dataesr/scientific-paragraphs-categorization huggingface dataset:
In combination with the potential for large-scale population surveillance, the capability to augment EE data with mapped movement patterns highlights the potential for smartphone-based measurement tools to support novel research evaluating the drivers and effects of interventions and policies that impact PA. However, such large-scale analyses also present challenges for data processing and analytics. Collectively, this study and previous works highlight the value of smartphones as an acceptable measure of PA in both laboratory-based and free-living contexts. Given their ubiquity, integrated sensor features (accelerometry, gyroscope, inclinometer, and GPS), and passive data collection, smartphones offer additional value compared with existing measurement approaches. Further value is added by the capacity to generate large datasets that could be used to understand temporal, location, and contextual factors that affect PA.
Smartphones offer pragmatic advantages over stand-alone GPS devices, although the maximum data capture frequency is typically lower (approximately 1 Hz vs 5+ Hz)  [26]. Increased sampling frequencies have the potential to improve GPS measurement accuracy but would have a detrimental effect on smartphone battery life and may only offer additional benefits during activities with rapidly fluctuating movement patterns  [26]. In this study, the Movn app was configured to record GPS location every 30 min, or when activity was detected via accelerometry to optimize smartphone battery life. More frequent sampling would increase the resolution of geospatial movement patterns but may limit the maximum recording duration because of faster power consumption. This study has important implications for future research.
Intrapartum fetal heart rate (FHR) monitoring constitutes a reference tool in clinical practice to assess the baby health status and to detect fetal acidosis. It is usually analyzed by visual inspection grounded on FIGO criteria. Characterization of Intrapartum fetal heart rate temporal dynamics remains a challenging task and continuously receives academic research efforts. Complexity measures, often implemented with tools referred to as Approximate Entropy (ApEn) or Sample Entropy (SampEn), have regularly been reported as significant features for intrapartum FHR analysis. We explore how Information Theory, and especially auto mutual information (AMI), is connected to ApEn and SampEn and can be used to probe FHR dynamics. Applied to a large (1404 subjects) and documented database of FHR data, collected in a French academic hospital, it is shown that i) auto mutual information outperforms ApEn and SampEn for acidosis detection in the first stage of labor and continues to yield the best performance in the second stage; ii) Shannon entropy increases as labor progresses, and is always much larger in the second stage; iii) babies suffering from fetal acidosis additionally show more structured temporal dynamics than healthy ones and that this progressive structuration can be used for early acidosis detection.
Goals, contributions and outline The present contribution remains in the category of works aiming to design new efficient features for FHR, here based on advanced information theoretic concepts. These new tools are applied to a high quality large (1404 subjects) and documented FHR database collected across years in an academic hospital in France, and described in Section 2. The database is split into two datasets associated each with one stage of labor, which enables us first to assess and compare acidosis detection performance achieved by the proposed features independently on each stage, and second to address differences in FHR temporal dynamics between the two stages. Reexamining formally the definitions of entropy rates in Information Theory, Section 3. 1 first establishes that they can be split into two components: Shannon entropy that quantifies data static properties, and Auto mutual information (AMI) that characterizes temporal dynamics in data, combining both linear and nonlinear (or higher order) statistics.
ApEn and SampEn, defined from Complexity Theory, are then explicitly related to entropy rates, and hence to AMI (cf. Section 3. 3). Estimation procedures for Shannon entropy, entropy rate and AMI, based on k-nearest neighbor (k-NN) algorithms  [30] , are compared to those of ApEn and SampEn, constructed on Correlation Integral algorithms  [18, 40, 29]. Acidosis detection performances are reported in Section 4. 1. Results are discussed in terms of quality versus analysis window size, k-NN or correlation integral based procedures, and differences between stage 1 and stage 2. Further, a longitudinal study consisting of sliding analysis in overlapping windows across the delivery process permits to show that processes characterizing stage 1 and stage 2 are different (Section 4. 4). 2 Datasets: intrapartum fetal heart rate times series and labour stages.
Data collection. Intrapartum fetal heart rate data were collected at the academic Femme-Mère-Enfant hospital, in Lyon, France, during daily routine monitoring across the years 2000 to 2010. They are recorded using STAN S21 or S31 devices with internal scalp electrodes at 12bit resolution, 500 Hz sampling rate (STAN, Neoventa Medical, Sweden). Clinical information was provided by the obstetrician in charge, reporting delivery conditions as well as the health status of the baby, notably the umbilical artery pH after delivery and the decision for intervention due to suspected acidosis  [41]. Datasets. For the present study, subjects were included using criteria detailed in  [41, 24] , leading to a total of 1404 tracing, lasting from 30 minutes to several hours. These criteria essentially aim to reject subjects with too low quality recording (e.
g. , too many missing data, too large gaps, too short recordings,. ). As a result, for subjects in the database, the average fraction of data missing in the last 20 minutes before delivery is less than 5%. The first goal of the present work is to assess the relevance of new information theoretics measures; their robustness to poor quality data is postponed for future work. The measurement of pH, performed by blood test immediately after delivery is systematically documented and used as ground-truth: When pH ≤ 7. 05, the newborn is considered has having suffered from acidosis, and referred to as acidotic (A, pH ≤ 7. 05). Conversely, when pH > 7. 05, the newborn is considered not having suffered from acidosis during delivery, and termed normal (N, pH > 7. 05). In order to have a meaningful pH indication, we retain only subjects for which the time between end of recording and birth is lower or equal to 10 min.
Following the discussion above on labor stages, subjects are split into two different datasets. Dataset I consists of subjects for which delivery took place after a short stage 2 (less than 15 min) or during stage 1 (stage 2 was absent), It contains 913 normal and 26 acidotic subjects. Dataset II gathers FHR for delivery that took place after more than 15 min of stage 2. It contains 450 normal and 15 acidotic subjects. Beat-per-Minute time series and preprocessing. For each subject, the collection device provides us with a digitalized list of RR-interarrivals ∆ k in ms. In reference to common practice in FHR analysis, and for ease of comparisons amongst subjects, RR-interarrivals are converted in regularly sampled Beat-per-Minute times series, by linear interpolation of the samples {. , 36000/∆ k ,.
Estimation We note in the following ApEn (m) and SampEn (m) the estimated values of ApEn and SampEn using our own Matlab implementation, based on Physio-Net packages. We used the commonly accepted value, = 0. 2σ, with σ the standard deviation of X, and m = 2. For all quantities, we used τ = 5 = fs/fmax with fmax = 2Hz the cutoff frequency above which FHR times series essentially contain no relevant information  [13] ; this time delay corresponds to 0. 5s.
Limit of large datasets and vanishing : exact relation When the size of the spheres tends to 0, the expected value of ApEn for a stochastic signal X with any smooth probability density is related, in the limit N → ∞, to the m-order Shannon entropy rate  [28] : ApEn(m, ) →0 h (m,τ ) (X) -log(2 ). (27) Both terms involve m-points correlations of the process X. This relation allows a quantitative comparison of ApEn with the m-order Shannon entropy rate h (m,τ ). The log(2 ) difference corresponds to the pavingwith hyperspheres of radius -of the continuous m-dimensional space over which the probability p(x (m,τ ) t ) involved in eq. (  2 ) is defined, and thus h (m,τ ). This paving defines a discrete phase space, over which eqs. (  18 ), (  19 ) and (  23 ) operate to define ApEn  [54]. This illustrates that, for a stochastic signal, ApEn diverges logarithmically as the size approaches 0, as expected for hKS.
AE ĥ SE Ĥ ĥ ApEn (m) , SampEn (m) , ĥ(m) , Ĥ and Î(m,p) for normal and acidotic (abnormal) subjects in dataset I and II using data from the last T = 20mn before delivery, which are the most crucial. We use the classical values m = 2 and p = 1 for embedding dimensions. To compare performance, we present the box plots of the five normalized (zero-mean, unit-variance) estimates in the left column of Fig. 1. For dataset I, the average of ApEn and SampEn for acidotic subjects is smaller than for normal subjects, while the average of Shanon entropy rate does not show any tendency. This is surprising as one might have expected for ĥ(m) a behavior similar to ApEn and SampEn (see section 3. 3). Average values of Ĥ and Î(2,1) are larger for acidotic subjects. The larger value of Shannon entropy H indicates that the acidotic FHR signals contain more information.
The larger value of AMI indicates a stronger dependence structure in the dynamics of abnormal subjects. For subjects in dataset II, it is harder to find any tendency by looking the average values. N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A N A -4 -2 0 Features performance Fetal acidosis detection performance is assessed with the p-value given by the classical Wilcoxon ranksum test. This non-parametric test of the null hypothesis -which corresponds to identical medians of the distributions of estimates in the normal and abnormal classes -is reported in table  1. We have added one symbol when the p-value is less than 0. 05, two when less than 0. 01. We see that for dataset I, ApEn (m) , SampEn (m) , Ĥ and Î(m,p) for m = 2 discriminate normal and acidotic subjects, while ĥ(m) does not.
Out of the three estimates (ApEn (m) , SampEn (m) , ĥ(m) ) based on entropy rates, the nearest-neighbors one for Shannon entropy rate is the poorest, although its decomposition into Shannon entropy (static one-point information) and AMI (which includes dynamic information) leads to two satisfying estimates Fig. 1  and Table  1  both show that the best performing estimators are SampEn (m) and Î(m,p). In dataset II, although all features performs more poorly than in dataset I, SampEn and AMI are again the best ones, with a p-value lower than 0. 05. We focus on these two features in the following.
Dataset II False positive rate Î(2,1) SampEn (2) Î(2,1) Receiver Operational Characteristics To compare the two best performing features, SampEn and AMI, we plot receiver operational characteristics (ROC) curves in Fig. 2 , both for dataset I and II, using data from the last T = 20mn before delivery. For dataset I, AMI better discriminates acidotic subjects from normal ones. For dataset II, AMI discrimination is only slightly better than SampEn. The area under the curve (AUC) of the ROC of the features is reported in Table  1 , with a bold font to indicate the estimator with the largest AUC. Performance is much worse in dataset II than in dataset I: the AUC is reduced. Nevertheless, AMI is always the better performing estimator (its AUC reduces from 0. 84 to 0. 68), followed by SampEn (AUC reducing from 0.
Effect of the window size on the performance We investigate the robustness of the detection performance when the window size T is reduced, using data from T = 20, 10 and 5 minutes. Results are reported in Fig  1  and Table  2. p-values and AUC both indicate that Î(2,1) and SampEn (2) provide robust discrimination in dataset I even when the observation length is reduced. Again, Î(2,1) is better performing: its AUC is reduced from 0. 84 to 0. 83 when T is reduced from 20mn to 5mn, where the AUC of SampEn is reduced from 0. 79 to 0. 72. In dataset II, once again, performance degrades, but AMI is still better at discriminating acidotic from normal subjects. In the following, we focus on AMI estimates only. SampEn (2)   Î(  2.
Effect of the embedding dimensions on (fetal acidosis detection) performance of AMI In order to improve the acidosis detection performance of the AMI, especially in dataset II, we increase the embedding dimensions m and p used in computing Î(m,p). This way, we probe higher order dependence stucture in the dynamics. Because of the symmetry of AMI (eq. (  10 )), and aiming at probing the effect of increasing either m or p, we plot the AUC of ROC as a function of m + p only. The dependence of AMI on m -p is much smaller and not reported here. These computations have been done with a larger value k = 15 in the k-NN algorithms, in order to accommodate the possibly large embedding dimensions (m + p up to 12). Results are presented in Fig. 3. For a fixed window size, the AUC increases when m + p increases, and reaches a maximum; it then remains constant or decreases slightly.
This behaviour is observed in both datasets I and II and for any window size T ∈  [5, 10, 20] mn. Varying T dos not seems to change the location of the maximum of the AUC in a given dataset. The optimal embedding dimension is m + p = 6 in dataset I and m + p = 10 in dataset II. This hints at a difference in the dynamics of the FHR in the two datasets. Because both bias and computation time increase with the total dimensionality  [57] , the maximal embedding is restricted to m = p = 3. A reduction of the AUC is observed when the analysis window is reduced, but this is only significant for dataset II. We reported in Table  2  the AUC and p-values of the AMI for two embedding dimensions: Î(2,1) and Î(3,3) , for datasets I and II and several window sizes. The best performing estimator is indicated in bold.
Dataset I: rapid delivery In this first section, we focus on dataset I and probe the stage 1, including early labor, active labor, and transition. Using the time at which stage 1 ends as a reference (setting it at t0 = 0), we compute for each subject Î(3,3) in a set of time windows [ti -T, ti], 0 ≤ i ≤ 50 of fixed size T ending at ti = t0 -i * 2mn, so separated by 2mn. We perform this analysis for three window sizes T ∈  [20, 10, 5] mn. The value of AMI computed in the i-th window is then assigned to time ti -T /2, at the center of the interval. By construction of dataset I, delivery occurs less than 15 minutes after pushing started, and can be as short as 1mn, so we completely discard data from stage 2. We then average the values of AMI over the population of normal subjects, and over the population of acidotic subjects, respectively.
Results, including p-values, are presented in Fig. 4. A first observation is that AMI is always larger for acidotic subjects than for normal subjects. As labor progresses, AMI increases in both populations, but the increase is stronger for acidotic subjects. As a consequence, the p-value of the test decreases clearly, so the feature performs better and better when approaching delivery. Detection of acidosis using the AMI feature and T = 20mn can be obtained in dataset I as early as 80 minutes before entering in second stage. Using shorter windows, T = 10mn or 5mn, detection is still reliable as early as one hour before stage 2. We interpret this reduced forecast of acidosis detection in dataset I as a direct consequence of the reduction of the statistics when the window size T is reduced.
Dataset II : delivery after pushing more than 15mn For dataset II, we performed the same dynamical analysis as in the previous section, using the end of stage 1 as the reference time (t0 = 0). Because there is now enough data in the pushing stage, we also perform the analysis of this stage using the delivery time (t0 = D) as the reference. All results are presented in Fig. 5. At the end of stage 1, we observe again that AMI is larger for acidodic subjects than for normal ones, but the difference is not significant in this group (see the corresponding p-value on the right of Fig. 5 ). The situation is identical at the end of stage 2, although we obtain a lower p-value in some windows. The p-value does not decrease clearly when approaching delivery time, as it was in dataset I, see Fig. 4. For subjects in dataset II it is very difficult to make an early detection of acidosis.
(for dataset I). When acidosis develops in first stage of labor, the Shannon auto mutual information estimator Î(m,p) significantly outperforms all other quantities both in terms of p-value and AUC. The performance of AMI is robust when tuning either the size of the observation -and hence the number of points in the data -and the embedding dimensions (m, p). In addition, the performance slightly increases when the total embedding dimension m + p increases; although one has to care about the curse of dimensionality. For abnormal subjects from dataset II, AMI is not able to detect acidosis using data from stage 1. This suggests that acidosis develops later, in the second stage of labor. For all datasets, AMI computed with τ = 0. 5s is always larger for acidotic subjects than for normal subjects.
Acidosis detection in second stage The results reported for stage 2 show a severe decrease in performance of the five estimated quantities. Analysing stage 2 is far more challenging than analysing stage 1, which suggests that temporal dynamics in stage 2 differ notably for those of stage 1  [38]  or simply that our database does not contain enough acidotic subject in that case. Î(m,p) achieves the best performance in terms of p-value and AUC; this clearly underlines that the analysis of nonlinear temporal dynamics is critical for fetal acidosis detection in stage 2. As in stage 1, the AMI is always larger in stage 2 for acidotic subjects than for normal subjects. Although the Shannon entropy computed from the last 20mn of stage 2 before delivery does not show a clear tendency in Fig. 1  for dataset II, looking at Figure  6  clearly shows that Ĥ increases as labor progresses: this is probably related to the average increase of the number of decelerations, which is expected in both the normal and acidotic population.
Probing the dynamics Increasing the total embedding dimensions in AMI improves the performance in the detection of acidotic subjects, in both first and second stages. The best performance is found for different total embedding dimension in the two datasets. This suggests that FHR dynamics is different in each stage. As seen in eq. (  11 ), the Shannon entropy rate can be split in two contributions: one that depends only on static properties (the Shannon entropy, estimated by Ĥ) and one that involves the signal dynamics (the auto mutual information, estimated by Î(m,1) ). By following the time evolution of these two parts, we were able to relate Shannon entropy Ĥ with the evolution of the labor and AMI not only with the evolution of the labor, but also with possible acidosis. Looking at subjects for which the pushing phase is longer than 15mn, it clearly appears that all fetuses are affected by the pushing, as evidenced by a large increase of the Shannon entropy Ĥ, and a small increase of AMI.
Additionally, the increase of AMI is steeper for abnormal subjects than for normal ones, which may indicate different reactions to the pushing and can be related to specific pathological FHR patterns. When the pushing stage is long (dataset II), fetuses reported as acidotic are not showing any sign of acidosis until prolongated pushing. These fetuses appear as normal until delivery is near. When acidosis develops during the first stage of labor, in dataset I, we observe clearly that while AMI increases steadily till delivery for healthy fetuses, it increases faster for acidotic ones. This suggests that acidotic fetuses in dataset I react to early labor, as early as one hour before pushing starts. This could not only indicate that some fetuses are prone to acidosis, but also may pave the way for an early detection of acidosis in this case.
We address this problem with AraQTL (http://www. bioinformatics. nl/AraQTL/), an easily accessible workbench and database for comparative analysis and meta-analysis of all published Arabidopsis eQTL datasets. AraQTL provides a workbench for comparing, re-using and extending upon the results of these experiments. For example, one can easily screen a physical region for specific local eQTLs that could harbour candidate genes for phenotypic QTLs, or detect gene-by-environment interactions by comparing eQTLs under different conditions.
RESULTS eQTL studies in AraQTL AraQTL is a web-based workbench and database for investigation of eQTLs (Figure  1 ). It includes publicly available data from the six original eQTL studies in segregating populations of A. thaliana published to date (Table  1 )  (Keurentjes et al. , 2007; West et al. , 2007; Cubillos et al. , 2012; Snoek et al. , 2012; Joosen et al. , 2013a,b; Lowry et al. , 2013). Together, these studies provide eight sets of mapped eQTLs covering five different populations and are genotyped using over 600 specific genetic markers cumulatively. These populations are defined by their parental ecotypes: Ler 9 Cvi, Bay 9 Sha, Cvi 9 Col, Bur 9 Col and Tsu 9 Kas. Gene expression was measured for over 20 000 different transcripts in more than 1200 samples taken from several different environments, growth stages and plant parts, such as seeds, seedlings, whole rosettes or specific leaves.
Below this, the summary table lists datasets currently in AraQTL which consists of: ② data set selection, ③ recombinant inbred population used, ④ plant parts from which gene expression was measured, ⑤ title and link to the publication the dataset was originally generated for, and ⑥ a link to download the dataset with the eQTL profiles in simple txt format. At the top of the page a navigation bar can be found from which several selections can be made: ⑦ the link to the homepage, ⑧ a link to a genome-wide overview by cis/trans plot per experiment, ⑨ a link to eQTL profile correlation investigation, ⑩ a link to find all eQTLs at a specific locus, ⑪ a link to help features like, About, the manual and FAQ, and ⑫ a link to a page with clickable graphical examples of the different investigations.
At ⑬ quick links to important information can be found. Zanten et al. , 2009; Terpstra et al. , 2010; Snoek et al. , 2012). Using several selection steps and enrichment tests based on eQTLs, transcription factor-binding sites, Gene Ontology (GO) terms and co-expression, a map kinase signalling cascade and a group of transcription factors from the WRKY family were found to be operating downstream of ERECTA  (Terpstra et al. , 2010). Another elegant example of exploiting eQTL datasets to identify positional and functional candidate genes for phenotypic QTLs was published by  Jimenez-Gomez et al. (2010) : combining co-expression, eQTLs  (West et al. , 2007)  and functional annotation to efficiently reveal ELF3 as the causal gene for a shade-avoidance response QTL in the Bay 9 Sha RIL population.