dataesr/scientific-paragraphs-categorization huggingface dataset:
Public databases with sequence data from diverse subtypes would facilitate the design of robust methods for these genotypes. Additionally, the availability of a standardized panel of HCV samples of diverse genotypic and global origin for method development, particularly for genotypes 4-6, would greatly assist in this endeavor and should be a priority. Most studies reported the binding location of all primers in addition to the primer sequence; however, some did not report the primer binding location. Due to the genetic variation of HCV, it was not possible to determine the binding location of all primer sequences that were not reported in the original publication. This issue was particularly exacerbated for primers specific to non-genotype 1 or 3 and highlights the need for authors to properly identify the binding location of primers in their associated publications.
Future Perspectives We have identified eight methods that are candidates for further evaluation and validation and may be useful for standardizing DAA RAS testing. Further method development is still required for the assessment of RASs in HCV genotypes 4-6, particularly in the NS5A region. The HCV Resistance Working Group, led by the HCV Forum, has proposed creating a standardized panel of HCV samples of diverse genotypic and global origins to be available for HCV diagnostic and method development. This effort would be particularly useful for the development of methods to detect RASs suitable for genotypes 4-6, and the panel could also be used for quality assurance programs, not only for RAS testing, but also for genotyping and viral load testing. Global efforts are underway to create a collaborative database of HCV sequence and clinical data for the monitoring of DAA RASs, which will further inform the clinical relevance of RAS testing into the future.
Acknowledgment: Unpublished HCV sequencing primers and methodologies were contributed to this review by  Johan Lennerstrand ,  Nader Kameli ,  Federico Garcia ,  Ana-Bel  en P erez,  Natalia Chueca ,  Janke Schinkel , and  Francesca Ceccherini-Silberstein. Monica Parker  provided advice on the assessment of laboratory-developed molecular tests.
New Tools to Accelerate Network Characterisation Machine learning offers huge potential for reconstructing ecological networks from available data. Statistical inference and logic-based machine learning approaches have been widely used in the social sciences and for molecular and genetic interaction networks, but have only recently been applied in ecology (Box 1). The idea behind these machine-learning methods is simple; embedded in a dataset is the imprint of the recent processes and interactions that created the data, and this information can be recovered to reconstruct networks. In an ecological dataset, variation is habitually used by ecologists to test hypotheses about past reproduction, migration and predation, for example using statistical regression. For machine learning, the use of the variation in the data is extended to state that these 'ghosts of interactions past' can be recovered to reconstruct ecological networks of interaction  [7].
Box 1. Machine Learning Ecological Network Structure The aim of reconstructing ecological networks is to recover the biotic interactions (e. g. , competition, parasitism, mutualism) that structure communities from the observed variation in sample data. This can be done from ecological time series that record the temporal variation of ecological communities or from the spatial variation in species occurrence or abundance, including time-resolved  [45] [46] [47]  and spatially-resolved  [48] [49] [50]  metagenetic datasets. The learning uses two forms of approach to recover biotic interactionsstatistical inference and logical machine learningthat constitute two independent and complementary lines of research. It is not yet clear which of these approaches will be most suited to biomonitoring ecological networks [ 2 6 3 _ T D $ D I F F ] [7,51].
What is clear is that cloud-based learning, based on either of these methods, is possible and already in use. [ 2 6 4 _ T D $ D I F F ] Learning approaches have for instance been used to develop language translations tools such as Google Translate. The underlying hypothesis of network reconstruction is that biotic interactions produce correlations and relational patterns in the abundance of species. In the case of statistical inference, the variation in the sample is treated statistically. Significant correlations between the abundances of any two species within the dataset are considered as potential network edges. The challenge is to remove edges that are not likely to be caused by biotic interactions, by integrating background [ 2 6 5 _ T D $ D I F F ] information such as environmental factors and species functional traits as covariates into the statistical model  [7, 36, 52, 53].
Logical machine learning considers relational patterns rather like the structure of grammar in a language  [54] [55] [56]. The grammar of a trophic interaction, for example, can be coded as background [ 2 6 6 _ T D $ D I F F ] informationin the agro-ecological network the predator and prey species must co-occur in the same samples, the predator should have appropriate mouthparts and predators should be larger than their prey. Trophic interactions between two species are only identified if this grammar rule is realized. For both statistical inference and logical approaches, such background knowledge can come from our current ecological knowledge or be learnt from observed data. Consequently, it is necessary to take care in choosing the learning method with a particular dataset, and to take into account the biases in all sample data; including taxa biases, identification errors, zero-rich data, and non-normal abundance distributions.
When selected correctly, however, we find that machine learning methods have great power to rapidly reconstruct networks from ecological sample data, in principle permitting their use in biomonitoring. Reconstructing a Large Agro-[ 2 7 0 _ T D $ D I F F ] Ecological Network To reconstruct a replicated, invertebrate food web from a large herbicide treatment dataset  [24, 25] , Bohan et al. [26]  first 'exposed' trophic interactions by transforming their data of species abundances to logarithmic treatment-ratios across two levels of an herbicide treatment. Their thinking was that following an application of herbicide, weed plants that provide refuge/food resources to a prey, y, would die. y would then either move or die, affecting the ratio. A predator of y, x, would in turn move or die and it might therefore be hypothesised that species undergoing trophic interactions would have correlated changes in their treatment ratios.
The reconstructed microbial networks often did not correspond well to those already understood. There is, therefore, room for improvement in statistical techniques of network reconstruction from NGS data  [34]. However, in learning new structure in plankton networks, Lima-Mendez et al. [35]  provide great encouragement that learning approaches can recover network information from NGS datasets. Case Study -Reconstructing Networks from NGS Data Jakuschkin et al. [36]  have recently attempted this for microbes on the leaves of oak trees (Quercus robur) to identify potential antagonists of the causal agent of powdery-mildew, Erysiphe alphitoides. From DNA sampled from leaves with differing levels of symptoms, NGS identified OTUs that were used to generate a simple co-occurrence network  [7]  (Figure  3A [ 2 7 3 _ T D $ D I F F ] ), which revealed E.
alphitoides to have numerous positive and negative associations that alter The food web was learnt using the logical machine learning methods, as described in the text for the agro-ecological example and in Box 1. In place of learning species identity, here a functionally-based description of the data was adopted. The thickness of the trophic links represents the estimated frequency that a link was found in the data. The pictures for each functional node represents an archetypal species for that ecological function. Figure reproduced with permission from  [28]. the composition of the invaded community. The key question was whether these association links are functional and predictive of invasion and disease formation. Given there is no established background information in microbial network reconstruction for determining functional links, it was proposed that most of the co-occurrence links should be explained by environmental requirements shared between OTUs, with functional interactions persisting once the environmental correlations were accounted for  [36].
Scientific Challenges The NGS case study and the work of Lima-Mendez et al. [35]  demonstrate clearly that NGS and learning approaches can generate hypotheses for ecological interactions, some of which have been validated  [35]. Most importantly, however, this network proof of concept shows how rich biomonitoring would give insight into the ecosystem-wide biotic response to stressors. For learning methodologies to work well, they need data in the appropriate form and require guidance from background information that may not be readily available. For the agro-ecological learning example, the data were transformed to a logarithmic ratio of abundance, while for the microbial network the OTU data came from across a gradient of infection. These learning datasets highlighted explicit contrasts, across either treatments or along gradients, in which the data structure exposed the linkages between the nodes of the network.
NGS data uses differences across sequences to provide an estimate of taxonomic or functional diversity  [66] [67] [68] , and the number of identical sequences may also estimate relative abundance  [69]. This is susceptible to primer biases, where the primers used preferentially amplify some OTUs at the expense of others. This bias can either be reduced via appropriate molecular approaches  [70, 71]  or quantified to identify primers with no evidence of bias  [65]. Furthermore, issues with sequencing errors, noise and statistical artefacts of the data (e. g. , zero-rich data, non-normal distributions) have all been studied at length and appropriate bioinformatics approach exist to deal with these  [66]. A major challenge is identifying the taxa and/or functions from the different DNA sequences present, by comparison with NGS sequence databases.
While 16S rRNA gene databases are well populated with robust information, other gene databases can contain incorrectly assigned sequences or are missing data on entire taxonomic groups  [43]. Thus, a significant global effort is required to provide robust, well-curated and maintained sequence databases. Each node represents a microbial taxon (either bacterial or fungal) and each link represents a significant correlation between their abundances. Red and green links indicate coexclusions and coassociations, respectively. The arrow indicates the node with the highest degree (i. e. , the highest total number of links). Degree decreases clockwise, with nodes stacked on the same line having the same degree. The size of the nodes is inversely proportional to the sum of the correlation coefficients: larger nodes have more numbers and/or stronger negative associations.
An embedded processor would then manage the process of sampling, sequencing, and uploading of sequence data, and a battery and solar panels would supply electrical power. Less complex hand-held samplers might fill gaps in the global array or provide information for specific end-users. For example, farmers could sample their fields for crop pathogens or medical technicians could sample for the presence and evolution of all diseases, much as is currently being done using NGS for Ebola alone  [42]. Drones might also be used to more cheaply collect and return samples to the laboratory for processing. Importantly, whether from a hand-held, drone or remote sampler, the sequence data should be of the same quality, sharing common date and location stamping. The uploaded data would be stored in a cloud database, where it would be collated and checked prior to being linked with online sequence databases to automate detection of the OTUs or functions present.
Finally, machine-learning would reconstruct the networks implicit in the sample data and determine potential changes in structure in real time. It is only at this point, once change in an ecosystem has been detected, that humans would explicitly enter the NGB process to interpret the ecology. This infrastructure would not need to be created anew for NGB: very large are already curated in the cloud and machine-learning approaches serve information to end-users via the internet. The proof that much of this technology exists right now is in our pockets. Smartphones combine embedded processing, GPS, and communications, and communicate with online machine learning structures, such as Google Translate iii and Apple Siri iv. The largescale storage and processing needed for NGB could 'piggy-back' on the existing infrastructure used in remote sensing.
The second technological barrier is the quality of current OTU databases. While certain gene databases are reasonably well populated and can return robust information, the coverage of all genes and taxa is still incomplete and databases contain incorrectly assigned sequences or omit entire taxonomic groups  [43]. A significant, global effort is required to provide robust, wellcurated and maintained sequence databases for use in NGB; fortunately, this is already underway for many taxonomic groups (e. g. , International Barcode of Life v ). NGB could accelerate this process by providing the 'big picture' impetus for generating shared sequence databases that would transform NGS, but which, because of time and cost constraints, are still lacking. The third technological barrier is the lack of a statistical framework for sampling using networks.
Conclusion: Potential Benefits of NGB The costs of building an NGB array will be considerable, but much of the infrastructure and technology already exists and the price of the sampler and sampling will likely fall with economies of scale. As the system is largely autonomous, humans only enter the biomonitoring process once the data are acquired, thus removing the high fixed costs of labour-intensive biomonitoring. Real-time NGB would be far more sensitive than current approaches, and the window for identifying when a stressor first elicits a response could be greatly foreshortened. The approach solves the three key problems, of accuracy, costs and generality of current approaches to biomonitoring. By broadening biomonitoring from single indicators, the approach enrichens the evaluation of change in ecosystem structure and function.
The costs of time and effort in reconstructing networks and, as a consequence, detecting change in an ecosystem are Outstanding Questions Scientific challenges: The scientific challenges in NGB lie in: (i) understanding and comparing the different methods of learning, so that the appropriate method may be selected for a particular learning setting; (ii) developing an understanding of how to appropriately treat NGS datasets so that the interaction linkages to be discovered are best 'exposed' for learning; (iii) testing of current models for network structure as background knowledge for generic reconstruction of ecological networks from known and unknown ecosystems; and (iv) research to refine our current understanding of how network and ecosystem structure lead to function, for interpretation of ecosystem change within the current biomonitoring framework.
Moreover, this large-scale biomonitoring will likely work well with existing large-scale monitoring approaches, such as remote sensing of the Earth's environments. Traditionally, there has been relatively little exchange and cross-fertilization between the disciplines of biomonitoring and ecology. A shift towards a large-scale biomonitoring approach that measures change in terms of network structure would provide richer, more ecological information that is moreover comparable between ecosystems. NGB data would add to our knowledge explicitly and foster a revolution in ecological understanding of ecosystem change.
Acknowledgements Dave Bohan  and  Corinne Vacher  receive support from  INRA   MEM  project  Learn-Biocontrol [ 2 7 5 _  T D $ D I F F ]. Support  is also provided to  Dave Bohan  from the  [ 2 7 6 _ T D $ D I F F ] ANR FACCE JPI SURPLUS PREAR project[ 2 7 7 _ T D $ D I F F ]. Alireza Tamaddoni-Nezhad ,  Alex Dumbrell  and  Guy Woodward  are funded by the  [ 2 7 8 _ T D $ D I F F ] NE/M020843/1  and  NE/M02086X/1. We acknowledge past support from the [ 2 7 9  _ T D $ D I F F ] Imperial College -Syngenta Ltd UIC [ 2 8 0 _ T D $ D I F F ].
Acknowledgments. We are grateful to all the participants who made this study possible. We also thank the  Rajanagatindra Tropical Disease International Center  staff for color blindness assessment, and  Dr. Yutthapong Imsuwan , the ophthalmologist at  Phramongkutklao Hospital, Bangkok, Thailand , for color blindness evaluation. The study received financial support from  Mahidol University , the  Royal Golden Jubilee PhD Program scholarship of the Thailand Research Fund (TRF) , the  Franco-Thai scholarship , and the  Mahidol-Oxford Tropical Medicine Research Unit  supported by the  Wellcome Trust of Great Britain.
Acknowledgements This work was sponsored by the statutory activities of the  Department of Physics and Biophysics of Wrocław University of Environmental and Life Sciences. Martin Hof  and  Teresa Kral  acknowledge support from the  Czech Science Foundation  via Grant  P208/12/G016  and the  Premium Academia Award (Academy of Sciences of the Czech Republic). The use of a DSC in the  Institute of Agricultural Engineering of Wrocław University of Environmental and Life Sciences  is gratefully acknowledged.
Background: Patients with hereditary angioedema (HAE) due to C1-inhibitor deficiency (C1-INH-HAE) experience recurrent attacks of cutaneous or submucosal edema that may be frequent and severe; prophylactic treatments can be prescribed to prevent attacks. However, despite the use of long-term prophylaxis (LTP), breakthrough attacks are known to occur. We used data from the Icatibant Outcome Survey (IOS) to evaluate the characteristics of breakthrough attacks and the effectiveness of icatibant as a treatment option. Methods: Data on LTP use, attacks, and treatments were recorded. Attack characteristics, treatment characteristics, and outcomes (time to treatment, time to resolution, and duration of attack) were compared for attacks that occurred with versus without LTP. Results: Data on 3228 icatibant-treated attacks from 448 patients with C1-INH-HAE were analyzed; 30.
The characteristics of attacks occurring during LTP, referred to here as breakthrough attacks, have not been formally examined in depth. Furthermore, there are currently no reports evaluating treatments for breakthrough attacks in detail. Icatibant (Firazyr ® , Shire, Zug, Switzerland) is a subcutaneously administered bradykinin B2 receptor antagonist for the treatment of acute angioedema attacks in adults with type 1 or 2 C1-INH-HAE. The Icatibant Outcome Survey (IOS; NCT01034969) is an ongoing, prospective, international, observational study monitoring the safety and effectiveness of icatibant during longterm treatment in the real-world setting. We conducted an analysis of data from IOS to evaluate the characteristics of breakthrough attacks, and compared them with attacks that did not occur during LTP.
Study design and patients Details for the design and conduct of IOS are described elsewhere  [12]. IOS captures real-world data on treatment with icatibant. Enrollment in IOS is open to all patients who have taken at least one dose of icatibant, regardless of attack frequency and level of icatibant usage. Patients with diagnosed C1-INH-HAE were included in this analysis  [1]. Data on icatibant-treated attacks and LTP use were collected between July 2009 and February 2016 via physician-completed electronic forms at routine visits (recommended at 6-month intervals), and with the aid of patient diaries. Characteristics of attacks and details regarding treatment with icatibant and any concomitant medications were recorded. Data on attacks and treatments during the 12 months before enrollment are referred to as historical.
Discussion Prophylactic treatment for C1-INH-HAE has been shown to reduce the frequency and severity of attacks  [7, 14, 15]. The present analyses of real-world data from IOS support that patients receiving LTP (who usually have high disease activity) experience attacks of similar duration and frequency as patients who do not use LTP. Our findings show that icatibant, when used as on-demand medication, is similarly effective in controlling attacks in patients who are taking LTP and in those who are not. In our dataset, patients across all LTP groups experienced breakthrough attacks, including those that involved the larynx and those that significantly impaired physical function. Breakthrough attacks may have occurred because local C1-INH levels fell below or did not reach the normal range despite administration of LTP, resulting in inadequate regulation of the contact system  [1].
The goal of this analysis was to evaluate the effectiveness of icatibant in treating breakthrough attacks, rather than to compare different LTP options. Data confirming disease severity prior to initiation of prophylactic treatment were also not available as a baseline comparison. In addition, the C1-INH treatment group was relatively small. Thus, these data cannot be used to evaluate the effectiveness of LTP or to compare the various LTP treatments. The small number of C1-INH users in the database population does reflect our clinical experience in that very few patients use C1-INH at the recommended dose of 1000 U every 3-4 days; instead, doses and regimens are often adjusted according to individual patients' responses. Thus it is difficult to fully assess the impact of C1-INH in comparison to other LTP treatments.
Acknowledgements S. A. F. is indebted to the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  636069 ). We further gratefully acknowledge funding from the  Austrian Federal Ministry of Economy, Family and Youth  and the  Austrian National Foundation for Research, Technology and Development  and initial funding from the  Austrian Science Fund (FWF , Project No  P26870-N19 ). The authors thank  R. Saf  for help with the MS,  R. Breinbauer  for discussions about the reaction mechanism,  S. Landgraf  for help with the NIR measurement, and  J. Schlegl  for manufacturing instrumentation for the methods used.
Molecular docking CK2 protein structures were modeled and ligand binding studies were carried out using SwissDock (http://www. swissdock. ch/) web service developed by the Molecular Modeling group of the Swiss Institute of Bioinformatics (Lausanne, Switzerland). The modeled and docked structures were analyzed with UCSF Chimera 1. 12rc software. The binding site of the structures was identified using the crystal structure of CK2α (PDB code 1PJK), CK2α′ (PDB code 3OFM), and holoenzyme CK2α 2 β 2 (1JWH). The docked structures showed binding energy in the range of -7. 49 to -8. 48 kcal/mol.
This work was supported by  Coordenacão de Aperfeicoamento de Pessoal de Nı ´vel Superior-CAPES-COFECUB  (grant  799-14 ), the  Institut Pasteur , the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases " (grant  ANR-10-LABX-62-IBEID ), and the  FAPERJ-Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de the Americas  is the mosquito Aedes aegypti. , or preparation of the manuscript.