dataesr/scientific-paragraphs-categorization huggingface dataset:
The new methodology of dissemination of detailed statistics is based on a Beyond 20/20 format which is also used by the International Energy Agency (IEA) and the French National Institute of Statistics and Economic Studies (INSEE; "Institut National des Statistiques et des Etudes Economiques"). It mainly provides long-term data series. The annual energy statistics summarize the consumption of different types of energies. This database presents the annual series in units (per kWh for gas or electricity). All statistics can be downloaded free of charge and reused with any license or payment of royalties, provided the acknowledgement of the source. More details on the PEGASE database are available on http://www. statistiques. developpement-durable. gouv. fr/donnees-ligne/r/pegase. html   37. Insulation measures affect indoor temperature, humidity, energy consumption and mould growth.
38. In particular, creation of a dampness and mould score from resident-and inspector-reported data and use of validated index of depressive symptoms to assess depression. 39. Angers (France), Bonn (Germany), Bratislava (Slovakia), Budapest (Hungary), Ferreira do Alentejo (Portugal), Forli (Italy), Geneva (Switzerland), Vilnius (Lithuania). 40. In  Ezratty et al. (2009) , dwelling energy inefficiency is defined in terms of characteristics of dwelling construction, indoor air quality, heating and cooking systems, ventilation, thermal comfort and weather-tightness of windows and roof. 41. Diarrhoea and ulcers of the stomach or duodenum. -Mental health effects of fuel poverty is significant in adults and x -Living in cold and damp housing contributes to a variety of different mental health stressors, such as persistent worry about debt and affordability, thermal discomfort and worry about the consequences of cold and damp for health -Improvements in energy efficiency are often associated with significant improvements in mental well-being  Lacroix and Chaton (2015)  Relationship between fuel 44.
Statistics on Resources and Living Conditions (SRCV) 19 database published by the French National Institute of Statistics and Economic Studies (INSEE) 20. This database is a part of the European Union Statistics on Income and Living Conditions (EU-SILC). The EU-SILC is the EU reference source for comparative statistics on income distribution and social exclusion at the European level, particularly in the context of the "Programme of Community action to encourage cooperation between Member States to combat social exclusion" and for producing key policy indicators on social cohesion for the follow-up of the main EU 2020 target on poverty and social inclusion. It provides two types of annual data.
50. In particular, Robić and Antcic (2018)  use the following fuel-poverty indicators: (1) subjective qualitative assessments by the affected persons, (2) subjective qualitative assessments by others, (3) objective non-expenditure-based indicators and (4) expenditure-based indicators. To construct their database, a total of 394 households were visited in Sisak-Moslavina County, and complete data were collected and analysed for 375 households.
Acknowledgments The author wishes to express her gratitude to the anonymous reviewers for their helpful comments. She also thanks  David Ormandy  ( University of Warwick, UK ) and  Véronique Ezratty  ( Medical service, Électricité de France (ÉDF), France ) for their useful comments on a preliminary version of the article. This study was financially supported by the  French Energy Council ("Conseil Français de l'Energie" (CFE) ). All errors remain with the authors. The usual disclaimer applies.
For more information on the methodological and practical framework for the computation and production of the EU-SILC database, as well as information on quality and methodological limitations, interested readers can consult http://ec. europa. eu/eurostat/statistics-explained/i. French data in the EU-SILC database are available for free upon request from the French Quetelet network, accessible at the following address: https: //quetelet. casd. eu/en/utilisateur/connexion.
Some examples of this information are included here. While we are aware that these observations were not performed by scientists, we see no reason why they may be erroneous, and thus we believe that they are be a valuable contribution to our scientific findings. In our previous article on bipaliines  (Justine et al. , 2018b) , after seeking authorisation from non-professionals, we made a considerable effort to publish all available photographs. This was time-consuming, since formal authorisation had to be collected sometimes up to 5 years after an email was received. In this article, we received a far higher number of citizen science reports than in our previous article on bipaliines  (530 records vs. 117). We decided that publishing all the photographic information and data would make the Supplemental Files too large and was probably not beneficial.
Instead, we selected the best and most informative photographs. Our aim was originally to collect information for metropolitan France, but a few records were also obtained from neighbouring countries, namely Switzerland and Italy. In an open science perspective, we have made all 530 records obtained in France public, as Supplemental File 1; records have been anonymised, that is the names of private persons have been deleted. Data will also be transferred to national databases on biodiversity and then to the Global Biodiversity Information Facility.
A fragment of 825 bp was amplified with the primers BarS (forward 5′-GTTATGCCTGTAATGATTG-3′)  (Álvarez-Presas et al. , 2011)  and COIR (reverse 5′-CCWGTYARMCCHCCWAYAGTAAA-3′)  (Lázaro et al. , 2009; Mateos et al. , 2013). PCR products were purified and sequenced in both directions on a 96-capillary 3730xl DNA Analyzer sequencer (Applied Biosystems, Foster City, CA, USA). Results of both analyses were concatenated to obtain a COI sequence of 909 bp in length (designated in this text as 'long sequence'). Sequences were edited using CodonCode Aligner software (CodonCode Corporation, Dedham, MA, USA), compared to the GenBank database content using BLAST, and deposited in GenBank under Accession Number MN529561-MN529582. For several specimens, only 'short' sequences were obtained (Table  3 ).
Matrix Many sequences of the COI gene of O. nungara are available in GenBank, under various species names, due to confusion and the complex nomenclatural history of the species  (Álvarez-Presas et al. , 2014; Carbayo et al. , 2016; Lago-Barcia et al. , 2015 , 2019). These sequences were obtained with various primers and thus overlap only partially. We constructed a matrix following three steps. Step 1: we constructed a large preliminary matrix including almost all the sequences of O. nungara (under different names) from GenBank and selected sequences of other species of Obama. We found that only a part of the COI sequence was shared by a significant number of them. Step 2: we trimmed the matrix to keep only the shared part. Preliminary analyses with this matrix produced trees with low support, because many of the sequences from GenBank had misreads.
Information obtained from citizen science: presence Our database of valid records of land flatworms received from mid-2013 to December 2018 contains more than 1,000 records, after elimination of species that are not flatworms. We found that 530 valid reports, that is over half of them, concerned O. nungara in Metropolitan France. This is therefore the most often recorded species. Most reports were from non-professionals, while a few were from professionals or scientists. Figure  14  is a map of these records in metropolitan France, with each record shown as a dot on the map. The map includes general colour-coded information on altitude. The records were most abundant along the Atlantic and Mediterranean coasts; mountainous areas (Alps, Pyrenees, Massif Central, France) were devoid of records, which were mainly in the plains.
Only 24 Departments had no records; they are mainly in the Massif Central and the north-eastern quarter of the country. In the discussion, we briefly comment on these results in view of population densities in various Departments. We used our database to investigate the influence of altitude. Figure  16  shows a chart of the number of records against increasing altitude. More than half of the records were from an altitude below 50 m; records above 250 m were rare, and no records were from above 500 m.
Interestingly, he reported in 2019 that no flatworms were present. Michel Hir, from Brest (Finistère, north-western France), collected and destroyed specimens every day in his garden from November 2015 to May 2016 (Table  4 ). His garden is 175 m 2 in surface area. He mentioned that the garden was enclosed by 2 m high walls and observed that specimens were only occasionally seen on the wall but never more than 40 cm in height, so we can consider that all specimens found originated from reproduction of species from his own garden, without invasion from the neighbouring areas. He collected a total of 1,442 specimens (10. 2/day). After that, he reported that flatworms were still as numerous in the garden. A simple calculation from Michel Hir's data for the highest period he recorded (November: 19 worms/day/175 m 2 ) provides an estimate of population increase due to reproduction of 1,085 worms/hectare/day, or 0.
Trees and distances A tree (Fig. 17 ) was constructed from a matrix built from a selection of COI sequences of O. nungara from GenBank and our own new sequences, with a selection of sequences from other species as the outgroup. The matrix had 99 taxa with 255 positions and was perfectly clean, that is without misread or blank parts. The O. nungara clade included 91 sequences and had high support (ML 96%, NJ 99%). Because of the complex nomenclatural history of the species, many specimens labelled in GenBank as 'Obama marmorata', and others labelled as 'Obama sp. ' or 'Obama sp. 6', were included within the O. nungara clade-we consider these sequences as representatives of O. nungara. Within the O. nungara clade, there were three clades, each with high support. These three clades are outlined in the simplified tree in Fig.
Geographical and temporal biases in citizen science data: a warning Data from citizen science were instrumental in our research. It has been shown that recordings of invasive species is one of the major fields of research using citizen science  (Kullenberg & Kasperowski, 2016). However, the quality of data obtained from citizen science requires assessment  (Kosmala et al. , 2016) , and we would like to point out two biases in our data: geographical and temporal. Geographical biases happen when more reports are obtained from an area where more members of the public provide reports, although this area does not actually harbour more specimens. This is evident on our map (Fig. 14 ) for the reports from the area around Paris. The Paris region harbours 18% of the French population, with a much higher density of people compared to the density in the rest of the country.
However, May is also a period when O. nungara is abundant, and, in the Northern Hemisphere, a period when amateur gardeners spend much time in their garden. Figure  21  shows the number of records of the species each month during the 5 year survey. Although it is probably true that O. nungara is less abundant in winter (December-February) we do not know with certainty whether this is an actual decrease of abundance, or whether the number of records is low simply because this is a period when gardeners spend less time in their gardens. Similarly, the peak of abundance in May and June is probably genuine, but the data were biased by the abundance of records in May 2018 mentioned above (Fig. 22 ). These geographical and temporal biases might hamper a thorough analysis of the data from citizen science; again, we insist that citizen science data are of extreme interest, but their interpretation should never neglect these possible biases.
CONCLUSION In this article, we analysed data from citizen science over a five-and-a-half-year period and showed that O. nungara has invaded more than two-thirds of Metropolitan France, except for mountainous areas. We also found that local abundance in gardens can reach up to 100 of specimens, and that the specimens in an invaded garden can produce, by sexual reproduction, numerous progeny. Our molecular barcoding study based on newly acquired and GenBank sequences of the COI gene confirmed that the populations of O. nungara found in Europe are similar to two populations from Argentina (but not from Brazil)  (Lago-Barcia et al. , 2019). We also confirmed or showed that a single population has invaded Spain, Portugal, France, UK, Italy, Belgium and Switzerland. Although more than 10 species of land flatworm have invaded Europe (Table  1 ), no other species is comparable with O.
ACKNOWLEDGEMENTS A complete French translation of the article is available as a  Supplemental File / Obama chez moi !L'invasion de la France métropolitaine par le ver plat Obama nungara (Plathelminthes, Geoplanidae). We thank all the members of the general public who participated in the survey; those who sent specimens are particularly thanked. Names of non-professionals, and sometimes scientists, who provided photographs and/or specimens are indicated in Tables and Figures. Two contributors,  Sylvain Petiet  and  Michel Hir , kindly provided their results for counting. The support of various Regional federations for the control of pests (Fédérations  Régionales de Défense contre les Organismes Nuisibles, FREDON)  is acknowledged. Hugh Jones  ( NHM, London, UK ) kindly provided unpublished information about records in the UK, Ireland and  Madeira.
The CALM2 variant (p. Gly114Arg, G114R; c. 340G>A) was present in the mother and the two female children (Child 3 and Child 4) (Figure  2A  and C and Supplementary material, pp. 8, 19, Figure  S1 , and Table  S4 ). DNA from the mother's parents was not available. Neither Sanger sequencing nor WGS/WES of the mother's saliva and buccal swab samples was suggestive of mosaicism for CALM2 G114R [saliva: 76 reference (C): 84 variant (T) reads; buccal swab: 106 reference (C): 104 variant (T) reads] and a blood sample could not be obtained. Sanger sequencing could not be performed on Child 3 and Child 4 due to lack of access to additional DNA. The CALM2 G114R variant is not found in databases of human genetic variation (ExAC, gnomAD, 1000 genomes, ClinVar). We have not observed the variant in 2580 in-house genomes sequenced at the Australian National University or in genomes from 2485 elderly Australians (MGRB).
Acknowledgements We are grateful to  Luca Sala  for constructive criticism and  Stephen Cordner  for providing the cardiac photomicrographs. We acknowledge  A ´ngela Romero Martı  ´nez for help with cataloguing relevant susceptibility genes from the literature,  Rong Liang  and  Mingri Liu  from  APF NGS  team for technical assistant in DNA extraction,  WES  and Sanger sequencing as well as the  ACRF Biomolecular Resource Facility, JCSMR, ANU  for NovaSeq 6000, and Sanger sequencing run.
Outcomes: cerebral palsy and death The primary outcome was CP, a disease due to permanent movement and posture disturbances resultingfrom a nonprogressive lesion of the developing brain. Itwas defined according to the diagnostic criteria of the Surveillance of Cerebral Palsy in Europe network. (3) Data for children at 2 years of corrected age were collected with a standardized questionnaire completed by the referring physician. The secondary outcome was a composite outcome, "CP or death",which included infants who died before the ageof two yearsand those who presented CP at that age, to take the potential competitive risk between these outcomes into account.
The imputation model included variables predicting nonresponse and/orcorrelated with outcomes (maternal, obstetric, and neonatal characteristics, severe IVH, cystic periventricular leukomalacia, bronchopulmonary dysplasia, necrotizing enterocolitis, early-and late-onset sepsis, postnatal corticotherapy, physiotherapy,blindness, and deafness at two years); 3) an analysis after multiple imputations for missing data for all covariates but not for CP,with a logistic regression imputation model for binary variables. Missing data were imputed by chained equationswith the R package "mice"v2. 25. Conversely, placental examination was missing for 34. 7% of cases. These data were not imputed because they were not missing at random (Table  II ; online only). P-values<. 05were considered statistically significant.
The  EPIPAGE-2 Study  was supported by the  French Institute of Public Health Research/Institute of Public Health  and its partners the  French Health Ministry , the  National Institute of Health and Medical Research , the  National Institute of Cancer , and the  National Solidarity Fund for Autonomy ; grant  ANR-11-EQPX-0038  from the  National Research Agency  through the  French Equipex Program of Investments  in the Future; and the  PremUp Foundation.
Review or Query Strategy A query on PubMed for the keywords of "chronic orchialgia", "chronic testicular pain", "testicular pain" and "orchialgia" was performed on April 1st, 2020. This query resulted in 2399 articles. These articles were then reviewed, and 61 were selected based on the following selection criteria: 1) study had human research review board approval, 2) presented an evidence-based treatment option for CSCP, and 3) presented an evidence-based assessment of CSCP. All data reviewed had human research review board approval.
Introduction Recent advancements in nondestructive testing (NDT) have made safety inspection in civil engineering more effective and precise than ever. So far, there are many types of NDT devices for structural health monitoring (SHM), mainly including infrared thermography, ultrasonic testing, ground-penetrating radar (GPR), and industrial radiography. Compared with other techniques for SHM, GPR is considered as one of the most powerful because of its desirable reliability and effectiveness. The increasing precision of GPR data encourages the research community to exploit this richer data for solving several SHM tasks, such as defect recognition, location, even 3D reconstruction. Fig. 1  presents a generic pipeline for processing a group of GPR data. Structure Scan data contain thousands of signals and/or points.
Precision of GPR devices has been improved remarkably with the development of measurement technologies  [7]. However, errors from feature extractors and nonlinear mapping algorithms are still inevitable, owing to their shallow structures. Until recently, the breakthrough from the work of Krizhevsky et al. [8]  tremendously changed the landscape of the GPR detection in civil engineering. Deep learning (DL) models, especially deep neural networks, now dominate on almost defect detection tasks using GPR devices, leading many NDT groups to redesign their systems. Although the concept of neural network has been proposed for a long time, the evolution of general processor units and the availability of large datasets make the main contribution to its recent tremendous success  [9, 10]. In pace with the dominance establishment of DL in 1D and 2D data processing, it was soon adopted to combine with GPR techniques for SHM tasks.
Motivated by this evolution, this paper surveys the main studies and presents an overview of existing DL models for civil engineering inspection tasks via GPR. Section 2 provides related works of GPR technologies in civil engineering to make the paper more self-contained, followed by a introduction to the conception of deep learning and the architectures used so far in the problems of GPR data processing in Section 3. Afterward, the advances of DL with GPR as the main body of this paper is presented in Section 4. Finally, conclusions are discussed in Section 5. Fig. 1. Generic pipeline for processing a group of GPR data.
In the work of Tong et al. [34] , a cascade CNN was proposed to recognize pavement subgrade defects using cropped GPR images. A cascade connection was used to distinguish low-resolution images from highresolution ones. The low-and high-solution images were classified by two different CNNs. The two CNNs were trained by the low-and high-resolution datasets, respectively. The experiment results indicated the strategy using a cascade connection improved the robustness of defect recognition in low-resolution images obtained at low transmitting frequencies, though this problem was still not solved well. A deep learning-based architecture, called deep dictionary learning, was proposed to detect buried objects  [93]. Each basic dictionary deep learning model was designed to calculate a Euclidean distance between a pattern and a dictionary, then all of the distances were used as representations for classification.
The experiment results demonstrated that the Faster R-CNN had the desirable performance on extracting ROIs from the GPR B-scan autonomously and efficiently, which had the potential in the analysis of synthetic and on-site GPR data sets. Xu et al. [100]  improved the Faster R-CNN framework by feature cascade, adversarial spatial dropout network, and soft-nonmaximum suppression for the railway subgrade defect detection. Feature cascade means that the low-, middle-, and high-level representations are combined to form new multisized features. It has been proved useful for detecting small objects  [101]. The adversarial spatial dropout network can be considered as a learning strategy for generating hard positive samples to reduce the unbalance in the B-scans dataset. In the soft-nonmaximum suppression, the confidence levels of bounding boxes are reduced according to their overlapping area instead of directly suppressing the boxes whose confidence levels are higher than a threshold.
The detecting results showed that the improved Faster R-CNN achieved an mAP of 83. 6% for subgrade defect detection, which was higher than the mAP of the baseline Faster R-CNN. In addition, a comparison study demonstrated the superiority of the proposed model on the robustness to the baseline Faster R-CNN thanks to the three improvements. In the work of Pham et al. [102] , the success of a Faster R-CNN on buried objects detection using GPR images is also reported. In general, we find that the data-driven algorithms for regionbased models outperform the rule-driven algorithms. This is because the rules provided by humans for ROI extraction are always not as complete as the knowledge summarized from a big dataset by a data-driven algorithms. Unfortunately, as the work principles of data-driven algorithms (e.
Overview and comparison of the DL architectures In the past decade, DL models have been designed and successfully applied to three types of GPR data. In order to further understand these models, the works described in Section 4. 1-4. 3 are compared using a pavement GPR dataset. The dataset was collected from four highways in China using two transmitting frequencies 300 MHz and 1. 2 GHz. Two types of pavement defects (cracks and uneven settlements) are labeled. Complete information can be found in Data Availability. The comparison study only presents a fair competition of these DL architectures in the pavement defect detection. More works can be performed in the future to compare the performance of these DL architectures in the entire field of civil engineering. Table  1  summarizes typical cited works, classifies them into the types of the input data, and presents the DL architectures along with some necessary details.