13 regions according to Sugarbaker's PCI and the structures in each region are defined using the PROMISE internet application to ensure uniformity in reporting. [15, 16]  Apart from the main data collection spreadsheet, separate forms have been created for the radiological, surgical and pathological evaluation to facilitation capturing of the disease distribution (supplements 2-4).
Secondly, the main focus will be a pathological complete response which does not have different criteria across the scores. Regional and peritoneal nodes will be evaluated as previously described. [11]  The reporting of other pathological findings like evaluation of the primary tumor, if present, grade or pathological classification will be according to the existing protocols at each center and will be captured in the data form. Similarly, the molecular markers that have been tested will be recorded with the test results. Performing specific molecular tests is not binding.
Ethics and dissemination This study was approved by the Zydus hospital (institution of the first author) ethics committee on the 27 th July 2020. At Lyon-sud hospital, this study is being carried out within the framework of the RENAPE observational registry (CNIL-no. DR-2010-297) and the BIG-RENAPE registry (NCT NCT02823860), IRB number A15-128. [30]  Subsequently, approval was obtained at other centres according to the existing institutional policies. The proposed analyses will be carried out during the course and after completion of the study and the results published in peer-reviewed scientific journals.
Methods For this cross-sectional study, we obtained data on aggregate utilization (number of prescriptions, total days' supply) and aggregate spending by patients and by pharmacy benefit managers to pharmacies for every brand-name drug prescription (excluding specialty drugs) dispensed to all fully insured patients (44. 4% of commercial market in 2017) and some self-insured patients with commercial insurance in Massachusetts from 2015 to 2017. 4  Data were derived from the Massachusetts All-Payer Claims Database and represented 1510 brand-name drugs and 8. 45 million prescriptions. Analyses were conducted between February and October 2020 using the R statistical software (R Project for Statistical Computing). This cross-sectional study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
Given the use of deidentified data exclusively, the study was deemed not to require ethics board review based on the policy of the Office of Human Subjects Research Protections, National Institutes of Health, under the revised Common Rule. We analyzed publicly available, aggregated drug-year level data. For each drug-year, we calculated the mean point-of-service price per 30-day prescription (ie, excluding rebates and discounts that may affect net spending),  5, 6  the mean patient OOP price per 30-day prescription, and the mean patient-paid proportion of point-of-service price ("patient share"). The point-of-service price is not the manufacturer list price; it is the negotiated reimbursement price paid to the pharmacy and is used to calculate patient cost sharing. For drugs not covered by one's insurance plan (eg, those subject to a deductible), patients pay point-of-service price.
Top 20 Brand-Name Drugs by Mean Patient OOP Price, With Point-of-Service Price and 2015-2017 OOP Price Growth Rates a Brand-name prescription drug use and spending data were obtained from the Center for Health Information and Analysis, based on pharmacy claims from mostly fully insured individuals from 11 commercial payers from Massachusetts (All-Payer Claims Database, 2015-2017). We included all drugyears in the data set with at least 30 prescriptions per year (n = 1510). The mean OOP price per 30-day prescription equals the mean OOP price per 30-day fill per year, from 2015 to 2017.
Table. Top 20 Brand-Name Drugs by Mean Patient OOP Price, With Point-of-Service Price and 2015-2017 OOP Price Growth Rates a Brand-name prescription drug use and spending data were obtained from the Center for Health Information and Analysis, based on pharmacy claims from mostly fully insured individuals from 11 commercial payers from Massachusetts (All-Payer Claims Database, 2015-2017). We included all drugyears in the data set with at least 30 prescriptions per year (n = 1510). The mean OOP price per 30-day prescription equals the mean OOP price per 30-day fill per year, from 2015 to 2017. Price per 30-d fill Annual growth Mean point- Drug name Therapeutic class Mean OOP, $ b rate in OOP, % c of-service, $ d All drugs Not applicable 45. 46 43. 9 2286. 50 Firazyr Anti-inflammatory 1937. 25 -21.
Acknowledgements This work was supported by  HFML-RU , member of the  European Magnetic Field Laboratory (EMFL). This work is part of the research program of the  Netherlands Organisation for Scientific Research (NWO). The authors also thank  Nigel E. Hussey  for useful discussions,  Uli Zeitler  for technical help during the VSM experiment, and  Lilian Prodan  for assistence with energy-dispersive X-ray spectroscopy. D. K. was supported by the  PRIME program  of the  German Academic Exchange Service (DAAD)  with funds from the  German Federal Ministry of Education and Research (BMBF). L. V. P. acknowledges support by the  European Research Council  grant  ERC-319286-"QMAC"  and the computer team at CPHT. Open access funding enabled and organized by Projekt DEAL.
Acknowledgements Administration of the questionnaire and editorial assistance was provided by  Ed Childs, PhD  of  Adelphi Communications (Macclesfield, UK)  in accordance with the  Good Publication Practice guidelines. Section Head of Advanced Heart Failure at NorthShore University Hospital. NS has particular expertise in cardiac amyloidosis and sarcoidosis and has published over 50 articles on advanced heart failure, mechanical circulatory support, and transplantation. Michel S. Slama  is a professor of cardiology at  Xavier Bichat Claude-Bernard hospital and Université Paris-Saclay. He is Head of the Centre de Compétence Amyloses Cardiaques  and Member of the  French National Reference Center for Familial Amyloid Polyneuropathy (CRMR-NNERF ) since 2005. His group has published research on multimodality imaging for early detection of amyloid cardiomyopathy, prophylactic pacing for conduction disorders, and the diagnostic and prognostic value of sympathetic and parasympathetic denervation.
ACKNOWLEDGMENTFunding provided by Mars Petcare. The authors acknowledge all the personnel at WALTHAM Petcare Science Institute involved in the care of the cats and in the collection and analysis of the study samples. We thank Jonathan Stockman for his contribution to the set-up of the study and Janet Alexander for sharing her expertise throughout. We are also thankful to Yann Queau and Delphine Moniot for providing scientific advice in the interpretation of the results.
Based on the results, experts from the Asia-Paci c Alliance for the Control of In uenza (APACI) issued recommendations for the improvement of existing surveillance systems. Results: Australia demonstrated the broadest scope of in uenza surveillance followed by China and Malaysia. In Australia, surveillance tools covered all sub-systems. In China, surveillance did not cover non-medically attended respiratory events, primary care consultations, and excess mortality modelling. In Malaysia, surveillance consisted of primary care and hospital sentinel schemes. There were disparities between the countries across the 5 evaluation criteria, particularly regarding data granularity from health authorities, information on data representativeness, and data communication, especially the absence of publicly available in uenza epidemiological reports in Malaysia.
This dual approach describing the scope of surveillance and evaluating the adherence to WHO guidance enabled APACI experts to make a number of recommendations for each country that included but were not limited to introducing new surveillance tools, broadening the use of speci c existing surveillance tools, collecting and sharing data on virus characteristics, developing immunization status registries, and improving public health communication. Conclusions: In uenza monitoring in Australia, China, and Malaysia could bene t from the expansion of existing surveillance sentinel schemes, the broadened use of laboratory con rmation and the introduction of excess-mortality modelling. The results from the evaluation can be used as a basis to support expert recommendations and to enhance in uenza surveillance capabilities.
In Malaysia, in uenza surveillance focused mainly on in uenza activity and virology (Fig. 3 ). 15 sentinel clinics under ILI syndromic surveillance and 8 sentinel hospitals under SARI syndromic surveillance collect ve samples per week each. Then, specimens are sent to two designated laboratories: the MKAK Sungai Buloh for the ILI specimens and the Virology Unit of Institute of Medical Research (WHO NIC) for SARI specimens. [32]  As a WHO NIC, the Department of Medical Microbiology in University of Malaya reports isolates separately to a WHO CC. Laboratory test results were reported through the FluNet database, and published on the WHO GISRS webpage. [9]  Although respiratory virus outbreaks in public settings may be reported to the MOH, there was no reporting obligation as in uenza is not a noti able disease in Malaysia.
As well as the data produced by the eight sentinel hospitals monitoring SARI cases, severe outcomes such as hospitalizations, ICU admissions and deaths were not monitored or reported. Similarly, data on in uenza-related ED visits were not collected for surveillance purposes in Malaysia. No surveillance tools were used to monitor non-medically attended cases occurring in the community. Adherence to WHO guidance Australia, China, and Malaysia contribute to WHO basic requirements in terms of monitoring of in uenza virology and activity. All three countries provide the results from their sentinel schemes in primary and secondary care on a weekly basis to their respective NIC, supporting the identi cation of seasonality patterns and circulating subtypes. Compared to Malaysia, both China and Australia have developed more in-depth laboratory surveillance and both countries have a WHO CC reference and H5 reference laboratory, providing data on in uenza virus genome sequencing, antigenic characterisation, antiviral resistance, and identi cation of pathogens with pandemic potential such as avian in uenza viruses.
Data granularity In the fortnightly reports from the Australian Department of Health, data on laboratory-con rmed cases from the NNDSS are reported in 5-year age groups, by virus type and sub-type and by state. As well as age, information on underlying conditions is made available for most severe cases, with the severity of the infection documented through data on ICU admission and death. No information on sex and immunisation status are publicly available. In China, data include age and sex, following WHO guidance, but there are no data available on underlying conditions, immunization status, or the complications of in uenza. Although the data reported by the Malaysian MOH are broken down by age, this is not in line with the age strati cation recommended by WHO. Also, no information on underlying conditions, immunization status, and consequences of in uenza are recorded.
Data accuracy Australia processes between 1,000-5,000 samples per week, reported by its sentinel laboratories across the states. In China, the NISN sampling strategy varied: in the South, 10-40 samples were collected from each site every week, with an average of 20 samples each week per site, whereas in the North, 20 samples per site were collected per week during the u season, and 20 samples per site each month at other times. The CCDC processes up to 20,000 samples per week from the NISN. The NIDRS uses clinical diagnosis and a range of test types, many of which are less sensitive than RT-PCR (which is used for NISN). The sampling strategy in Malaysia was ve samples per week per site for reverse transcriptase polymerase chain reaction (RT-PCR) testing, but at the discretion of the sentinel sites.
Data communication The Australian Department of Health publishes the national in uenza surveillance report fortnightly during the season, and monthly otherwise. [26] In China, the CNIC has generated online weekly in uenza reports since 2005 to share when, where, and which in uenza viruses were circulating using NISN and CPHEMIS surveillance data. The weekly reports, in both Chinese and English, are emailed to key stakeholders and are also made available on the CNIC website. [30] The NIDRS noti ed in uenza cases and deaths are shared with a breakdown by age and province on a monthly basis by the CCDC on the Public Health Science data platform. [29]  Severe outcomes data (hospitalizations) are not available, except occasionally in scienti c publications from observational studies. [39]  Data are not made publicly available by the Malaysia MOH even though both weekly and annual in uenza surveillance reports are produced for internal use.
[40]   [41]  In China, in uenza epidemiology outcomes on a longitudinal and nationwide perspective would be useful to estimate the national burden of in uenza with the absolute number of in uenza cases, consultations, hospitals admissions, and deaths. Estimates of in uenza incidence, associated hospitalization rate, and mortality rate could be produced on an annual basis at a minimum by province. It is also suggested to expand the current SARI network to cover all provinces and publish data on severe in uenza cases using the existing weekly bulletin from the CNIC. To compensate for the limitations of in uenza deaths noti ed under the NIDRS, the adoption of an excess-mortality model to estimate the mortality attributable to in uenza or for all causes, as previously performed by Chinese research groups at national, provincial, and city levels would be useful.
Acknowledgments We would like to thank the  APACI  for its enthusiastic and active support to prepare this manuscript and in particular,  Dr. Raja Dhar  from the  Department of Pulmonology, Fortis Hospital in Kolkata, India , member of the APACI for their nal review of the manuscript. The manuscript has received an editorial support by  Nicola Truss , inScience Communication, London and was funded by  Sano Pasteur.
Acknowledgements Financial support is gratefully acknowledged (L. J. C. and R. C. K. ) from the  French Ministère de l'Education Nationale et de la Recherche , the  French Canada Research Fund (KS) ,  Frontier Research in Chemistry Foundation (LabEx CSC ,  ANR-10-LABX-0026_CSC ), and the  French National research agency  ( LAPIN  project n°  ANR-20-CE09-0021-02 ;  LUCAS  project  n°ANR-19-CE29-0014-01 ) for financial support. N. H thanks the  European Commission  ( Horizon 2020 -MSCA-IF  for L. F. -S. ). Dr Bruno Vincent  is acknowledged for technical support for NMR. A. R. -R. and C. P. -I. thank  de Computación de Galicia (CESGA)  for providing the computer facilities and the  Ministerio de Ciencia e Innovación  (Grant  PID2019-104626GB-I00 ).
Introduction Clustering is a standard optimization task that seeks a "good" partition of a metric space, such that two points that are "close" should be in the same part. A good clustering of a dataset allows to retrieve and exploit data, and is therefore a common routine in data analysis. The underlying data can come from various sources and represent many different objects. In particular, it is often interesting to cluster geographic data. In that case, the metric space can be given by a transportation network, which can be modeled by graphs with low highway dimension. In this article, we study some popular clustering objectives, namely Facility Location, k-Median, and k-Means, in graphs with constant highway dimension. The two latter problems seek to find a set S of k points called centers in a metric (V, dist) that minimizes v∈V (min f ∈S dist(v, f )) p , with p = 1 for k-Median and p = 2 for k-Means.
Study Design APACAN2 is a French single-centre, prospective, interventional, non-randomized trial designed to assess the feasibility of a homebased adapted physical activity (APA) intervention during neoadjuvant chemotherapy for early HER2-positive breast cancer (NCT02963363). Seventy patients are expected to be recruited. Patient enrolment is expected to take 6 years and the study duration for each patient is 20 months. Participants can withdraw at any time. Data obtained will be retained with consent, and any reasons given for withdrawal will be recorded.
Secondary Objectives and Endpoints The secondary objectives of the APACAN 2 trial are: -assessment of the impact of the APA program on exercising time and sedentary time; quality of life; fatigue; weight; physical capacities; physical activity in the rest of the day; lipid profile; ventricular ejection fraction. This will be evaluated using questionnaires [RPAQ  (34) , QLQ-C30  (35) , MFI-20  (36) ], physical tests, blood tests and cardiac ultrasound. This data will be collected at baseline, at the end of chemotherapy and at the beginning and the end of targeted therapy. -description of the longitudinal evolution of physical activity and sedentary behaviour at each step of treatment, -exploration of barriers to program uptake, -conditions of the return to work, -exploration of changes in cancer treatment.
The Hryniuk score will be used to explore the treatment changes occurring. Concerning the resumption of professional activity, a survival curve for time to recovery will be calculated. Concerning the analysis of the longitudinal evolution of physical activity and time spent in sedentary behaviour, a mixed-model ANOVA will be used. The evaluation of barriers to physical activity adherence will be carried out using the following tests: ANOVA, Pearson's correlation or Spearman's rank tests depending on whether the distributions are Gaussian and homoscedastic. A logistic regression will test the respective influence of the different factors on whether or not the recommendations are met. The secondary endpoints will be compared to control data from the Jean Perrin Centre or with data from the literature whenever possible.
Data Management And Monitoring The data collected for the trial will be entered on an electronic case report form (eCRF) (EnnovClinical). The people with access to the data will be the investigators, the clinical research associates, the project leader and the biostatistician. They are authorized professionals and are subject to professional secrecy. The investigator will ensure the accuracy, completeness, and reliability of the data recorded (pseudonymized patient data) and the provision of answers to data queries. Regular monitoring will be carried out by a clinical research associate mandated by the sponsor. The objectives will be to ensure the correct conduct of the study in each centre, the collection and recording of data generated in writing, its documentation, recording and reporting, in accordance with the legislative and regulatory provisions in force.