dataesr/scientific-paragraphs-categorization huggingface dataset:
Lomb-Scargle powerspectrum The Lomb-Scargle approach was done in the field of astronomy, corresponding to a framework which can be quite different from time series in coastal oceanography. In contrast to the Blackman-Tukey method, the Lomb-Scargle algorithm evaluates the data of the time series only at times that are actually measured. Detailed discussions of the Lomb-Scargle method are given in  Scargle (1989)  and  Press et al. (1992). An excellent summary of the method and a TURBO PASCAL program to compute the normalized Lomb-Scargle powerspectrum of paleoclimatic data has been published by  Schulz and Stattegger (1998). A comfortable MATLAB algorithm lombscargle to compute the Lomb-Scargle periodogram has been published by Brett Shoelson (The MathWorks Inc. ). We suppose that a physical variable is measured at a set of time.
Empirical Mode Decomposition The key part of the approach HHT is that any complicated dataset can be decomposed with the EMD method into a finite and small number of Intrinsic Mode Functions (IMFs), which represent different scales of the original time series and physically meaningful modes. An IMF is defined as a function having the same number of extrema and zero-crossings. It has also symmetric upper and lower envelopes defined by the local maxima and minima respectively. The IMFs represent different scales of the original time series and physically meaningful modes. Indeed, the EMD divides the time series into a series of modes, but unlike a Fourier transform where each component has a constant frequency and a cosine or sine shape, the EMD allows each mode to have a time-dependent frequency and amplitude.
Rodo and Rodriguez-Aria (2006)  developed the scale-dependent correlation technique. Although these methods detected the correlation between two nonstationary signals by computing the correlation coefficient in a local sliding window, the main problem is to determine the size of this window. Recently,  Chen et al. (2010)  introduced an approach based on EMD. They proposed to first decompose the nonlinear and nonstationary data into their IMFs, then use the instantaneous periods of the IMFs to determine an adaptive window and finally compute the time dependent intrinsic correlation coefficients. Huang and Schmitt (2014)  used TDIC to analyze temperature and dissolved oxygen time series obtained from automatic measurements in a moored buoy station in coastal waters of Boulogne-Sur-Mer (France).
Section 7: Conclusions and perspectives In the era of "Big Data", the time series are longer and longer. Time series analysis in marine sciences is becoming a challenge at the crossroads of marine observatories' data. Indeed, the observatories allow more and more marine data to be safely archived. They contribute to understand and forecast the most crucial marine processes. Therefore, it is necessary to analyze this increasing data flow in an optimal way. In environmental sciences, adequate and specific methods are needed to analyze the complex time series which are nonstationary and reflecting nonlinear processes. To explore these methods, we have analyzed here four time series recorded at high frequency in the coastal waters of Boulogne-sur-Mer, using the MAREL Carnot monitoring station: sea level, salinity, turbidity and temperature data sets.
A C C E P T E D M A N U S C R I P T original signal's variance, such analysis is not relevant for the other time series. Different spectra for the turbidity and salinity are investigated. They reveal a very complex dynamics, with a turbulent-like stochastic behaviour at high frequencies, whereas the dynamics at low frequencies are dominated by deterministic forcings. Compared to the modified periodogram, the Welch method improves the signal-to-noise ratio of the spectrum versus a loss of resolution. Since the Blackman-Tukey method requires evenly-spaced data, we have interpolated the time series. However, interpolation introduces numerous artifacts to the data, both in the time and the frequency domain. Thus, the Lomb-Scargle algorithm is adapted for unevenly-spaced data and has been used as an alternative method.
We can mention here some perspectives opened by this work. Techniques of filtering will be investigated to study the high frequency or to concentrate on seasonalities. Classical Butterworth filters can be implemented or demerliac filters, already used by the "Service Hydrographique et Océanique de la Marine" (S. H. O. M) to calculate the daily mean sea level. Moreover, we have studied each parameter separately. A development of this approach will be to study the couplings between some parameters. Such coupling will be studied considering joint or conditional moments, joint probability density function, and joint spectral analysis. More generally, time series analysis methods could be implemented in an automated way in the environmental databases in order to facilitate the data discovery as well as the investigation tools and techniques.
We have performed experiments for both these tools, using the package FGb  [12]  in Maple and an implementation of F5  [11]  for Gröbner bases, and using the package RegularChains  [21]  in Maple for triangular sets. Regarding the contrast imaging problem, we illustrate the behaviour of our algorithm in the case of water, giving the whole classification. Using Gröbner bases to perform the eliminations, the computation takes 10 s on an 2 GHz Intel Xeon CPU. The Re-alRootClassification command of the Maple RegularChain library needs 1600 s to find this classification. We also ran our algorithm on the general case. While none of the available implementation is able to tackle this classification problem directly, ours can find the polynomials separating the open sets C i within 4 h using FGb, or 2 min using F5, and the projection step of the CAD can be done in 4 h.
Over a period of 5 years, the Innovative Medicines Initiative PROTECT (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium) project has addressed key research questions relevant to the science of safety signal detection. The results of studies conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases are summarised and 39 recommendations have been formulated, many based on comparative analyses across a range of databases (e. g. regulatory, pharmaceutical company). The recommendations point to pragmatic steps that those working in the pharmacovigilance community can take to improve signal detection practices, whether in a national or international agency or in a pharmaceutical company setting.
Introduction The opportunities for effective signal detection in large databases have improved substantially since the early days of pharmacovigilance. In those early days, much effort necessarily focussed on manual clinical review of incoming reports-often in the form of handwritten or typed reports of suspected adverse drug reactions (ADRs) by pharmacovigilance experts. Adverse event reports, or sets of reports, in some way triggering the suspicion of a clinical reviewer would be further investigated by the reviewer and in some instances go on to become signals with some of these signals leading to further actions. While sophisticated approaches to statistical signal detection had been proposed and even tested in a limited methodological or theoretical context  [1] [2] [3] [4] [5] , routine prospective screening using tools and automated systems was a mere pipe dream.
Several decades later, systematic screening of adverse event reports is not just a reality; it is today the de facto standard in large datasets  [6]. However, as adverse event reports are exchanged electronically around the world, in the tens of thousands on a daily basis, it is well accepted that our capabilities for signal detection are far from perfect and should be improved. In addition, we are seeing a significant shift of focus, beyond adverse event reports and prescription event monitoring systems, on to the use of longitudinal observational databases and clinical trials for signal detection. Although the former have been used extensively in the past for epidemiological purposes, their potential use for signal detection is novel and new approaches for identifying safety signals in clinical study data beyond basic comparisons of adverse event frequencies have been slow to gain widespread use.
CIOMS VIII  [7]  was convened in part to address the widespread and growing interest in quantitative analysis of spontaneous reports and particularly the desire for guidelines around the appropriate use of such quantitative approaches. There was also a need to contextualise the role and use of such approaches, including disproportionality, within a holistic signal management perspective. CIOMS VIII was able to achieve these ambitions but also highlighted several areas of signal detection in spontaneous report and observational data and in the use of terminologies that needed more research. PROTECT was set up to address these, and a number of other important topics, in signal detection. The following recommendations  1  relate to research conducted into quantitative signal detection in spontaneous reporting, clinical trial and electronic health records databases conducted under the auspices of the Innovative Medicines Initiative (IMI) PROTECT Work Package 3 (one of seven work packages) between September 2009 and February 2015.
The PROTECT consortium (Pharmacoepidemiological Research on Outcomes of Therapeutics by a European ConsorTium; http://www. imi-protect. eu) was a public-private partnership co-ordinated by the European Medicines Agency (EMA) and received support from the Innovative Medicines Initiative Joint Undertaking (http://www. imi. europa. eu) under Grant Agreement No. 115004. Full details of IMI PROTECT, including specific information about Work Package 3 and a detailed technical report supporting the recommendations can be found at http://www. imi-protect. eu/about. shtml. In attempting to advance the science of safety quantitative signal detection and with the overall goal of increasing the efficiency of quantitative signal detection practices, the scope of IMI PROTECT Work Package 3 has been ambitious, with 12 separate work streams covering a wide range of research questions and undertaken between September 2009 and February 2015.
To convert the insights gained from these efforts into meaningful and executable outputs, the following recommendations have been developed: in total, 39 separate recommendations have been formulated, many based on comparative analyses across a range of databases (e. g. regulatory, pharmaceutical company) and in several data sources (spontaneous reporting, clinical trials and electronic patient records). A further 25 recommendations for future research are also offered. It is acknowledged that signal detection relies on quantitative and qualitative elements and it should be noted that the focus of Work Package 3 was primarily concerning the former. The majority of recommendations are based on the outcomes of the signal detection research conducted under the auspices of IMI PROTECT Work Package 3.
Timeliness of Quantitative Signal Detection Using MedDRA Ò Terms and Groupings Different terms in standard medical terminologies can be used to describe the same suspected ADR. Many organisations rely on disproportionality analysis for first-pass screening of large collections of individual case safety reports, in which the observed rate of a drug and adverse reaction reported together is compared with an expected value based on their relative frequencies reported individually in the spontaneous reporting database. Confidence intervals or statistical significance tests are used to provide some protection against spurious associations and a certain number of reports are generally required before an association can be detected. By grouping together related medical terms for the purpose of analysis, the observed count will increase, but so too may the corresponding expected value.
It is not known whether lumping or splitting is preferable for timely quantitative signal detection. A previous study reported lower sensitivity but higher positive predictive value for MedDRA Ò groupings than for preferred terms (PTs) using cumulative data, but did not evaluate the timeliness of statistical signalling  [8]. The study of Hill et al. [9]  sought to determine to what extent the use of standard MedDRA Ò2 groupings could expedite the detection of disproportionate reporting patterns for historical safety signals, relative to analysis by individual MedDRA Ò PTs, separately, as is common practice. Analyses were performed in the World Health Organization (WHO) Global Individual Case Safety Reports Database, VigiBase Ò as of 5 February, 2010. The scope of the study was restricted to 13 medical concepts identified as having medium to high probability of being drug related  [10].
Recommendation Rationale For overall timeliness in quantitative signal detection, analysis can be performed at the MedDRA Ò PT level The PROTECT study found no advantage in conducting signal detection at levels of MedDRA Ò above the PT level and indeed observed a net loss in timeliness of quantitative signal detection from replacing an analysis at the PT level with one at a higher level of the hierarchy  [9]  Future research should evaluate the false-positive burden for signal detection at each level of the terminology The false-positive burden was out of scope for the PROTECT study  [9]  Recommendation Rationale The PROTECT study was restricted to a selection of 13 ADR categories  [9]  The concordance between groupings derived with knowledge engineering techniques and standardised event groupings, or manually derived groupings were studied and found fair concordance between the terms included in the standard and proposed groupings  [16, 17, 19] , as well as high concordance between the corresponding measures of disproportionality with randomly selected drugs in the FAERS 3  database  [16, 17].
Development of a Structured Database of SPC §4. 8 as Reference Dataset The information available from the Summary of Product Characteristics (SPC) is increasingly used by computer applications. For a computer program to make use of this information, it must be coded according to a well-defined and exhaustive dictionary. Probably the most commonly used dictionary for ADRs is MedDRA Ò. It is recommended  [20]  that ADRs in the SPC be listed using exact (usually preferred) terms from MedDRA Ò but this does not always occur in practice. Sometimes this is because the ADR could be described using multiple MedDR AÒ PTs and would thus be difficult to communicate efficiently to clinical staff in this format. Of course, no such problem arises with a computer that can handle multiple terms efficiently.
The final step was to subject remaining codes to expert evaluation at the EMA and Bayer Pharmaceuticals. An obvious application of machine-readable ADR information arises in pharmacovigilance signal detection systems. Such systems use ADR reports that are themselves coded in MedDRA Ò and hence it is easy to use the ADR dataset to determine immediately if a new signal of disproportionate reporting (SDR)  [22, 23]  corresponds to a known ADR. This eliminates the need for manual inspection of the SPC where the focus of the monitoring is the detection of new risks. This use has already been tested successfully at the EMA and the UMC and implemented into their corresponding signal detection process. Another potential application would be to alert doctors to a possible known ADR using the information on medications and clinical events contained in electronic health records.
A further use is in research on ADRs, for example, it could be used to identify products with similar ADR profiles or to help construct reference datasets for evaluation of quantitative signal detection algorithms  [21, 24]. If similar databases are constructed in other regulatory systems, they could also be used to investigate the consistency of regulatory decisions across these systems. A difficulty with mapping textual SPCs to MedDRA Ò is that a given medical concept may map to several PTs. When HLTs from MedDRA Ò are used in the SPC this is handled easily but non-MedDRA Ò terms require considerable thought. No systematic process exists to build up groups of MedDRA Ò PTs corresponding to broader medical concepts. It is currently an incremental process that improves over time as non-standard terms are discovered and the mappings refined through repeated scrutiny, and this area needs additional work.
Comparison of Disproportionality Analysis Methods Within and Across Spontaneous Report Databases Most pharmacovigilance departments maintain a system to identify ADRs through analysis of spontaneous reports. The majority of statistical methods used employ a disproportionality statistic calculated for each drug-event combination in the dataset and a signal detection algorithm that consists of a set of conditions that the disproportionality statistic and, possibly, other statistics calculated for the drug-event combination must satisfy for a SDR to be identified. The nature of the spontaneous report databases in terms of size and drug diversity varies between operators and it is unclear whether any signal detection algorithm can be expected to provide good performance in a wide range of environments.
A number of different disproportionality statistics are in use  [12, [29] [30] [31] , but they are conceptually very similar  [32] [33] [34]. The study of Candore et al. [35]  compared the performance of a number of commonly used signal detection algorithms used across a range of spontaneous report databases at national and international pharmacovigilance organisations and individual pharmaceutical companies. A set of 220 products was chosen and a reference set of ADRs was compiled based on SPC and company core data sheets. Among four companies, one national agency and two international spontaneous report databases, 15 quantitative signal detection algorithms based on five disproportionality statistics were tested using a subset of products that fell within the pharmacovigilance responsibilities of the respective database owners.
Signals of disproportionate reporting were identified at monthly intervals and classified as true positives if they corresponded to an entry in the reference set. To measure the (algorithm's) performance, these results were summarised as sensitivity and precision (specifically, the positive predictive value) for each algorithm in each database. Time to signalling was also investigated, as early detection is an important contributory factor to effective pharmacovigilance. Different algorithms gave very different levels of signal detection performance across all spontaneous report databases tested. However, increases in sensitivity were generally associated with a decrease in precision and no method clearly dominated all others. The performance is strongly dependent on the thresholds and other rules based on the disproportionality statistics that define a statistical signal.
However, the different disproportionality statistics did not themselves influence the achievable performance: the choice of signal detection algorithm was much more important than the choice of disproportionality statistic. Absolute performance of the same algorithm might be very different between one spontaneous report database and another but the relative performance of two algorithms was generally similar in different databases. Over the lifetime of a product, there is a reduction in precision of any quantitative signal detection algorithm. The changes in sensitivity and precision obtainable by replacing one quantitative signal detection algorithm with another are predictable. However, the absolute performance of a method is specific to the spontaneous report database and is best assessed directly on that database.
Recommendation Rationale Computerised text processing to help in mapping non-standard descriptions of ADRs to MedDRA Ò codes should be considered both for efficiency and consistency of coding practice The approximate matching system was used to find appropriate MedDRA Ò terms when non-standard terminology was used in the SPC. This was usually successful and also much more efficient than human intervention alone  [28]  In Choice of a disproportionality statistic for signal detection should be primarily based on ease of implementation, interpretation and optimisation of resources Several disproportionality statistics are currently used in data mining spontaneous report databases. All these can achieve similar overall performance by choice of appropriate signal detection algorithm. Thus, choice should be based on criteria other than signal detection performance.
5 Use of Subgrouping and Stratification in Disproportionality Analysis Spontaneous report databases cover a range of products aimed at diverse medical conditions and used across a broad range of patient populations. This diversity is important as, for example, vaccines are given to healthy subjects, often children who are likely to have fewer underlying medical conditions and consequently different reported background adverse events than the main population of patients that use other medicines. Many quantitative signal detection algorithms disregard this diversity and give equal weight to information from all products and all patients when computing the expected number of reports for a particular drug-event pair, which may result in signals either being masked or false associations being flagged as potential signals.
Recommendation Rationale Consideration should be given to the choice of signal detection algorithm used with disproportionality statistics because these can have important effects on quantitative signal detection performance In contrast to the choice of disproportionality statistic, the choice of signal detection algorithm to define a SDR can provide very different levels of quantitative signal detection performance in terms of sensitivity, precision and time to signal. Hence, these criteria must be carefully selected on the basis of empirical evidence  [35]  For moderate to large spontaneous report databases, the relative performance of a quantitative signal detection algorithm in one database can be predicted from research in other databases In the PROTECT study, signal detection algorithms with good signaling properties (in terms of sensitivity and positive predictive value) compared to other signal detection algorithms in one spontaneous report database also had relatively good signaling properties in other spontaneous report databases.
The databases were both regulatory and company based and ranged in size from about 500,000 to 5,000,000 reports. Hence, relative performance in moderately large databases can be reliably inferred from evaluations in other settings  [35]  Absolute performance of the selected quantitative signal detection algorithm must be validated in the target spontaneous report database Although the relative performance of signal detection algorithms is similar in different spontaneous report databases, the absolute performance characteristics may vary substantially. Hence, it is advisable to test the chosen disproportionality statistic with a range of signal detection algorithms within the target database  [35]  Consideration should be given to the effect of reduced positive predictive value with time on the market There appears to be a reduction in precision with time and hence it may be more productive to put additional effort into the evaluation of signals from newer products.