dataesr/scientific-paragraphs-categorization huggingface dataset:
Of these, 19 countries also performed PCR testing. Molecular testing to demonstrate the presence of HEV RNA for at least three months is essential for confirmation of a chronic hepatitis E case. Eleven countries use PCR testing of serum/plasma for this purpose. Interestingly, 26 Member States have HEV testing in place and 19 already perform sequencing of virus isolates. The expert group suggests to sequence a representative subset of virus isolates. For the molecular epidemiological analysis of HEV sequences, an online sequence database and voluntary network is available: HEVnet  [19, 20].
Acknowledgements The authors would like to thank previous expert group members for their great commitment and support:  Wilfrid van Pelt (Netherlands , until 2018),  Lelia Thornton  (Ireland, until 2018),  Elisabeth Couturier  (France, until 2018),  Ágnes Fehér  (Hungary, until 2017),  Rita Korotinska  (Latvia), and  Harry Dalton  ( United Kingdom ). Representatives of the  European Food Safety Authority (EFSA ,  Valentina Rizzi  and  Michaela Hempen ) and the  World Health Organization Regional Office for Europe (Antons Mozalevskis ) were observers in this group. The work was supported by two service contracts No. ECD. 7600 (HEV epidemiological support - NP/2017/OCS/253 ) and No. ID 5132: Hepatitis B, C, and E in the EU/EEA: monitoring and testing activities. Staff that were involved in the contracts:  Esther Aspinall  ( Glasgow Caledonian University and Health Protection Scotland ),  Andrew Rideout  ( NHS Dumfries and Galloway ),  Chris Biggam  ( Glasgow Caledonian University ),  Gill Hawkins  ( Health Protection Scotland ), and  Alison Smith-Palmer  ( Health Protection Scotland ).
Background: Glioblastomas (GB) are the most common and lethal primary brain tumors. Significant progress has been made toward identifying potential risk factors for GB and diagnostic and prognostic biomarkers. However, the current standard of care for newly diagnosed GB, the Stupp protocol, has remained unchanged for over a decade. Large-scale translational programs based on a large clinicobiological database are required to improve our understanding of GB biology, potentially facilitating the development of personalized and specifically targeted therapies. With this goal in mind, a well-annotated clinicobiological database housing data and samples from GB patients has been set up in France: the French GB biobank (FGB).
The French GB biobank (FGB) was developed for this purpose, following a call for tenders from the "Institut National du Cancer" (INCa) in 2012  [6]. This academic biobank is run along the lines of The Cancer Genome Atlas (TCGA) (http://www. cance rgeno me. nih. gov) created by the National Cancer Institute (NCI) of the National Institutes of Health (NIH) in 2005  [7, 8]. The FGB holds biological materials and data for adult patients with GB, and it is run with the support of neurosurgeons, neuropathologists, neuro-oncologists and biologists from 24 centers located throughout France. Its overall aim is to establish a repository of samples associated with clinical data from about 1500-2000 GB patients including epidemiological, imaging, tumor characteristic and follow-up data, together with a collection of classic biological samples (including frozen and formalin-fixed paraffin-embedded tumor tissues and blood samples), and original samples (including tumor tissue frozen in dimethyl sulfoxide (DMSO) for the development of cell-and animal-based GB models, macroscopically normal peritumoral brain zone tissues, hair and fecal samples) to support translational research projects and artificial intelligence technologies for GB.
Design of FGB Following a call for tenders from INCa in 2012 ("Constitution de bases clinico-biologiques multicentriques nationales en cancérologie"), the FGB was set up, with the participation of 24 sites throughout France. The FGB is a member of BBMRI-ERIC (biobank number BB-0033-00093) and has a LinkedIn Page (https ://www. linke din. com/in/frenc h-gliob lasto ma-bioba nk-80850 8153). The preparation of a website is currently underway. The legal and administrative aspects of this project are supported by Angers University Hospital. It is governed by a steering committee including representatives from all the participating centers and a scientific board including two or three members from each participating center. Figure  1  presents a schematic diagram of the workflow of data and biomaterial collection in the FGB.
Eligibility and informed consent The protocols and regulations of the FGB have been approved by the French Ministry of Health and Research (declaration number: DC-2011-1467, cession authorization number: AC-2017-2993), the CPP OUEST II ethics committee (CB 2012/02) and the CNIL ("Commission Nationale de l'Informatique et des Libertés"; the French national data protection authority; no. 1476342). All adult patients undergoing surgery for presumed primary GB are pre-included and asked to sign an ICF for the inclusion of their data and samples in the biobank. The ICF is also used for other brain tumor biobanks funded by INCa (http://www. e-cance r. fr). Patients are definitively included in the FGB when the diagnosis of GB is confirmed by the neuropathologist of the inclusion center according to the criteria of the 2016 CNS WHO classification  [9].
Clinical data and biological material collection The clinical data registered in the FGB include age, sex, family history of brain tumor, medical history, a short survey on pesticide, petrochemical and electromagnetic field exposure, date of symptoms and diagnosis, imaging data, Karnofsky performance status (KPS) score, tumor location, extent of resection, histology, standard biomarkers, treatments, and follow-up every 6 months until death. Biological materials are collected at the time of surgery and include blood, tumor and a lock of hair (Table  1 ). The procedures for biological material collection and storage were developed in accordance with the best practice guidelines issued by several organizations, including INCa (http://www. e-cance r. fr), NCI (https ://biosp ecime ns. cance r.
Biological material and data storage All biological materials are stored in certified biological resource centers (NF96900 and/or ISO9001) or tumor tissue banks at the corresponding participating center. Clinical and biological material data are stored in anonymized and certified eCRFs built with Ennov Clinical software (Ennov, Paris, France). This software is ISO9001:2015certified for all products and activities and meets the recommendations of the FDA 21CRF Part11 and EMA for the IT security of clinical data. Secure Sockets Layer is used to secure data transfer. Only authorized persons from each center can complete the eCRF (https ://nante s-lrsy. hugo-onlin e. fr/CSOnl ine). Quality control for data recording is based on pre-testing and consistency checks during data entry and data management.
Characteristics of the GB patients included in the FGB To date, data and samples for 1087 patients have been included in the FGB. For 573 patients, histology proofreading was performed by RENOCLIP and the initial diagnosis of GB was validated in 99. 5% of cases. The baseline characteristics of the 1087 patients included are shown in Table  2. The median age at diagnosis is 63 years for these patients, and the incidence of the disease is higher in men than in women (sex ratio = 1. 5). The GB included are unilobar or multilobar, located mostly in the supratentorial region (frontal, temporal, parietal, and occipital lobes), and much more rarely in the cerebellum. The IDH1R132H mutation was detected in about 6% of FGB patients. MGMT promoter methylation status was analyzed in 38% of the GB patients and methylation of the MGMT promoter was detected in 49% of these patients.
Data for familial and/or epidemiologic risk factors were collected for 62% of the GB patients, and about 18% indicated exposure to risk factors, such as pesticides (pesticide exposure reported for 44% of these patients). Median OS for the patients included in the FGB (n = 915) is 24. 2 months. This median OS is significantly longer than that for the patients in two other GB cohorts: TCGA-GB (http://www. cance rgeno me. nih. gov/; n = 486; 12. 5 months) and the French brain tumor database (FBTDB; n = 1936; 11. 2 months)  [11]  (P < 0. 001) (Fig. 2 ).
Discussion GB biobanks are an essential tool for ensuring an adequate supply of the high-quality samples associated with clinical data required to support translational programs and artificial intelligence technologies to foster the introduction of personalized medicine for this brain disease. Several glioma biobanks including GB tissues have been developed, in China and the USA, for example, but, to our knowledge, the FGB is the only national clinicobiological database for GB in Europe  [7, [13] [14] [15] [16] [17].
Strengths of the FGB To date, the FGB has included data for 1087 adult GB patients, for 66% of whom a complete set (tumor, blood and hair) was obtained. The GB patients of the FGB are representative of the general population undergoing surgery for GB. In particular, GB is primarily diagnosed in older individuals, at a median age of 63 years, and the incidence of GB is higher in men than in women  [1]. Consistent with other studies, the IDH1R132H mutation is rare in the GB patients of the FGB cohort, in which it was found in 6% of GB cases  [18, 19]. Median OS is 24. 2 months in the GB patients of the FGB, which is significantly longer than the median OS of the patients from two other GB cohorts (TCGA-GB and FBTDB). This difference can be explained by the more recent establishment of the FGB, as GB management has evolved since the other two cohorts were set up.
The possible link between brain tumors and pesticides has been investigated over the last few decades, but remains controversial  [21, 22]. The collection of hair samples included in the FGB provides a potential source of information about the influence of pesticides on the process of GB development. In addition to the collection of clinical and biological data, imaging data can be obtained electronically or on an anonymized and coded CD-ROM. The accessibility of the FGB imaging data and biological materials will facilitate radiogenomic studies seeking to assess correlations between imaging findings and molecular markers predictive of treatment response and prognosis by noninvasive methods. We plan to upload radiological images onto the anonymized eCRF website, to improve data transmission.
We have not performed quality control tests on DNA, RNA, and protein at different time points for a subset of cases to check the integrity of the biological materials over time. Several studies have shown that long-term storage at -80 °C is satisfactory and does not negatively influence RNA quality  [26] [27] [28]. It remains unclear whether storage in liquid nitrogen has significant advantages over storage at -80 °C. These data indicate that the storage conditions used for the FGB may be suitable for the long-term preservation of GB tissues and blood samples.
Sustaining the FGB As shown by the experience of other biobanks, the development and maintenance of clinicobiological databases is a costly activity. The budget from the "Direction Générale de l'Offre des Soins" (DGOS) of the French Ministry of Health allocated to certified biological resource centers or tumor tissue banks covers the costs of collecting and storing biological materials, but not those of collecting and storing clinical data or managing these processes. The budget from different contracts signed with academic and industrial partners requesting samples for research projects is also insufficient to cover these costs. Additional funding must therefore be found to sustain such structures, and this process is time-consuming and fastidious, with low success rates. In the future, research institutions and governments must find solutions to facilitate the funding of clinicobiological databases.
Furthermore, it will be essential to gain and maintain the trust and commitment of all participating centers to sustain such structures. A scientific board has been set up for the FGB, to select, according to transparent rules, the scientific projects authorized to use the data and biomaterial collected. A newsletter is published every 6 months, to inform all FGB staff about the number of inclusions and the projects using the stored data. Furthermore, a group called "FGB network", including at least three representatives from each center has been set up and will be cited in all publications (in authors or acknowledgements) in which FGB samples are used, to enable centers to benefit from their own cooperative effort.
Conclusions Researchers and scientists wishing to set up large-scale studies for understanding the development of GB and its progression, and/or for identifying new diagnostic, prognostic and therapeutic biomarkers for the development of personalized therapy should contact the FGB (https ://www. linke din. com/in/frenc h-gliob lasto ma-bioba nk-80850 8153) to ask for biological materials and associated clinical data. This academic biobank has already included samples and data for 1087 GB patients, and is flexible enough to adapt to diverse requests, making it possible to implement projects quickly.
Acknowledgements We thank  Denise Jolivot ,  Sylvie Couasnon ,  Sybille Lazareff ,  Frédéric Blazek  and  Léontina Boulay  ( CHU, Angers ) for managing the legal and administrative aspects of this project,  Tanguy Roman  ( CHU, Nantes ) and  Carole Colin (Aix-Marseille Univ, Marseille)  for their help in data management,  Annick Barthelaix ,  Marina Denyset  ( CHU, Angers ) and  Marie-Hélène Mauboussin  ( Cancéropôle Grand Ouest, Nantes ) for assistance in the design of the FGB and the interregional coordinators of RENOCLIP:  Claude Alain Maurage  ( CHU, Lille ),  Audrey Rousseau  ( CHU, Angers ),  Karima Mokhtari  ( Hôpital La Pitié Salpêtrière, Paris ),  Pascale Varlet  ( CH S t Anne, Paris ),  Guillaume Gauchotte  ( CHU, Nancy ),  Emmanuelle Uro-Coste  ( CHU, Toulouse ),  François Labrousse  ( CHU, Limoges ) and  Catherine Godfraind  ( CHU, Clermont-Ferrand ).
Data collection Demographic data were collected at inclusion and included: age, sex, American Society of Anesthesiologists classification, body mass index. All intraoperative monitoring variables (blood pressure, heart rate, pulse oximetry, ventilatory variables (tidal volume, ventilator frequency, peak, and mean airway pressures whatever the mode, partial tension of end-tidal carbon dioxide pressure) were collected using Centricity Anesthesia with one value per minute. This is an Anesthesia Information Management System which provides automated data collection and storage into a repository for subsequent exportation as an Excel file (GE Healthcare, Buc, France). In addition to that, number of alveolar recruitment maneuvers and a possible change in the ventilator modality were noticed. At the end of the surgery, the neurosurgeon involved described the amount of bleeding using a 7-level score.
05 significance level. Continuous variables were analyzed using Student t test (normal distribution) or the Mann-Whitney test (non-normal distribution). Categorical variables were analyzed using a chisquare test or Fisher exact test. Association between bleeding levels and ventilation type has also been estimated with proportional cumulative odds-ratio (OR) and [95% confidence intervals (CI)]. Data are expressed as the number of patients (percentages with 95% confidence interval) and means ± SD or medians [25 to 75%, interquartile range]. A P <. 05 was considered statistically significant. All the analyses were performed with R software version 3. 1. 3. The datasets generated and analyzed during the current study are available in the Dryad repository.
47; OR = 0. 68 [0. 31 -1. 51]) (Fig. 2 ). Sex, age, ASA class, BMI, and etiology of surgery did not emerge as factors influencing the level of intra-operative bleeding (P >. 05 for each one). Intraoperative data are reported in Table  2. In the other hand, changes in ventilator settings were more frequent in the PCV group than in the VCV group (3. 5 [2-5. 8] and 1. 5 [0-3] respectively; P <. 001). Peak airway pressure was lower in the PCV group (P <. 001) while mean airway pressures were similar in both groups (P =. 08). Tidal volumes were lower in the VCV group when expressed as absolute values (P =. 05) or as tidal volume / theoretical ideal weight ratio (P <. 001) but similar partial tension of end-tidal carbon dioxide was measured in both groups (P =. 37). Other intraoperative variables were also similar between groups especially mean arterial pressure (Table  2 ).
Literature Review on Blockchain Blockchain is a distributed database (ledger) that maintains a list of records (of transactions, information, Internet Protocol (IP), etc. ). These records are called blocks. Each block has a timestamp, a link to the previous block, and contains the history of every block (transaction) that came before it (down to the second it was created. ) This 'chains' the blocks together. The whole system is end-to-end encrypted. Every transaction is linked to a unique cryptographic signature (called a hash function) that is easy to verify and nearly impossible to falsify because the blockchain is hosted by a growing network of (personal) computers; each computer is a 'node' in the system and each node monitors every other node on a continuous basis. The system grows with each transaction and can become global.
• Consensus-based, which makes it harder to hack or tamper with -and • Requires less trust; a blockchain can only reduce the need for trust. At the bare minimum, trust must be placed in the underlying cryptography. The chain is only as good as the data input, so trust in the quality of the data that others provide is also essential. In the case of a permissioned network (Permissioned blockchain networks allow the network to appoint a group of participants who are given the express authority to validate blocks of transactions and to participate in the consensus mechanism. The second key difference is the ability to restrict who can create smart contracts and/or transact on the blockchain network. (https://monax. io/explainers/permissioned_blockchains/)) trust must be placed in the operator(s) and/or the validators.
• A 51% attack: if more than half of the computers working as nodes to service the network tell a lie, the lie will become the truth. This is called a '51% attack' and was highlighted by Satoshi Nakamoto (2008) when he launched Bitcoin  [7]. Blockchains employ cryptography for authentication, permission enforcement, integrity verification, and other areas. The mere application of cryptography, however, does not automatically make the system more secure per se. The system may be more resilient as data storage and permissions are distributed but compromising the private keys of some network participants could give attackers full access to the shared database, including the ability to reverse a transaction history.
• Data quality: If a blockchain is used as a database  [8] , the information going into the database needs to be of high quality. The data stored on a blockchain is not inherently trustworthy, so events need to be recorded accurately in the first place. The phrase 'garbage in, garbage out' holds true in a blockchain system of record, just as with a centralized database. Blockchains are particularly well suited for the transfer of assets or data native to the respective blockchain. A blockchain cannot assess whether a given input from the 'outside world' is accurate/true or not. If the input is inaccurate or wrong, the blockchain will just treat it as any other input and consider all transfers involving the input as valid as long as certain conditions are met. Attention needs to be paid to data validity and integrity.
Data Collection This study looked at 56 residential building programs distributed across France from the real estate developer's database. The financial cost sheets of those programs were collected. The program's cost is distributed as in Table  1. This study considers only the perspective of the real estate developer, from the phase after land acquisition. The hypothesis is that blockchain in Construction cuts intermediaries and contributes to control risks. The impact of the blockchain in this case study is assessed as follow: Risk + Project Management + Sales support Total Cost The result of this equation is a percentage that approximates the potential cost savings from blockchain deployment in the case of this research article: Housing building construction.
Results and Discussion Table 2 reveals the average, median, and standard deviation of the case study's samples. The potential cost savings from blockchain deployment is estimated at 8. 3% of the total cost of the housing building construction, with a standard deviation of 1. 26%. The results of the savings, mainly from the transactional costs rooted in the conventional organization, are shown in Table  2. We also collected data on the real estate's margin on the building housing programs. The average net margin is 6% with a 2. 5% standard deviation. The latter is high because of the inherent risk associated with each program. The potential cost savings from a blockchain is higher than the current net margin.
Discussion and Synthesis of the Role of Blockchain in Construction The blockchain is, above all, a structured database. Today, it is mainly used to record financial decisions, transactions and agreements. While transaction costs account for a significant proportion of construction costs  [15] , it can also be applied to physical flows (e. g. , product movement), and information flows (e. g. , a particular action is activated). The construction sector is first and foremost a sector dominated by social interactions and the human aspect. Our experience and years of research works with construction companies suggest that one predominant challenge of the sector is the search and identification of the Right information at the Right time in the Right place and especially to the Right person. In this sense, the blockchain has a big role to play by highlighting the flow of information.
• Finality: A single, shared ledger provides one place to go to determine the ownership of an asset or the completion of a transaction, thus providing a great contract management system between the involved parties. A case study was realized to provide an estimation of the potential impact, in terms of cost, of the blockchain technology for a real estate developer. A total of 56 building housing programs from the real estate developer's database concern this study and are distributed in different regions across France. The results revealed a potential cost savings from blockchain deployment at 8. 3% of the total cost of the housing building construction, with a standard deviation of 1. 26%. Future research should focus on validating the hypothesis considered in this study and providing a framework to assess the impact of the blockchain in construction.
Among the 4,296 genes representing the core genome, 3,782 genes were present only in a single copy in the genomes (Fig. 1B ), and these formed the basis for a phylogenetic assessment (Fig. 1A ). Our previously published genome-scale metabolic models of the Penicillium species (  16 ) served as an annotation of metabolic genes as defined in the MetaCyc database  (17). We found that 582 single-copy OGs were part of the core metabolism and that 1,220 metabolic reactions were present in all metabolic networks (Fig. 1C ). These core reactions were significantly depleted for reactions involved in secondary metabolism (adjusted P ϭ 5eϪ9, hypergeometric test), in particular, alkaloid and terpenoid biosynthesis. Conversely, all other parts of metabolism (as defined in the MetaCyc database), except for inorganic nutrient metabolism, were significantly enriched in the core genome fraction (adjusted P Ͻ 0.