dataesr/scientific-paragraphs-categorization huggingface dataset:
g. Consent), summary of the event, impact of the event, statement of the lesson learnt and action taken. Lessons are searchable by others using these key items. Each lesson has a blog, which all staff are encouraged to use to make comments, for example, to share similar experiences or suggest solutions. Lessons are reviewed by a core review team on a monthly basis. The review team record their recommendations, request further actions and/or disseminate findings to all research staff. A lesson is closed once all actions and dissemination have been completed. Reports can be printed from the database by the review team which, amongst other functions, provide metrics such as stage of study where lessons are reported, making it easier to identify recurring issues.
This includes pre-hospital care, intensive care (ICU) admissions and A&E attendances. Screening was performed by one author (CR) with duplicate screening of 10% of the database performed by a second author (KF). 4 1 MRC conduct-II Hub for Trials Methodology Research, School of Social & Community Medicine, University of Bristol; 2 Division of Surgery, Head & Neck, University Hospitals Bristol NHS Foundation Trust; 3 MRC North West Hub for Trials Methodology Research, Institute of Translational Medicine, University of Liverpool; 4 MRC conduct-II Hub for Trials Methodology Research, School of Social & Community Medicine, University of Bristol and Division of Surgery, Head & Neck, University Hospitals Bristol NHS Foundation Trust Correspondence: Ceri RowlandsBackgroundRecruitment to randomised controlled trials (RCTs) in unplanned hospital care (UHC) settings is more challenging than in the elective setting because there is less time and patients are often unwell.
However, while this focus has led to an increase in the quantity of published research, navigating this literature to identify recruitment strategies relevant to different types of trials has remained difficult. AimThe ORRCA project aims to provide an online searchable database, categorising recruitment research according to key themes. Data Sources An unrestricted search of Medline (Ovid), Scopus, Cochrane Database of Systematic Reviews (CDSR) and Cochrane Methodology Register, Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (Results Electronic searches identified over 40,000 articles of which 3979 required full text review. The online database (www. orrca. org. uk) launched in August 2016 and is being updated periodically. We anticipate it will contain over 2000 articles once the review process is completed towards the end of 2016.
Direct comparisons of such methods for trolled trial (RCT). Sample size calculations typically use published re- missing proms data in RCT settings are needed to ensure the bias intro- sults from similar trials, and we illustrate the different methods using duced in such analyses is minimised. published results from the MOSAIC trial. Objective To compare ML, MI and IPW approaches for handling missing longi- tudinal proms data in RCTs. Methods Real-life missing data following missing at random patterns were simulated within the follow-up of an RCT using the Oxford Knee Score. Datasets of sample sizes ranging from 100 to 1,000 with miss- ing proms outcome data in 10% to 40% of participants were simu- lated. Both intermittently missing data and monotone missing data patterns were considered. Missing data was addressed using ML, MI and IPW.
1, Xinxue Liu 2 , Charlotte Wilhelm-Benartzi 3 , Robert Brown 4 , Deborah Ashby 2 1 Imperial College London; 2 Imperial College London, School of Public Health, Imperial Clinical Trials Unit; 3 Cardiff University, College of Biomedical & Life Sciences, Centre for Trials Research; 4 Imperial College London, Department of Surgery & Cancer, Division of Cancer Correspondence: Meredith Martyn Trials 2017, 18(Suppl 1):P337 Background Clinical trials are valuable resources for biomarker exploration as prospectively collected data and experimental designs minimise bias. Models using multiple biomarkers to predict therapeutic re- sponse are of particular interest; however, the high-dimensional, small datasets come with challenges. This study aimed to high- light over-fitting issues with producing predictive models using commonly-used methods for Stratified Medicine in typical clinical trial datasets, using an ovarian cancer trial derived dataset as a case study.
Methods In a recently completed randomized clinical trial of acute treatment of patients with traumatic brain injury (protect NCT00822900), we retrospectively determined the coincidence of enrollment sites being outliers on both AE reporting and data corrections found by site monitoring. Outlier sites were those outside 95% boundaries on fun- nel plots of AE reporting and of data corrections. Variability in AE reporting was assessed by examining the average number of AEs reported at each site (the total number of AEs reported at a site di- vided by the number of subjects enrolled at that site). Data correc- tion at each site was assessed as the average number of data clarification requests (DCRs) written by a site monitor during source document verification visits that resulted in the site correcting erro- neous data in the case report form (CRF).
Brightfield images of a T-cell during activation (left), with the corresponding length of the part of the cell that is aspirated in the cell micropipette, L tail (see Figure  1B ). At time t = t tail , the tail started retracting inside the cell micropipette (red star). In this example, the retraction lasted ∼40 s and stopped at t∼70s (red #). Scale bar is 5 µm. (C) Comparison of timings. Two time points, t push and t tail , were measured from contact to the onset of mechanical changes (see A and B), and for activation of human primary CD4+ T-cells (resting) with anti-CD3/anti-CD28 beads, or only anti-CD3 beads, and for human CD4+ T lymphoblasts (preactivated) with anti-CD3/anti-CD28 beads. Each data point represents one cell, red thick line shows median, whiskers span the interquartile range.
Micropipette pulling and calibration Micropipettes used to hold the cells (cell micropipettes) were prepared as described previously  (Guillou et al. , 2016a) : borosilicate glass capillaries (1 mm OD, 0. 78 mm ID; Harvard Apparatus, Holliston, MA) were pulled with a P-97 micropipette puller (Sutter Instruments, Novato, CA). An MF-200 microforge (World Precision Instruments [WPI], Sarasota, FL) was used to cut the tip of pulled capillaries to the desired inner diameter (2. 5-3. 5 µm). The diameter was first assessed optically with calibrated graduations in the microforge's (Mathworks, Natick, MA) code calling the Micromanager software. The gray levels along a line parallel to the x-axis intersecting the micropipette were retrieved at acquisition frequency of ∼300-500 Hz (depending on the size of the acquisition window).
Profile microindentation Profile microindentation was performed as described previously  (Guillou et al. , 2016a). Briefly, the setup consisted of a cell micropipette, a microindenter (see above in micropipette pulling), and a rigid bead micropipette coming at a right angle (Figure  1C ). Microindentation algorithm used the same method as in  Guillou et al. (2016a) , except that the software part was now implemented in Matlab to control directly the MicroManager software and obtain a higher acquisition frequency.
Data analysis From all the cells recorded in the micropipette experiments, we excluded nonresponding cells, which did not show any activity after the contact with the bead, and cells dead during recording, which retract the tail very fast, visibly grow larger in diameter and show no activity afterward, presumably because they lose membrane integrity. Across all the experiments with anti-CD3/anti-CD28 and anti-CD3 beads we recorded 486 cells from 10 different donors, of which 84 cells were nonresponding, 26 cells were dead during recording, and 376, or 77% of all cells, were active and were analyzed. Morphological parameters of cells during activation (the diameter and the length of the punch, cup angle) were measured manually in the microscopy images, with the ImageJ software  (Schneider et al.
Optical microscopy A glass-bottom Petri dish (Fluorodish; WPI) containing cells and activating beads was mounted on an inverted microscope (Nikon Eclipse Ti-U; Nikon Instruments, Tokyo, Japan) placed on an air table (Newport, Irvine, CA). The microscope was equipped with a 100× oil immersion, 1. 3 NA objective (Nikon) for monitoring the experiments and lower magnification objectives (40×, 20×, 10×, and 4×; Nikon) for micropipette positioning. Images were acquired with an ORCA-Flash 4. 0 complementary metal oxide semiconductor camera (Hamamatsu Photonics, Hamamatsu City, Japan) controlled with the Micro Manager software  (Edelstein et al. , 2014). The micropipettes were attached to two motorized micropositioners  (Sensapex, Oulu, Finland). Beads were aspirated into the tip of the calibrated micropipette by capillarity forces with no aspiration pressure added.
Micropositioning and detection The bead was pushed or pulled by the cell along the x-axis. The position of the bead, x bead , is the same as the position of the tip of the bead micropipette. To track it, we developed a custom Matlab return, and pulling phases and fitted a line for every slope. As the early experiments were performed with low sampling frequency (x bead noted only once every 2 s, in the full image), the high-sampling-frequency data were appropriately averaged to consistently fit the v push and v return for all experiments that are shown in Figures  2E  and 3 , B and C. Maximal pushing and pulling force was taken from the x bead (t) chart, as shown in the Figure  3A. Buckling force was also taken from the chart, and the step in the pushing slope was verified with the corresponding frame in the image sequence, to check whether it shows the described buckling morphology.
ACKNOWLEDGMENTS We thank  Virginie Bazin  ( IBPS, Université Pierre et Marie Curie, Paris ) for her help in obtaining scanning electron microscopy images. We acknowledge  Caroline Frot ,  Antoine Garcia ,  Daniel Guy ,  Delphine L'Huillier ,  Sandrine Laguerre ,  Thérèse Lescuyer ,  Magali Tutou , and  Do Chi Toai Vu , at  LadHyX , for technical support. We also thank  Morgan Huse  and  Audrey Le Floc 'h for fruitful discussions and  Pierre-Henri Puech  for critical reading of the manuscript. This work has benefited from the financial support of the  LabeX LaSIPS  ( ANR-10-LABX-0040-LaSIPS ) managed by the  French National Research Agency  under the " Investissements d'avenir"program  ( ANR-11-IDEX-0003-02 ). This work was also supported by  GEFLUC Paris-Ile-de-France funding , a CNRS PEPS funding, an endowment in cardiovascular cellular engineering from the  AXA Research Fund , and funds from the  DC-BiolLabex  ( ANR-10-IDEX-0001-02 PSL*  and  ANR-11-LABX-0043 ) and from the  Fondation pour la Recherche Médicale  ( FRM DEQ20140329513 ).
In this paper, we extend some usual techniques of classification resulting from a large-scale data-mining and network approach. This new technology, which in particular is designed to be suitable to big data, is used to construct an open consolidated database from raw data on 4 million patents taken from the US patent office from 1976 onward. To build the pattern network, not only do we look at each patent title, but we also examine their full abstract and extract the relevant keywords accordingly. We refer to this classification as semantic approach in contrast with the more common technological approach which consists in taking the topology when considering US Patent office technological classes. Moreover, we document that both approaches have highly different topological measures and strong statistical evidence that they feature a different model.
Introduction Innovation and technological change have been described by many scholars as the main drivers of economic growth as in  [1]  and  [2]. [3]  advertised the use of patents as an economic indicator and as a good proxy for innovation. Subsequently, the easier availability of comprehensive databases on patent details and the increasing number of studies allowing a more efficient use of these data (e. g. [4] ) have opened the way to a very wide range of analysis. Most of the statistics derived from the patent databases relied on a few key features: the identity of the inventor, the type and identity of the rights owner, the citations made by the patent to prior art and the technological classes assigned by the patent office post patent's content review. Combining this information is particularly relevant when trying to capture the diffusion of knowledge and the interaction between technological fields as studied in  [5].
Furthermore, most of the existing studies rely on a subsample of patent data, whereas we implement it on the full US Patent database from 1976 to 2013. This way, a general structure of technological innovation can be studied. We draw from this application promising qualitative stylized facts, such as a qualitative regime shift around the end of the 1990s, and a significant improvement of citation modularity for the semantic classification when comparing to the technological classification. These thematic conclusions validate our method as a useful tool to extract endogenous information, in a complementary way to the technological classification. Finally, the statistical model introduced in Section 4. 4 seems to indicate that patents tend to cite more similar patents in the semantic network when fitted to data.
An existing classification: the USPC system Each USPTO patent is associated with a non-empty set of technological classes and subclasses. There are currently around 440 classes and over 150,000 subclasses constituting the United State Patent Classification (USPC) system. While a technological class corresponds to the technological field covered by the patent, a subclass stands for a specific technology or method used in this invention. A patent can have multiple technological classes, on average in our data a patent has 1. 8 different classes and 3. 9 pairs of class/subclass. At this stage, two features of this system are worth mentioning: (i) classes and subclasses are not chosen by the inventors of the patent but by the examiner during the granting process based on the content of the patent; (ii) the classification has evolved in time and continues to change in order to adapt to new technologies by creating or editing classes.
We construct from raw data a unified database. Data is collected from USPTO patent redbook bulk downloads, that provides as raw data (specific dat or xml formats) full patent information, starting from 1976. Detailed procedure of data collection, parsing and consolidation are available in Supporting Information. The latest dump of the database in Mongodb format is available at http://dx. doi. org/10. 7910/DVN/BW3ACK Collection and homogenization of the database into a directly usable database with basic information and abstracts was an important task as USPTO raw data formats are involved and change frequently. We count 4,666,365 utility patents with an abstract granted from 1976 to 2013. 3 he number of patents granted each year increases from around 70,000 in 1976 to about 278,000 in 2013.
Towards a Complementary Classification Potentialities of text-mining techniques as an alternative way to analyze and classify patents are documented in  [26]. The author's main argument, in support of an automatic classification tool for patent, is to reduce the considerable amount of human effort needed to classify all the applications. The work conducted in the field of natural language processing and/or text analysis has been developed in order to improve search performance in patent databases, build technology map or investigate the potential infringement risks prior to developing a new technology (see  [28]  for a review). Text-mining of patent documents is also widely used as a tool to build networks which carry additional information to the simplistic bibliographic connections model as argued in  [16].
4. Multi-stems construction: these are the basic semantic units used in further analysis. They are constructed as groups of successive stems in a sentence which satisfies a simple grammatical function rule. The length of the group is between 1 and 3 and its elements are either nouns, attributive verbs or adjectives. We choose to extract the semantics from such nominal groups in view of the technical nature of texts, which is not likely to contain subtle nuances in combinations of verbs and nominal groups. Text processing operations are implemented in python in order to use built-in functions nltk library  [30]  for most of above operations. This library supports most of state-of-the-art natural language processing operations. 4.
Title of semantic classes USPC technological classes are defined by a title and a highly accurate definition which help retrieve patents easily. The title can be a single word (e. g. : class 101: "Printing") or more complex (e. g. : class 218: "High-voltage switches with arc preventing or extinguishing devices"). As our goal is to release a comprehensive database in which each patent is associated with a set of semantic classes, it is necessary to give an insight on what these classes represent by associating a short description or a title as in  [26]. In our case, such description is taken as a subset of keywords taken from K(k, t). For the vast majority of semantic classes that have less than 5 keywords, we decide to keep all of theses keywords as a description. For the remaining classes which feature around 50 keywords on average, we rely on the topological properties of the semantic network.
3. Second, this choice is "statistically fair" in the sense that both approaches have focused on various goals and not on maximizing directly the within class proportion. Nonetheless, the within class proportion is too sensitive to the distribution of the shape of classes. For example, a dataset where patents for each class account for 10% of the total number of patents will mechanically have a better within class proportion than if each class accounts for only 1%. Consequently, an adequate statistical model, which treats datasets fairly regardless of their distribution in classes, is needed. This effort ressembles to the previous study of citation modularity, but is complementary since the model presented here can be understood as an elementary model of citation network growth. Furthermore, the parameters fitted here can have a direct interpretation as a citation probability.
Conclusion The main contribution of this study was twofold. First we have defined how we built a network of patents based on a classification that uses semantic information from abstracts. We have shown that this classification share some similarities with the traditional technological classification, but also have distinct features. Second, we provide researchers with materials resulting from our analysis, which includes: (i) a database linking each patent with its set of semantic classes and the associated probabilities; (ii) a list of these semantic classes with a description based on the most relevant keywords; (iii) a list of patent with their topological properties in the semantic network (centrality, frequency, degree etc. ). The availability of these data suggests new avenues for further research.
dispth : θ c • ethunit : θ (0) wTasks The tasks to be done in order : keywords extraction, relevance estimation, network construction, semantic probas construction, are launched with the following options :1. keywords : extracts keywords 2. kw-consolidation : consolidate keywords database (techno disp measure) 3. raw-network : estimates relevance, constructs raw network and perform sensitivity analysis 4. classification : classify and compute patent probability, keyword measures and patent measures ; here parameters (θ w , θ c ) can be changed in configuration file.
• dispth : θ c • ethunit : θ (0) wTasks The tasks to be done in order : keywords extraction, relevance estimation, network construction, semantic probas construction, are launched with the following options :1. keywords : extracts keywords 2. kw-consolidation : consolidate keywords database (techno disp measure) 3. raw-network : estimates relevance, constructs raw network and perform sensitivity analysis 4. classification : classify and compute patent probability, keyword measures and patent measures ; here parameters (θ w , θ c ) can be changed in configuration file.
The main challenge is due to climate, which has now been demonstrated to change temperature averages, as well as rainfall means and distributions in the season, and to increase their variability. The shift to a non-stationary climate now implies that observed datasets are no longer a sufficient base to predict system behaviour even at specific locations where the data were collected. There is evidence that pathogens which for decades have had no impact on crops in specific environments are now becoming key determinants of crop yield (e. g. ,  Lees and Hilton, 2003; Yang and Navi, 2005; Berger et al. , 2007; Parker and Warmund, 2011; Gramaje et al. , 2016). At the same time, the increasingly comprehensive goal of estimating risks to global food security requires the inclusion of geographical areas and production system where the available baseline data are not adequate to develop local, robust empirical relationships.
Data requirements Common inputs for PDM are air temperature, precipitation, relative humidity, and leaf wetness  (Magarey et al. , 2001) , at daily or hourly resolution. Other variables such as soil temperature, radiation, wind speed, and direction are used in more specialized models such as those targeting aerial transport or soil pathogens. For many PDM, daily weather data is sufficient, but for many disease models hourly data is required, which can be estimated for scenario analysis with an acceptable level of accuracy (e. g. , air relative humidity,  Bregaglio et al. , 2010). Additionally, numerical weather models can provide gridded data at increasingly finer spatial resolutions, both for current and forecasted data (three-fifteen days). Examples of gridded datasets that can be used for plant disease forecasting include the Real Time Mesoscale Analysis system (RTMA) in the United States  (De Pondeca et al.
, 2011)  and AGRI4CAST in Europe  (JRC, 2015) , and Climate Forecast System Reanalysis (CFSR ) globally  (Saha et al. , 2014). Many of these datasets have ten or more years of historical data, allowing researchers to conduct simulations in the past. For plant disease forecasting, leaf wetness has been a limitation since the data has historically not been collected by weather stations, except those specifically deployed for agricultural monitoring or for research. However, the use of simulation models is now proving to be a practical alternative  (Magarey et al. , 2006; Bregaglio et al. , 2012). When targeting scenarios of climate change, assumptions need to be made for weather variables which are not a direct output of global circulation models, such as wind and relative humidity. When coupling PDM to crop models with the aim of developing an operational tool for pest and disease management, the limiting factor is often the lack of ad-hoc benchmark datasets.