dataesr/scientific-paragraphs-categorization huggingface dataset:
Both of these were carried out in a custom written Matlab program. The cluster maps that were generated using Ripley's K-function were used to visualize the distribution of clusters of VAMP2 and BoNT/A-Hc. The K function is a method used to describe a spatial point pattern at increasing distance scales. The K function is simply: λ ( ) = ( ) - K t E [Number of points within distance r of a randomly chosen point] 1 1 where λ is the density of points and E is the expectation value. The most commonly used form of the K function is due to Ripley  32  and can be expressed as: ∑∑ λ ( ) = ( < ) ( ) - ≠ K r I d r N 2 i j ij 1 1 where I is the indicator function, d is the distance between the points i and j and N is the total number of points. Rather than use the K function directly we used a linearized form that can be expressed as π ( ) = ( ).
Simulations of localizations. Matlab was used to generate simulations of clusters of localizations to model the results from the cross-correlation function. Two separate distributions were generated to simulate dual-channel data, with the parameters for each of the distributions set independently. Seed points for circular domains were chosen at random, with an overall density and number (70 seeds for each channel) determining the number of localizations within each cluster. To replicate the clusters of localizations in each channel, x-y coordinates were selected from a uniform distribution within a predetermined radius from each seed point. A radius of 16 arbitrary units (AU) was chosen to replicate VAMP2, and 12 AU for BoNT/A-Hc, with the number of localizations in the clusters calculated using a density of 0.
Acknowledgements Electron microscopy was performed in the  Australian Microscopy and Microanalysis Facility (AMMRF) at the Centre for Microscopy and Microanalysis at the University of Queensland. We thank  C. Breillat ,  D. Bouchet ,  B. Tessier ,  A. Frouin ,  L. Royer , and  N. Retailleau  for help in molecular biology and cell cultures. The GSDIM imaging was carried out in the  Bordeaux Imaging Center of the University of Bordeaux Segalen  with the help of  Magali Mondin. We thank  Rowan Tweedale  for critical appraisal of the manuscript. CBH's travel to the  Interdisciplinary Institute for Neuroscience in Bordeaux  to carry out GSDIM imaging was supported by a  Graduate Student International Travel Award  ( The University of Queensland ). The work was supported by an  Australian Research Council   Discovery Project grant  ( DP120104057 ) and an  Australian Research Council (ARC)   Linkage Infrastructure Equipment and Facilities grant  ( LE130100078 ).
Over the past decades, many studies have been published on the extraction of non-invasive foetal electrocardiogram (NI-FECG) from abdominal recordings. Most of these contributions claim to obtain excellent results in detecting foetal QRS (FQRS) complexes in terms of location. A small subset of authors have investigated the extraction of morphological features from the NI-FECG. However, due to the shortage of available public databases, the large variety of performance measures employed and the lack of open-source reference algorithms, most contributions cannot be meaningfully assessed. This article attempts to address these issues by presenting a standardised methodology for stress testing NI-FECG algorithms, including absolute data, as well as extraction and evaluation routines. To that end, a large database of realistic artificial signals was created, totaling 145.
8 h of multichannel data and over one million FQRS complexes. An important characteristic of this dataset is the inclusion of several non-stationary events (e. g. foetal movements, uterine contractions and heart rate fluctuations) that are critical for evaluating extraction routines. To demonstrate our testing methodology, three classes of NI-FECG extraction algorithms were evaluated: blind source separation (BSS), template subtraction (TS) and adaptive methods (AM). Experiments were conducted to benchmark the performance of eight NI-FECG extraction algorithms on the artificial database focusing on: FQRS detection and morphological analysis (foetal QT and T/QRS ratio). The overall median FQRS detection accuracies (i. e. considering all nonstationary events) for the best performing methods in each group were 99.
e. non-invasive FECG (NI-FECG), makes use of surface electrodes placed onto the maternal abdomen and is usually applied from 20th week of gestation onwards. NI-FECG's undemanding recording setup comes at cost of a generally lower signal-to-noise ratio (SNR) for the FECG signal, since FECG overlaps both in time and frequency domain with the maternal ECG (MECG) and various noise sources (e. g. muscular artefacts). Extracting the foetal signal from the abdominal mixture remains a challenging task, which has hindered NI-FECG's further usage in the clinical practice. A number of methods have been proposed to process abdominal mixtures (see  Behar et al (2016)  for a detailed review). However, due to the lack of annotated public databases and defined protocols for assessing these algorithms, available studies may be biased and of questionable reproducibility.
The PhysioNet/Computing in Cardiology Challenge 2013  (Clifford et al 2014) , here referred to as 'Challenge', addressed the topic of NI-FECG extraction. A few records of Challenge's database consisted of simulated abdominal signals using the synthetic foetal ECG synthetic simulator ( fecgsyn-  Behar et al (2014c) ). The present contribution aims at providing a standardised evaluation procedure for NI-FECG signal processing algorithms. For this purpose, a large simulated dataset was generated using the fecgsyn and three experiments were conducted: (1) assessing how the success of blind source separation algorithms is changing with the number of input recorded abdominal channels; (2) benchmarking a series of open-source state-of-the-art extraction methods, in terms of FQRS detection accuracy; (3) investigating the accuracy of morphological parameters evaluation (i.
In this study, we are particularly interested in investigating the output of NI-FECG processing methods in the presence of such non-stationarities. A total of seven physiological events (i. e. described in table  1 ) was considered. For each case, the heart dipole models (for mother and foetuses) were generated ten times by randomly selecting one of the nine vectorcardiograms available in the fecgsyn toolbox. Five different levels of additive noise were included (0, 3, 6, 9, and 12 dB). Simulations were repeated five times, re-generating noise signals on every iteration, to obtain a more representative database. Overall a total of × × × = 7 10 5 5 1750 synthetic signals were produced. Each simulation consisted of 5 minutes abdominal mixtures projected onto 34 channels 5 Available at www. physionet.
NI-FECG extraction techniques Despite the large number of NI-FECG extraction methods that have been proposed in the literature, very few studies provide open-source code for their algorithms. The Challenge promoted a considerable advance in the field by making a dataset and evaluation algorithms freely available, while some participants could voluntarily open-source their own code. Aside from the Challenge, another valuable source for NI-FECG algorithms is the Open-Source  U a b , an uniform distribution between a and b. mheart was allowed to vary its position up to 1% of the conductor's volume in any direction. Electrophysiological Toolbox (OSET)  (Sameni 2010) , which contains several algorithms for filtering, detecting and extracting foetal signals and was released under a GNU GPL license.
The mixing matrices for BSS techniques were estimated on every 60 s and used for processing the data in the following 60 s. This was done in order to allow the methods to partially cope with non-stationarities and be in conformity with the algorithm initialisation window requirement imposed. 2. 2. 2. Template subtraction. TS rely on building an average MECG cycle (the so-called 'template') by means of coherent averaging several maternal beats. This procedure heavily depends on accurate maternal QRS detection. After being constructed, templates are adapted and subtracted from each maternal cycle, leaving residual FECG and noise. A variety of TS techniques have been described in the literature  (Cerutti et al 1986 , Martens et al 2007 , Ungureanu et al 2007 , 2009 , Vullings et al 2009 , Di Marco et al 2013 , Zaunseder et al 2013 , Andreotti et al 2014 , Behar et al 2014a , Lipponen and Tarvainen 2014).
3. 2) can be obtained. The experiment took into consideration the presence of noise and effects of the different NI-FECG extraction methods on the estimated morphological measures. In this experiment, all extraction methods from experiment 2 were used, since an accurate FQRS detection does not necessarily imply that the FECG morphology is best preserved. For providing a meaningful pathophysiological analysis and clearer presentation of the results, we concentrate on a subset of the database containing Baseline and case 0 (noise only). These cases are similar in the sense that the Baseline can be considered as a case 0 with infinite SNR, since no noise is present. Figure  2  depicts the evaluated features and signal processing key-steps for the morphological analysis. After performing the extraction (analogously to experiment 2 with a 0.
3% to 97. 3%. In this example the number of final components ranged between three and eight depending on the dataset and simulated case. Figure  3  shows the F 1 results of BSS pca and various BSS ica algorithms with respect to the number of available abdominal channels. Best average results using eight channels were BSS pca (97. 40%), BSS ica with FAST-ICA (97. 22%-both deflationary and symmetric) and BSS ica with JADE (97. 46%). For this reason, JADE was selected to represent BSS ica techniques in experiments 2 and 3. Since for ⩾ N 8 ch , little increase in F 1 was achieved, further experiments only used the 8 channels electrode configuration.
Results for experiment 3: morphological analysis Figure  6  shows exemplary selected independent components (output from BSS ica ) around segments where the mixing matrix was updated. The dataset presented in this figure includes a highly non-stationary event (case 1-foetal movement) and demonstrates the expected difficulties of analysing the morphology in case such non-stationarities would to be considered in our morphological analysis. Due to the current state of signal processing techniques to foetal morphological analysis, several beats were excluded either during template generation, segmentation using ecgpuwave or due to distortions by the extraction methods. The percentage of excluded beats increased with an decrease in the SNR level ranging from 8 to 78% for TS methods, 19 to 48% for AM, meanwhile it was relatively constant for BSS (14-20%).
Similarly to the analysis in the previous experiment, the difference between median results were statistically tested (see figure  8 ). In this figure, a Kruskal-Wallis test was performed in evaluating if differences in the median ∼ FQT and ∼ FTQRS results for the various SNR levels were significant. It has to be kept in mind that the percentage of missing templates due to failed segmentation or failure in construction is not represented on these plots and increase with a decreasing SNR. Figure  6. Two exemplary segments are portrayed during which BSS ica 's mixing matrix is updated (i. e. at 60 s and 180 s-dashed line) for a dataset containing foetal movement. Depicted above are the abdominal mixtures for channel 14, below are the selected components with highest F 1 , eight channels were used as input.
However, these results should be validated on a larger database with clinical (pathological) recordings. From figure  8  it is evident that TS techniques heavily depend on the amount of noise present in the measurement, while BSS and AM methods showed a lower dependency to noise. Moreover, our analysis showed a negative correlation between the number of templates that were unsuccessfully generated or segmented for TS and AM techniques. The TS ekf algorithm did not perform as well as expected, this may be attributed to its simple model, which falsely models the FECG signal as a white Gaussian noise. A promising solution to this modelling problem are variations of the TS ekf technique, which takes into consideration both maternal and foetal heart models, as suggested by  Behar et al (2014d)  and  Niknazar et al (2013).
g. different BSS approaches as the tensor decomposition  (Niknazar et al 2014)  and combinations of different classes of algorithms as the deflation algorithm proposed in  Sameni et al (2010)  or πCA  (Sameni et al 2008). Besides the extraction procedure, other aspects of the NI-FECG's signal processing should be separately considered, e. g. preprocessing, channel/ component selection, smoothing techniques, FQRS detection and FECG enhancement. The authors strongly recommend that researchers compare their own methodologies performance against the provided database and techniques. In this study, simulated data were used and silver-standard reference FQT and FTQRS values were obtained using the open-source segmentation software ecgpuwave. Despite its wide use, in this study ecgpuwave often did not return relevant fiducials (e.
g. T-peak or T-end). Still, ecgpuwave is one of the very few existent open-source algorithms for ECG segmentation freely available in the WFDB toolbox  (Silva and Moody 2014). Several alternatives have been proposed in the literature  (Martinez et al 2004 , Schmidt et al 2014) , which have reported better performance than ecgpuwave. However, current algorithms are mainly designed and trained using adult ECG databases and are likely to be sub-optimal for FECG analysis. Preprocessing is a crucial aspect of morphological analysis. In this work the signal bandwidth was configured as recommended by the American Heart Society for adult electrocardiography  (Kligfield et al 2007). A clinical trial is required to confirm if such standards can indeed be adopted for FECG analysis. Regarding the template generation, there are no current standards in ECG analysis.
Conclusion This contribution features a well-defined analysis framework, reproducible statistical measures and a substantial dataset for benchmarking algorithms in NI-FECG analysis in terms of FQRS detection and morphological analysis. Data, extraction algorithms and evaluation routines used in this study were released as part of the fecgsyn toolbox available at www. physionet. org/physiotools/ipmcode/fecgsyn/. Three experiments were conducted to benchmark the performance of eight state-of-the-art NI-FECG extraction methods in their capacity to evaluate the FQRS locations, FQT length and FTQRS ratio. For that purpose, a large dataset comprising simulated abdominal signals modelling different non-stationary scenarios was developed. Indeed, no method performed systematically better on every experiments or non-stationary cases (see table  3  and figure 4 ).
Therefore, a combination of different extraction techniques may be beneficial. Moreover, it is important to consider non-stationary cases when evaluating NI-FECG extraction algorithms, since a given algorithm can perform well in some specific instances and fail in the case of some non-stationarity (see table 3-no method performs systematically best). In general the following items need to be further studied: pre-filtering, template generation and segmentation. For BSS techniques important considerations such as the number of channels, dimensionality reduction step and the limitations of source domain morphological analysis were addressed. The fecgsyn toolbox is the largest open-source collection of NI-FECG extraction algorithms known to the authors, which also provides a large artificial database and the necessary code for evaluating algorithmic performances for the purpose of FHR extraction and morphological analysis.
Acknowledgments FA is financially supported by the  Conselho Nacional de Desenvolvimento Tecnologico (CNPq-Brazil)  and  TU Dresden's Graduate Academy. JB is supported in part at the  Technion  by the  Aly Kaufman Fellowship. JO was supported by the  Royal Society  under a  Newton Fellowship , grant number  793/914/N/K/EST/DD PF/tkg/4004642.
Example We illustrate the behavior of QUACQ on a simple example. Consider the set of variables X 1 ,. , X 5 with domains {1. 5}, a language Γ = {=, =}, a basis B = {= ij , = ij | i, j ∈ 1. 5, i < j}, and a target network C T = {= 15 , = 34 }. Suppose the first example generated in line 4 of QUACQ is e 1 = (1, 1, 1, 1, 1). The trace of the execution of FindScope(e 1 , ∅, X 1. X 5 , false) is in Table  1. Each line corresponds to a call to FindScope. Queries are always on the variables in R. '×' in the column ASK means that the previous call returned ∅, so the question is skipped. The initial call (call-0 in Table  1 ) does not ask the question because the initial call to FindScope has the Boolean ask query set to false. Y is split in two sets Y 1 = {X 1 , X 2 , X 3 } and Y 2 = Algorithm 3: Function FindC: returns a constraint of C T with scope Y 1 function FindC(in Y : scope): constraints; 2 begin 3 B ← B \ {cY | CL |= cY }; 4 ∆ ← {cY | (cY ∈ B[Y ]); with R = {X 1 , X 2 , X 3 , X 4 } and Y = {X 5 }.
Second, we consider the query submitted to the user in line 6 of QUACQ and count how many times it can receive the answer yes and no. The key observation is that an instance of this language contains at most O(n) non-redundant constraints. For each no answer in line 6 of QUACQ, a new constraint will eventually be added to C L. Only nonredundant constraints are discovered in this way because the query must satisfy C L. It follows that at most O(n) such queries are answered no, each one entailing O(log n) more queries through the procedure FindScope. Now we bound the number of yes answers in line 6 of QUACQ. The same observation on the structure of this language is useful here as well. We show in the complete proof that a query maximizing the number of violations of constraints in the basis B while satisfying the constraints in C L violates at least |B|/2 constraints in B.
Conclusion We have proposed QUACQ, an algorithm that learns constraint networks by asking the user to classify partial assignments as positive or negative. Each time it receives a negative example, the algorithm converges on a constraint of the target network in a logarithmic number of queries. We have shown that QUACQ is optimal on certain constraint languages. Asking the user to classify partial assignments allows to converge on the target constraint network in a polynomial number of queries. Furthermore, as opposed to existing techniques, the user does not need to provide positive examples to converge. This last feature can be very useful when the problem has not been previously solved. We have also proposed GENACQ, a technique to make constraint acquisition more efficient in practice by using information on the types of components the variables in the problem represent.
Infectious diseases still represent a major challenge for humanity. In this context, their surveillance is critical. From 2010 to 2016, two Point-Of-Care (POC) laboratories have been successfully implemented in the rural Saloum region of Senegal. In parallel, a homemade syndromic surveillance system called EPIMIC was implemented to monitor infectious diseases using data produced by the POC laboratory of the Timone hospital in Marseille, France. The aim of this study is to describe the steps necessary for implementing EPIMIC using data routinely produced by two POC laboratories (POC-L) established in rural Senegal villages. After improving EPIMIC, we started to monitor the 15 pathogens routinely diagnosed in the two POC-L using the same methodology we used in France. In 5 years, 2,577 deduplicated patients-samples couples from 775 different patients have been tested in the Dielmo and Ndiop POC-L.
739 deduplicated patients-samples couples were found to be positive to at least one of the tested pathogens. The retrospective analysis of the Dielmo and Ndiop POC data with EPIMIC allowed to generate 443 alarms. Since January 2016, 316 deduplicated patients-samples couples collected from 298 different patients were processed in the Niakhar POC laboratory. 56 deduplicated patients-samples couples were found to be positive to at least one of the tested pathogens. The retrospective analysis of the data of the Niakhar POC laboratory with EPIMIC allowed to generate 14 alarms. Although some improvements are still needed, EPIMIC has been successfully spread using data routinely produced by two rural POC-L in Senegal, West Africa.
Thus, it allows the real-time monitoring of the number of samples tested and positives performed every week. The percentage of positivity for each test performed is also automatically calculated. The new values entered in the software are then automatically compared to their historical mean number +/-two standard deviations (thresholds), and alarms are automatically emitted by EPIMIC when the last entered values (the number of tested samples, the number of positive samples and the percentage of positiveness) exceed the thresholds. Plots are then automatically produced by the software. Major improvements have been performed in the current EPIMIC version since its first description. All these improvements have been made using the Visual Basic programming language. The current version of EPIMIC consists in a single soft Microsoft Excel file which can be simply configured by following a step-by-step procedure proposed by the software during its first use.
After this initial configuration, EPIMIC automatically collects and analyzes data from the different POC Microsoft Excel databases without any manual contributions, avoiding any inputs errors. If duplicates are present for the same week, the software only keeps the sample from which the most important number of positive tests have been identified. Moreover, the software automatically produces a Microsoft PowerPoint summary of the epidemiologic situation in the two POC-L using plots and screen shots when alarms are emitted for pathogens monitored by the software (Fig  2 ). In this way, data are anonymized to garanty the confidentiality of the patients' data. Furthermore, the software separates the data according the place they come from (per villages for the Dielmo and Ndiop database, and per health structures for the Niakhar database), facilitating the weekly analysis of the epidemiological events.
Niakhar The POC laboratory of Niakhar has been set up in January 2016, i. e 26 weeks ago. Over this period, this POC laboratory tested 316 deduplicated patients-samples couples collected from 298 different patients to look for the presence of the same 15 pathogens using the same POC tests as those mentioned above. On average, 12 patients were weekly tested (range: between 3 to 26 patients per week). These tests allowed to identify 56 deduplicated patients-samples couples positive to at least one of the tested pathogens. The retrospective analysis of the historical database of the Niakhar POC laboratory with EPIMIC virtually allowed to identify 14 alarms, 8 before May 2016 and 6 since May 2016. Borrelia spp. was the pathogen the most cited by alarms emitted by EPIMIC (4 alarms, 315 tested samples, and 36 positive samples), followed by P.
This is especially due to the fact that it was developed on Microsoft Excel, with low human and computer resources, using a simple algorithm for the detection of abnormal epidemiological events, and with no need for data specifically produced for surveillance as the software was specifically developed for syndromic surveillance  [6, 10]. However, this implementation required major improvements related to local needs. Thus, as mentioned before, the software was fully automated for the collection, anonymisation and analysis of the data, and for the production of a simple Microsoft PowerPoint activity report  (Fig 2). This improvement considerably decrease the level of complexity to use the software including for people without in-depth computer skills, which is crucial to ensure its optimal long-term use  [11].