dataesr/scientific-paragraphs-categorization huggingface dataset:
However, harmonization should run in parallel with regulatory implementation to avoid delay and allow cross-fertilization during the process. As procedures and tools for NTS are still under development, standardization with strict requirements should, if at all, be considered when more experiences with NTS have been accomplished and the results have been evaluated for their accuracy and precision. A harmonized framework that defines the level of confidence with which a compound is identified is crucial to allow effective communication among users via the literature and databases. Today, the confidence of identification is often communicated by a system with the following five identification levels: "certain identification" with a standard (level 1); "probable identification" with unambiguous match of MS library spectrum (level 2A); "probable identification" based on diagnostic evidence, but not confirmed by standard or literature information (level 2B); "tentative identification of a structure" where multiple structures are possible (level 3); "unequivocal molecular formula", certain molecular formula but no structure (level 4) and finally only a "measured exact mass of interest" (level 5)  [25].
Future needs: infrastructures and databases (item 4) A prerequisite for NTS is not only the high resolution mass spectrometry technology but also the sharing of information. Here, future needs are expected to be focused on data sharing through dissemination of chromatograms, spectra or suspect lists. Large platforms and server space will be needed for archiving data because single measurement files often exceed one gigabyte in size. Open digital repositories for retrospective queries of analytical data from various sample locations and time points could bring an enormous additional benefit, first for prioritization of ubiquitous compounds but also for comparison of data from different laboratories, e. g. along a river system. Data sharing will also open the possibility for joint evaluation initiatives where NTS experts could support less experienced laboratories either by offering advice or by evaluating data of other laboratories with less resources to invest in NTS.
IPCHEM has been built as a European-wide access point for searching, accessing and retrieving chemical occurrence of monitored chemicals in various media, but it was not designed to cover NTS data. In conclusion, digitalisation and development of "big-data" tools is increasingly important and offers great opportunities. European and national institutions together with data science experts should discuss how to benefit from the digitalisation and how to tackle the challenges associated with large data volumes, data protection and data security. In this context, it would be beneficial if central repositories could be hosted by public institutions within Europe to allow for high interoperability among the various repositories. In comparison to sample data sharing, dissemination of compound-related data (such as MS/MS spectra) through open-access infrastructures has started much earlier.
Distribution of compound-related data is generally easier because the volume of data is smaller (only the spectra of reference compounds are shared for the identification process) and no information on environmental samples is provided. In 2011, an open-access repository for MS (mainly high resolution) spectra of substances present in the environment was established in Europe, using the format previously developed in Japan  [31]. NORMAN MassB ank now contains over 50,000 mass spectra of more than 15,000 substances from 15 main instrument types and 32 institutions. The spectra search is a very important component for filtering suspect and non-target hits. The database is intended to be extended to cover the majority of compounds on suspect lists and thus facilitate the elimination of false positives in the suspect screening process (as discussed in the section above).
MassBank also allows researchers to upload the spectra of tentatively identified compounds and thus enables the tentative identification of relevant compounds in European-wide samples for which a reference standard is not available. Additionally, including the mass spectra of REACH compounds provided by industry would extend the relevance of such a database enormously. To properly compare and discuss the several thousands of chemicals present in NTS data, clear and harmonized identifiers are needed for all databases, especially identifiers that are suitable for incorporation in NTS workflows. Since there is no unique CAS number for a given chemical structure, InChI Keys are more widely used in the research community. This newer coding system should be considered by the regulatory bodies to facilitate searching for and identification of chemicals.
Future needs: training (item 5) Dissemination of knowledge will be important for NTS standardization. Training people to apply new analytical technologies reliably and to process and evaluate the large datasets reproducibly is an important task that facilitates the implementation of NTS in regulation and chemical management. It can be addressed in different ways. In the short term, training courses for employees of governmental and private laboratories could be offered by organizations such as NORMAN or other European or national societies. However, it has to be considered that the different HRMS instruments provide different raw data and the vendor software use different algorithms that result in different outputs. Therefore, such courses can give only a general overview on NTS workflows and quality control or use open-access software applicable to all kind of HRMS data.
Synergy of NTS with effect-based methods (item 6) Effect-based methods (EBM) have been thoroughly investigated in the SOLUTIONS project and were proposed as useful tools for prioritization, monitoring and chemical management approaches within European regulation  [33]. NTS and EBM provide complementary information. EBM can be applied to identify sites of poor environmental quality and trigger NTS as a tool to identify the responsible chemicals possibly after fractionation (effect-directed analysis) and subsequently plan appropriate mitigation measures. An unambiguous identification is not required in every case. For example, a fingerprint or pattern analysis together with EBM may be a sufficient proof to prompt mitigation measures. Larger datasets from NTS and EBM can be explored using multivariate statistics, pattern recognition and machine learning methods to identify peaks that co-vary with detected effects.
They illustrate this point dramatically with a comparison of formant 'breaks' in two spectrograms; we give a similar illustration in Figure  1  with a comparison of weak bursts drawn from our Campidanese materials. The spectrograms each show a VCV sequence. A researcher in the midst of classifying a large number of segments would probably classify the consonant on the left as having no burst (and possibly a 'break' in formants), and the one on the right as having a (weak) burst. The 'trick' here is that these are two spectrograms of the same token; the dynamic range setting in Praat  (Boersma & Weenink, 2018)  is the only difference between them. If the measurement of such qualitative phonetic features depends on software settings in addition to actual phonetic properties, one could doubt whether these features are appropriate for phonetic studies.
While there are very few studies of Sardinian prosody, there is some evidence that at least one level of constituent exists above the word but below the intonational phrase level  (Jones, 1993; Del Mar Vanrell et al. , 2015). The approach taken here is to compare consonants at the beginning and/or end of relatively large syntactic constituents with those at the edges of smaller constituents. Even if the relationship between syntax and prosody is not fully deterministic (and there is no reason to believe it is), this procedure may result in enough prosodic differences on average between structural positions to reveal prosodically-driven phonetic patterns. The results in Section 4 suggest that the strategy succeeded in this regard. Ennever et al. (2017)  have graciously made their code available on Github.
Semi-automated phonetic measurements The stop_lenition code is in R, and interfaces with Praat  (Boersma & Weenink, 2018). It takes as input a series of sound files paired with Praat textgrids that mark the origins of various consonants, the rough location of the transition between those consonants and preceding sounds. The script bandpasses each sound file according to parameters set by the user, extracts intensity data from each frequency band of each sound file, then attempts to segment each consonant and measure various types of intensity extrema, changes, and slopes. The segmentation routine first retrieves intensity data in the vicinity of each origin using forward and backward search windows specified by the user. It then fits smoothing splines to the intensity contour in R, where smoothing is more or less extreme according to the smoothing parameter selected by the user.
Acknowledgements Many thanks to  Bob Ladd  for extensive help with both linguistic and practical aspects of this research. Erich Round  shared his brilliant code, his expertise in helping to implement it for our materials, and some very useful comments on an earlier draft of this paper. Uriel Cohen  provided extensive and helpful discussion and comments on an earlier draft. Thanks as well to  Maria Cristina Lavinio  for logistic support, and  Bruce Hayes  for pointing the first author to Sardinian in the first place. This research has benefitted from discussion with  Edward Flemming ,  Haike Jacobs ,  John Kingston ,  Donca Steriade ,  Anne-Michelle Tessier , and  Maurizio Virdis. We are also very grateful to our Sardinian consultants for their time and hospitality.
, 2001)  and Legionella pneumophila  (Conover et al. , 2003) , and the type IV pilus of Pseudomonas aeruginosa  (Cowles and Gitai, 2010). It will thus be of great interest to investigate a possible conserved role for MreB in the positioning of virulence factors. To follow the localisation of MreB during actin-based motility, we infected HeLa cells stably producing LifeAct-iRFP670 (to visualise F-actin) with Shigella MreB-GFP sw for 2 h 10 min and performed time-lapse Airyscan Fast confocal microscopy. Here, we found that for 52. 6±3. 2% of bacteria polymerising actin tails, MreB can switch between accumulating at the cell pole and localising into patches along the cell cylinder (Fig. 2H ). These data suggest that MreB can rearrange back into patches after positioning IcsA for the induction of actin tail formation.
To test this, we determined the length and speed of actin tails formed by bacteria that exhibit polar MreB (exclusively), patchy MreB (exclusively) or switching MreB ( polar and patchy) during the imaging period. Here, we found that bacteria that exhibit patchy MreB have significantly longer actin tails and move faster, as compared to bacteria that exhibit polar MreB and switching MreB (Fig. 2I, J ). In the outer membrane, IcsA cleavage by the protease IcsP is well known to influence actin-based motility  (Shere et al. , 1997). We speculate that once actin tails are formed, MreB switching can help to replace IcsA at the cell pole after IcsA in the outer membrane has been cleaved by IcsP. Taken together, these data support a model in which polar accumulation of MreB helps to position IcsA to initiate actin tail formation but is not strictly required during actin-based motility.
3F, G ). When siRNA-treated cells were treated with A22, we observed the same amount of actin tails in SEPT7-depleted cells as in control cells (Fig. 3F, G ). These data demonstrate that A22-treated bacteria are not entrapped in septin cages to a greater extent than control cells, suggesting that their defect in actin tail formation is due to inhibition of MreB polymerisation. To explore the role of MreB polymerisation in actin-based motility, we performed time-lapse microscopy using LifeAct-mCherry-transfected HeLa cells infected with Shigella expressing a soluble GFP in untreated or A22-treated conditions. In this case, movement of the bacteria (i. e. both the linearity and speed of Shigella cells) is significantly reduced in the presence of A22 (Fig. 3H-J ; Movies 1 and 2). Therefore, we conclude that MreB polymerisation can promote IcsA polarisation, actin tail formation and efficient actin-based motility.
4 C-Plan Apo oil immersion lens on a fluorescenceinverted microscope Axiovert Z1 driven by ZEN software (Carl Zeiss) or on an LSM 880 (Carl Zeiss) in Airyscan super resolution (SR) mode driven by ZEN Black software. For time-lapse microscopy of infected HeLa cells, samples were imaged every 15 s in FluoroBrite (A1896701, Thermo Fisher Scientific) from 2 h 10 min after infection using a 63×/1. 4 C-Plan Apo oil immersion lens on an LSM 880 (Carl Zeiss) in Airyscan Fast SR mode driven by ZEN Black software (Carl Zeiss) (Fig. 2H-J ) or samples were imaged every 10 s in FluoroBrite containing 50 μg/ml gentamicin from 1 h 40 min after infection using a 63×/1. 4 C-Plan Apo oil immersion lens on a confocal microscope LSM 710 (Carl Zeiss) driven by ZEN 2010 software (Fig. 3H-J ).
Microscopy analysis Microscopy images were quantified from z-stack image series by taking 8 to 15 images over 0. 2-0. 4 μm. Image processing was performed using Fiji (ImageJ) or Icy (http://icy. bioimageanalysis. org) and deconvolution was performed using Huygens deconvolution software or ZEN software. Bacterial cell length and width were measured manually using the plugin 'Coli-inspector' for FIJI. The movement (i. e. linearity and speed) of bacteria polymerizing actin were measured using the motion profiler from Icy. For single-particle analysis  (Gray et al. , 2016) , the MreB-positive pole and long axis were manually selected. All bacterial cells were automatically aligned using these two references and the resulting stacks were averaged to create population-representative models. Metabolically active bacteria were quantified according to  Sirianni et al.
Funding We thank The  Company of Biologists for Travel fellowship  to  S. K. J. S. is funded by a  Royal Society   Dorothy Hodgkin Research Fellowship  ( DH140154 ). D. L. -M. is funded by the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. H2020-MSCA-IF-2016-752022. Work in the R. C. -L. laboratory was supported by a Starting Grant from the  European Research Council  ( ERC-StG-311231 ) and an international grant from the  Agence Nationale de la Recherche  ( French National Research Agency)  ( ANR-12-ISV3-0004-01 ). Work in the S. M. laboratory is supported by a  Wellcome Trust   Senior Research Fellowship  ( 206444/Z/17/Z ),  Wellcome Trust   Research Career Development Fellowship  ( WT097411MA ) and the  Lister Institute of Preventive Medicine.
Aim and scope: Artificial intelligence (AI) in medicine is a fast-growing field. The rise of deep learning algorithms, such as convolutional neural networks (CNNs), offers fascinating perspectives for the automation of medical image analysis. In this systematic review article, we screened the current literature and investigated the following question: ''Can deep learning algorithms for image recognition improve visual diagnosis in medicine?'' Materials and methods: We provide a systematic review of the articles using CNNs for medical image analysis, published in the medical literature before May 2019. Articles were screened based on the following items: type of image analysis approach (detection or classification), algorithm architecture, dataset used, training phase, test, comparison method (with specialists or other), results (accuracy, sensibility and specificity) and conclusion.
Results: We identified 352 articles in the PubMed database and excluded 327 items for which performance was not assessed (review articles) or for which tasks other than detection or classification, such as segmentation, were assessed. The 25 included papers were published from 2013 to 2019 and were related to a vast array of medical specialties. Authors were mostly from North America and Asia. Large amounts of qualitative medical images were necessary to train the CNNs, often resulting from international collaboration. The most common CNNs such as AlexNet and GoogleNet, designed for the analysis of natural images, proved their applicability to medical images. Conclusion: CNNs are not replacement solutions for medical doctors, but will contribute to optimize routine tasks and thus have a potential positive impact on our practice.
Artificial intelligence and image analysis Artificial intelligence (AI) in medicine is a fast-growing field, generating hopes and raising perplexing issues. AI can be defined as the ability for a computer to mimic the cognitive abilities of a human being. AI corresponds to a large array of techniques. Among them, machine learning is one of the most relevant approaches in the medical field. Three converging technical progresses have facilitated the medical applications of machine learning: the global rise of ''big data'', that is the constitution and the analysis of very large databases; the spectacular increase of the computing power of processors; the design of new deep learning algorithms. These technological advances allowed algorithms to process images and sounds, and be integrated into the digital tools of everyday life and into automated professional workflows.
Material and methods Here we provide a systematic review of the publications using CNN technology for medical image analysis, available in the National Library of Medicine database (PubMed). The search equation was the following: (convolutional OR deep learning) AND (classification OR detection) AND (image OR photography), filtered for ''Human studies'' and ''Title/Abstract'' as search fields. The selected articles were screened according to a standard grid containing the following items: aim of the study: detection or classification; methods: network architecture, dataset, training, validation, test, comparison method (with specialists or other); results: accuracy, sensibility and specificity and; conclusion. More precisely, within the methods section, the following data regarding network architecture were collected: pre-training (yes/no); network parameters; layers (convolution and pooling, fully connected and softmax) and; software.
Results We identified 352 articles in the PubMed database based on our search equation between 01/01/2013 and 05/20/2019. We excluded 327 articles where CNNs performance was not assessed (review articles) or where tasks other than detection or classification, such as segmentation, were assessed (Fig. 5 ). The 25 included papers were ordered according to medical specialties: dermatology  [12, 13] , ophthalmology  [14] [15] [16] [17] [18] , cardiothoracic imaging  [19] [20] [21] [22] [23] [24] , senology  [25] [26] [27] , oral and maxillofacial surgery  [28] [29] [30] [31] [32] , hepato-gastro-enterology  [33] [34] [35] [36]. Demographic data about the included papers and information on the journals in which the works were published are provided in Tables  1  and 2. These results are summarized in Figs.
6  and 7 , which showed a stable increase of the volume of publications on CNN performances in the last few years: 1 paper in 2013  [27] , 8 papers in 2016  [13, 15, 16, 19, 22, 25, 29, 33] , 6 papers in 2017  [12, 14, 20, 21, 26, 28] , 5 papers in 2018  [17, 23, 24, 30, 34]  and already 5 papers in 2019  [18, 31, 32, 35, 36]. Our results furthermore indicate that most of the papers were produced by North American and Asian teams: 12 papers were from the USA and Canada  [12] [13] [14] 16, 20, 21, 23, 24, 28, 30, 31, 34]  and 13 papers were written by Asian teams  [13, [16] [17] [18] 22, 26, [29] [30] [31] [32] [33] 35, 36]. Interestingly, international collaboration between Asian/South American teams and European/North American teams allowed compiling very large databases in 4 cases  [13, 16, 26, 31].
The assessment of the characteristics of the networks (Table  3 ) showed that most teams used previously developed codes such as GoogleNet  [12, 16, 17, 20, 21, 32, 36]  and AlexNet  [19, 21, 29, 33, 36]. Most of the 25 networks were pre-trained  [12, [16] [17] [18] [19] 21, [23] [24] [25] [30] [31] [32] [33] [34] [35] [36] , and most teams had included proofs of veracity for the labeling of the images, based on standard methods depending on the dataset used in the study, such as for instance histological confirmation  [12, 25, 27]  or assessment by 1-4 specialists (Table  4 )  [14] [15] [16] [18] [19] [20] [21] [22] 24, [28] [29] [30] 32, 34]. The quantity of included images (Table  5 ) was variable and was not very informative given the variety of the datasets: from 170 pictures of skins lesions used for melanoma detection  [13]  to 139,886 fundoscopy images used for diabetic retinopathy diagnosis  [16].
These networks easily run on softwares as ''Caffe''  [40] , ''Theano''  [41]  or ''Tensorflow''  [42]. All of these softwares are freely available online and are based on the widespread programing language ''Python''. The data used in the 25 studies included macroscopic and microscopic images of different types: clinical photographs  [12] [13] [14] [15] [16] [17] [18] [30] [31] [32] [33] [34] [35] [36] , pathology slides  [27] , X-rays  [20, 21, [23] [24] [25] [26] 28]  and CT images  [19, 22, 29]. Four criteria are often cited in the literature as crucial elements for the design of reliable networks and are referred to as the four ''V'': volume and variety. The training phase requires a significant number of images. Beyond the quantity, heterogeneous databases increase robustness. Augmentation techniques (rotating -cutting -resizing the images) are often used to increase volume and variety without decreasing the third V, veracity; veracity is conditioned by two quality criteria; image quality -resolution, limited variability due to angle of view, zoom or brightness -often increased by pre-treatment  [43] ; label quality, depending on the expertise of the practitioners who created the database.
Database review by a scientific committee is thus recommended before training; velocity, related to power of the processor. GPU, made of several parallel cores, are well fitted for CNNs, specifically during the training phase when a lot of images are simultaneously processed. CNN training is a key step, for which specific technical skills are required, in order to avoid over-fitting  [44]  on limited data, leading to issues for when using the network to analyze wider datasets. Training thus requires evaluation and monitoring. Transparency is a fundamental parameter in medical AI. CNNs are opaque structures often referred to as ''black boxes''. Some systems offer partial visualization techniques (heat-maps, probability maps) in order to provide some view of the inner functioning of the CNN.
Sustained observations Coordinated action is required to monitor changes in key environmental variables, species diversity, genetic diversity and species distributions. Europe has a rich historical legacy of studies documenting population demographics and biogeographic ranges across several degrees of latitude dating back to the mid-1800s (see Mieszkowska & Sugden 2016 for a review). In this context, the MarClim project can be used as an exemplar model study based on long-term monitoring. It was established in 2001 to rescue, archive and analyse historical datasets that were started in the 1950s  (Crisp & Southward 1958)  and establish a current time-series network of 100 sites covering England, Wales, Scotland and northern France. MarClim has been tracking the distribution and abundance of temperature-sensitive, climate indicator species and non-indigenous species of intertidal invertebrates and macroalgae in response to climatic fluctuations across half a century  (Mieszkowska et al.