dataesr/scientific-paragraphs-categorization huggingface dataset:
Objectives: Several trials have documented the favorable safety profile, and promising clinical results of pressurized intraperitoneal aerosol chemotherapy (PIPAC) directed treatment in different types of peritoneal malignancies. However, until the results of randomized trials are available, the quality of documentation and acceptance by the users may be improved through a worldwide registry. The International Society for the Study of Pleura and Peritoneum (www. ISSPP. org) facilitated this process by creating a dedicated focus group and providing the funding needed for the creation and implementation of an international database. This article describes the design and the journey of establishing this international database and the first, preliminary results from the ISSPP PIPAC online database.
Methods: In 2019 the ISSPP PIPAC Registry Group started to create a database with a minimal dataset relevant to many diseases and applicable in different framework conditions. The task was divided into three phases including design, testing, implementation, protocol, handbook, legal requirements, as well as registry rules and bylaws for the registry group. Results: The ISSPP PIPAC online database has six key elements (patient, consent, treatment, complications, response evaluation and follow-up). Following design, testing and implementation the database was successfully launched in June 2020. Ten institutions reported on 459 PIPAC procedures in 181 patients during the first 6 months, and the recorded data were comparable to the present literature. Conclusions: A new international multicenter PIPAC database has been developed, tested and implemented under the auspices of ISSPP.
Hence, PIPAC remains so far an "off-label" therapy. Further challenges are occupational health safety, regulatory issues, reimbursement (in the absence of cost-effectiveness data), and the involvement of a wide range of dedicated specialists and institutions. According to the IDEAL framework, PIPAC has now reached the exploration phase (Stage 2b) as the technique was well described and more centers have adopted this procedure. Prospective registry is recommended at this stage to document the safety and clinical outcomes from different centers for learning purpose  [12]. Currently, most PIPAC procedures are performed in high volume, experienced cancer centers. Early in their experience, these centers expressed the wish to share data (in particular indications, number of procedures, technique, drug, dose, complications, etc.
). In January 2016, they started a multicenter, international online documentation (registry) of indications and results of PIPAC and the IDEAL framework (PITAC) for treating malignant peritoneal and pleural diseases (www. clinicaltrials. gov, NCT03210298). This registry was closed in 2020 after reaching the number of patients planned. Results are currently being analyzed. Since many patients cannot be treated within the framework of controlled clinical studies, there was a need for a second registry building upon the experience gained. The aim was to improve further the quality of documentation and acceptance by the users. The International Society for the Study of Pleura and Peritoneum (www. ISSPP. org) facilitated this process by creating a dedicated focus group and providing the funding needed.
Materials and methods The ISSPP PIPAC database aims to monitor PIPAC activities and results worldwide, and for this purpose, the database uses few but relevant variables. This concept should then provide the foundation on which the international PIPAC community would enhance and expand the international collaboration, development, and scientific approach to treating primary and secondary malignant peritoneal diseases. Specifications were easy online access, user-friendly interface, drop-down menus, relatively few and easy to understand relevant variables, low time requirements, and simple monitoring of incomplete input. Besides, the database's technical support and organization should be accessible, transparent, embracing, and open for relevant changes and potential new variables. Finally, an annual report should be provided to the participating centers.
Ethical framework The ISSPP PIPAC database and the study protocol were approved by the Region of Southern Denmark (GDPR, 20/18204) on April 21st, 2020; the Institutional Review Board of the University of Southern Denmark (SDU REC 20/24559) on May 5th, 2020 and by the hosting unit Odense Patient data Explorative Network (OPEN) on May 19th, 2020 (OP-1140, 19/49984).
Methodology of implementation The ISSPP registry group decided to focus on two major tasks: 1) Design, testing and implementation of the new database 2) Create documentation, including registry handbook, standards of practice (SOPs), registry rules and bylaws for the steering committee. These tasks were completed between November 2019 and April 2020 and coordinated by the Odense PIPAC Center, Denmark. There were three phases in project management. -Phase I (November 2019 -March 2020) Phase I included the creation, discussion, and correction of the following documents: -The patient consent form, -Patient module, -Treatment module, -Complications module, -Follow up module, including tumor response. These documents were first drafted during the First Scientific Congress of ISSPP which was held in Singapore in November 2019.
After the programming work, the new PIPAC online database was first tested locally at Odense PIPAC Center, Denmark, and several corrections were implemented. Then, a beta version of the database was tested by a handful of international centers, and the second round of edits was completed. The database guidelines, annual database report design, and bylaws were generated and submitted to internal review in parallel. -Phase II (April 2020-August 2020) The ISSPP PIPAC database was launched in June 2020. During Phase II, the main activities were inclusion from new institutions, monitoring and correction of errors based on feedback from reporting centers. In parallel, we started a prospective quality control of the data entered, including the listing of incomplete data entries for each center. We performed a first exploratory analysis to test the ability of the database to deliver the information expected.
-Phase III (September 2020-August 2021) Phase III is currently ongoing and includes a regular reminder of incomplete data entries, monitoring participating centers and data collection, data analysis, and drafting the first annual report. An annual revision of the dataset is scheduled. Besides the annual scientific report, the ISSPP Registry Group will report to ISSPP Executive Committee on the project progress, the legal and financial aspects. Finally, the ISSPP Registry Group will report on its activities in the society's peer-reviewed journal.
Results The PIPAC database was launched on June 15th, 2020, on the official ISSPP website (www. isspp. org). The access is password protected. The launch was prepared in detail, starting with local testing at Odense PIPAC Center in January 2020. Then, the beta version of the database underwent international testing in five centers (Lausanne, Switzerland; Lyon and Paris, France; Singapore; Tübingen, Germany) in February 2020. The documents were evaluated by the test centers, and after a few edits, they were sent by the ISSPP Registry Group to ISSPP's executive committee for approval, together with the bylaws and an interim progress report. ISSPP's Executive Committee provided its legal consent early in April 2020, but the launch was only effective by June 15th, 2020, due to the Covid-19 pandemic.
Legal aspects The database meets the requirements of the European General Data Protection Regulation (GDPR). Pseudoanonymized data are transferred to OPEN based on the patient consent obtained in each center. The registry data do not leave European soil. For participating centers, legal issues may vary across countries. Still, the minimal universal requirement is the patient signature on the patient consent form and the nondisclosure of identifying personal information. Acquiring the patient signature and compliance with local regulations are the responsibility of each participating center, including non-EU centers.
Access to the ISSPP PIPAC database Access to the database is password-protected. Access requirements are an active ISSPP membership and registration with the database. ISSPP members can find a user request form for the database on the society's website. Users are required to fill out the form and mail it to the database manager. The final account creation requires a manual process, and some waiting time may occur after submitting the request by e-mail. Once the registration process is completed the database can be accessed at https://open. rsyd. dk/ redcap/. More information can be found on the ISSPP website (http://isspp. org/professionals/pipac-database/).
Database modules and components The goal of the first iteration of the database was to keep the number of variables low in order to facilitate a quick data entry process for the PIPAC procedures (Table  1 ). The emphasis is on getting a high degree of complete and valid data entries. The consent form documents the patients' informed consent to participating in the ISSPP PIPAC database. The patient module is the primary entry form for the database; it holds data on demography, primary tumor, and treatment before PIPAC. The number of PIPAC procedures for each patient is not limited. The Treatment, Response, and Complications modules are used for each PIPAC procedure. Complications are graded according to either Dindo-Clavien (surgical complications) or the Common Terminology Criteria for Adverse Events (CTCAE v.
5, for medical complications). The follow-up module holds information on the last contact (e. g. , date of death) and reason(s) for stopping additional PIPAC directed therapy. Response evaluations are registered using either the peritoneal regression grading score  [13]  or merely reporting the level of response. Complete documentation of the different modules and variables can be found in the codebook on the ISSPP website. The current REDCap data dictionary is published there and allows participating institutions to host and extend a copy of the databaselimited to their own cases.
Patient recruitment and first interim analysis Six months after the launch, 10 institutions from around the world have joined the database. There was a steady increase in the number of cases. Currently (as of December 29th, 2020), the database contains data on 181 patients and 459 PIPAC procedures. Forty-four (24%) of the patients were treated with PIPAC-directed therapy within prospective clincal trials. Demographic data are listed in Table  2. The included patients had median 2 PIPAC procedures (range 1-8) with a nonaccess rate of 13% at the first PIPAC (Table  3 ). Only one grade 3 surgical complication was registered in the 459 procedures (0. 2%). Adverse events according to CTCAE were all grade 1 and 2 (Table  4 ).
Annual report Once a year, the RPM will analyze the dataset. Results will be summarized and presented in the Annual ISSPP PIPAC database Report. Members of the ISSPP Registry Group will write the report draft, submitted to the legal entity (ISSPP) before publication. After approval, the results will be published in Pleura & Peritoneum, on the ISSPP website, and presented at the following international ISSPP Congress. Once a year, based on its own experience and the participating centers' feedback, the ISSPP Registry Group will implement the changes needed.
Discussion Since 2011, when the first PIPAC directed treatment of peritoneal metastases in humans was performed  [14] , the number of procedures has steadily increased worldwide, exceeding 10,000 at the end of 2020 (Capnomed GmbH, Zimmern o. R. , Germany, private communication). The evidence is multiplying with ongoing, and results from Phase-I  [2] [3] [4] 15]  and Phase-II  [5] [6] [7] [8]  trials published. Twenty-four clinical trials are currently listed at ClinicalTrials. gov (January 15, 2021), including two randomized Phase-2 trials  [16, 17]. However, in the absence of controlled results comparing PIPAC vs. standard of care, PIPAC-directed treatment has not yet reached the level of evidence needed for full recognition within the oncology community  [11]. The number of patients treated "off-label" as an individual therapy outside the framework of clinical studies, the procedure's generic character, the numerous indications, and the multiple drugs available are arguments in favor of gathering additional data through a quality-controlled, international registry.
This article presents the development, testing, and implementation of the quality-controlled ISSPP PIPAC database as well as the preliminary data gathered between July 2020 and December 2020. The ISSPP PIPAC database was successfully designed, tested and implemented using a stringent sequence of tasks and deadlines. Within six months, despite the Covid-19 pandemic, the database received the official approvals needed and went online. The users' feedback is so far positive, and it seems possible to include patient data easily using drop-down menus and within a reasonable time frame-although this was not yet evaluated in detail. The first preliminary analysis illustrates that the patients who entered into the ISSPP PIPAC Database mirror the general experience with PIPAC as presented in the literature (e.
g. primary diagnosis, number/type of procedures, and complications)  [10]. It is important to note that the data have been entered by a few centers with variable inclusion rates. However, the preliminary results present selected data to illustrate the additional value of such registry. For example, data on PIPAC in rare indications have been collected from individual institutions, which experience is now merged. A successful clinical database is characterized by clinically relevant questions and variables, easy access, low time requirements, high participation and data completeness rates. In addition, the organization behind the database (i. e. the ISSPP Registry Group) should be transparent, embracing, and open for relevant changes. The database should be dynamic and enable updates based on conclusions drawn from regular outcome reports.
The initial step was not to create a research database but to set up a registry to monitor PIPAC activities and results worldwide. Now we will actively promote the inclusion of additional PIPAC centers and create the first annual ISSPP PIPAC database report in 2021. Simultaneously, the ISSPP PIPAC Registry Group will start to include data from other institutional and national databases (Phase IV) and merge data on rare peritoneal diseases treated by PIPAC (e. g. Malignant Peritoneal Mesothelioma).
Conclusions A new international multicenter PIPAC registry has been developed, tested and implemented under the auspices and the legal responsibility of ISSPP, which ensures transparent, independent governance and compliance with good scientific practice rules. The database is accessible through the ISSPP website (www. ISSPP. org), and PIPAC institutions worldwide are highly encouraged to participate. Research funding: None declared. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Competing interests: Olivier Glehen is consultant for GAMIDA. All other authors state no conflict of interest.
Ethical approval: The ISSPP PIPAC database and the study protocol were approved by the Region of Southern Denmark (GDPR, 20/18204) on April 21st, 2020, the Institutional Review Board of the University of Southern Denmark (SDU REC 20/24559) on May 5th, 2020, and by the hosting unit Odense Patient data Explorative Network (OPEN) on May 19th, 2020 (OP-1140, 19/49984).
Installation The SuperIso code can be downloaded from http://superiso. in2p3. fr which upon unpacking creates the main directory superiso_vX. X. The main directory includes thirteen main programs predicting observables in different models; sm. c calculates a few sample flavour observables in the SM, while amsb. c, cmssm. c, cnmssm. c, gmsb. c, hcamsb. c, mmamsb. c, ngmsb. c, nnuhm. c, nuhm. c, thdm. c make predictions in specific NP models (which require to be linked to one of the external spectrum generator programs ISAJET  [1 ] , NMSSMTools  [11] , SOFTSUSY  [12] , SPheno  [13] , SuSpect  [14] , 2HDMC  [15] , etc. ) and flha. c and slha. c predict observable values taking as input a file of FLHA and SLHA format, respectively (a sample example. lha input file in the SLHA format is included).
We describe a way to represent computable functions between coinductive types as particular transducers in type theory. This generalizes earlier work on functions between streams by P. Hancock to a much richer class of coinductive types. Those transducers can be defined in dependent type theory without any notion of equality but require inductive-recursive definitions. Most of the properties of these constructions only rely on a mild notion of equality (intensional equality) and can thus be formalized in the dependently typed language Agda.
This function is continuous in the wild sense, but because we need to know the whole ith layer of the input to get the ith value of the output, this function is not continuous for the natural topology (and certainly not computable). 2. Preliminaries II. Martin Löf Type Theory 2. 1. Basic Features. We work in a meta theory that is in essence Martin-Löf's dependent type theory  [ML01]  with two additional features: • coinductive types, • inductive-recursive definitions. All the constructions described in the paper have been defined using the dependently typed functional programming language Agda  [tea19]. A Note about Equality. This paper is concerned with constructions in "pure" type theory, i. e. dependent type theory without identity. Those constructions enjoy many interesting properties, but proving them requires some notion of equality.
Equality in Martin-Löf type theory is a complex subject about which whole books have been written  [Uni13]. We try to be mostly agnostic about the flavor of equality we are using and only rely on the "simplest" one: intentional equality, written a ≡ T b, or simply a ≡ b. This makes it possible to use vanilla Agda for checking proofs. 2  We annotate the proofs in the paper with • [Agda] : for those that have been formalized using Agda, • [Agda] : for those which have only partly been formalized, typically because the proof is too complex to write in Agda, or because it requires a much stronger version of equality than we have at our disposal. Because we want to explain Agda code only slightly less than you want to read Agda code, the formalized proofs are either omitted from the paper, or explained informally.
We sometimes need to assume equality for functions is extensional, i. e. that f ≡ g iff f a ≡ B g a for all a : A. Those proofs are clearly identified. Notation. The notation for dependent types is standard. Here is a summary: • We write Γ B to mean that B is a well-formed type in context Γ. We write A = B to express that A and B are the same by definition. • We write Γ b : B to mean that b is an element of type B in context Γ. The context is often left implicit and we usually write b : B. We write a = b : B to express that a and b are definitionally equal in type B. When the type B can easily be deduced from the context, we will usually write just a = b. • If B is a type and C is a type depending on x : B, i. e. , x : B C, we write 2 All the Agda code was checked using Agda 2. 6. 1. 1 with the flag --without-K.
The Agda code is available at http://www. lama. univ-smb. fr/~hyvernat/Files/Infinite/agda. tgz, with the file PAPER. agda referencing all the formalized results from the paper. For those without a working Agda installation, the code is also browsable directly from http://www. lama. univ-smb. fr/~hyvernat/Files/Infinite/browse/PAPER. html. • The usual amenities are present: the natural numbers, W-types and so on. We use a universe Set of "small types" containing 0 (with no element), 1 (with a single element ) and 2 (with two elements 0 and 1). Moreover, the dependent sums and products are reflected in this universe, and we use the same notation Σ b : B C and Π b : B C whenever B : Set and b : B C : Set. We assume that this universe is closed under many inductive-recursive and coinductive definitions which will be treated below.
We test our method using a multi-annual dataset from two AERONET sites in Siberia, which is a remote boreal region where biomass burning is typically a predominant source of aerosol during a warm season  (Mikhailov et al. , 2017; Konovalov et al. , 2018 Konovalov et al. , , 2021)). The latter fact facilitates our analysis, as it could be challenging to isolate biomass burning aerosol from anthropogenic and dust aerosol in a general case  (Bahadur et al. , 2012; Schuster et al. , 2016). Even more importantly, studying the absorption properties of biomass burning aerosol in Siberia is of interest in the context of modeling studies aimed at better understanding the role of atmospheric aerosol in the rapid climate change in the Arctic  (Bekryaev et al. , 2010) , since Siberian fires are known to be one of the major sources of absorbing aerosol in the Arctic  (Evangeliou et al.
4 × 10 -3 , which is a typical value of the IRI of BB aerosol particles according to  Reid et al. (2005a). To assess the impact of this parameter on our estimates, we additionally considered a special test case (see Sect. 4. 4) in which the coarse-mode particles were assumed to be nonabsorbing. Note that although we could not properly characterize the variability in the IRI for the coarse-mode particles, the corresponding effects on the absorption properties of BB aerosol particles are partially taken into account by varying the mass ratio of coarse-to accumulation-mode particles. The assigned values for the constant parameters are also listed in Table  1. To build a dataset (or, in other words, a look-up table) of the simulated cases representative of the assumed variability in the aerosol parameters, we performed 10 6 Monte Carlo runs of OPTSIM.
Tech. , 14, 6647-6673, 2021 contribution of anthropogenic BC to AAOD. These situations can result in some underestimation of the relative contribution of BrC to the overall BB aerosol absorption in our analysis but are unlikely to lead to significant (i. e. , exceeding corresponding confidence intervals) biases in our retrievals of k OA and MAE OA. Applying these selection criteria to the available AERONET retrievals yielded a dataset that includes 115 data records, the majority of which (65) come from the Yakutsk site. Different data records normally correspond to different days, but some observations are taken on the same day. The temporal coverage of the selected data is not uniform: the most abundant data (40 data records) correspond to the year 2012, when a major mega-fire event occurred in Siberia  (Konovalov et al.
, 2000; Mallet et al. , 2013; Torres et al. , 2014). Recently, estimates of uncertainties in the AERONET retrievals of SSA and some other variables were derived from the analysis of 27 inversions involving different combinations of probable biases in input data, including AOD, sky radiances, and surface reflectance  (Sinyuk et al. , 2020). These uncertainty estimates are provided in terms of the standard deviations (U27) for the corresponding retrieved variables as part of the auxiliary data products of AERONET and are also available from the AERONET data portal. Accordingly, we used the values of U27 for SSA 440 as estimates for the standard deviation of this input variable. In our dataset, the values of U27 for SSA 440 vary for different SSA retrievals, ranging from 5. 2 × 10 -3 to 2. 0 × 10 -2.