dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Subjects We studied participants enrolled in the MANOE (MAternal Nutrition and Offspring's Epigenome) study, an ongoing prospective, observational cohort study initiated in April 2012. Healthy Caucasian women who desired to become pregnant or who were in the first trimester of pregnancy were recruited at the Department of Obstetrics and Gynecology of the University Hospitals Leuven (Belgium). We enrolled 150 women (34 women before pregnancy and 116 in the first trimester of pregnancy) between April 2012 and January 2015 (Figure  2 ). Exclusion criteria were: non-Caucasian women, multiple pregnancies (twins, triplets etc. ), infertility treatment, and longitudinal missing data. Only two women had no methyl-group donor intake data and were not included in the statistical analysis, which gives us a total of 148 women (Figure  2 ).
To assess maternal intake of dietary methyl-group donors (methionine, folate, betaine, and choline) before and during each trimester of pregnancy of women enrolled in the MANOE study a food-frequency questionnaire (FFQ) was developed and validated. We validated the FFQ in two validation studies (one using a food diary as reference method and one with additional biomarkers of the one-carbon metabolism) in women of reproductive age  [20, 21]. Ideally, food consumption data are linked to food composition data collected in the same region/country. Since, the Belgian food composition database NUBEL  [22]  does not contain information about the four methyl-group donors under study databases of neighboring countries were used in the validation of the FFQ. The Dutch NEVO food composition database  [23]  was used for folate and the German BLS Nutrient database  [24]  for methionine.
For choline and betaine, no European database was available. However, the United States Department of Agriculture (USDA) database has a special section for the choline and betaine content of common foods. The total choline content was used and calculated as the sum of free choline, glycerophosphocholine, phosphocholine, phosphatidylcholine, and sphingomyelin  [25]. The USDA database was also used for the nutrient content of folate and methionine if not found in NEVO and BSL databases, respectively. Some typically Belgian food products like "speculaas", a type of shortcrust biscuit, are.
For choline and betaine, no European database was available. However, the United States Department of Agriculture (USDA) database has a special section for the choline and betaine content of common foods. The total choline content was used and calculated as the sum of free choline, glycerophosphocholine, phosphocholine, phosphatidylcholine, and sphingomyelin  [25]. The USDA database was also used for the nutrient content of folate and methionine if not found in NEVO and BSL databases, respectively. Some typically Belgian food products like "speculaas", a type of shortcrust biscuit, are not included in the USDA database. For these products, the choline and betaine content of the American variety was entered in the database. For example, a ginger biscuit was used for the choline and betaine content of "speculaas".
This is a problem in most human epidemiological studies. Another limitation is the fact that the Belgian food composition database NUBEL  [22]  does not contain information about the four methyl-group donors under study. Databases of neighboring countries or the USDA database for choline and betaine  [25]  content were used in the validation of the FFQ  [20, 21]. For folate, the Dutch NEVO food composition database was used  [23]  and the German BLS Nutrient database for methionine  [24]. The USDA database was also used for the nutrient content of folate and methionine if not found in NEVO and BSL databases respectively. The limitations of using food composition databases from different countries include differences in description of foods, calculation of nutrient content, recipe calculations, etc.
Acknowledgments: We acknowledge the women who volunteered to take part in this study. Also the  Unit Leuven Biostatistics and Statistical Bioinformatics Centre (L-BioStat ) and in particular  Annouschka Laenen  who did the statistical analysis. Funding for the present study was provided by a PhD grant (grant number  11B1812N ) from The  Research Foundation-Flanders (FWO)  and the  Flemish Institute of Technological Research (VITO).
The 2nd level operates at the writer level by converting the set of words of each writer into a Bag of 1st-Layer Clusters that is augmented by a multidimensional description of his/her writing stability across words. This 2nd-layer representation is input to another clustering algorithm that generates categories of writer styles along with their age distributions. We have carried out extensive experiments on a large public online HW database covering all ages from teenagers to old people, augmented by HW samples of elders acquired at Broca Hospital in Paris. Thanks to this extended population, extra patterns of handwriting evolution emerge through age, with respect to our previous works in  [19, 20]. Contrary to our previous works in  [19, 20] , an extensive study on such patterns through unsupervised learning techniques is here presented and a complete section is devoted to a new analysis on subjects older than 65 years.
Database Description. For experiments, we use the IRONOFF database  [27]  of online HW word samples in English and French, acquired using a Wacom Tablet (Ultra-PadA4) that records a sequence of tuples ((), (), ()) sampled at 100 Hz with a resolution of 300 ppi. Although this database consists of 880 writers, only few are more than 60 years old (concretely 11 are between 60 and 77 years old). For a more reliable study of HW change as people age, we collected HW samples at Broca Hospital in Paris from a population of 25 persons with no diagnosed pathology, 23 of which are between 58 and 86 years old with an average of 72. These samples were also acquired on a Wacom Tablet (Intuos Pro Large) at the same sampling rate (100 Hz) but at a higher resolution (5080 ppi); we thus decreased the resolution of the new samples to match the 300 ppi of the IRONOFF database.
Combining both databases, we obtain 27,683 HW samples from 905 writers from 11 to 86 years old (Y. O. ). For age characterization, we split the obtained database into 6 age groups as shown in Table  1. In this dataset seniors and elders are still underrepresented and age groups  2 and  3 are overrepresented. Therefore, to ensure meaningful results we balance the database, at the 2nd-layer stage, in terms of the age categories. To do so we proceed as follows: we divide the set of words written by a given person into groups from 10 to 15 words and assign each resulting group to a virtual new writer, making sure that the generated writers do not share words. Finally, to properly evaluate the clustering and its correlation with age, we retain the same number of virtual writers for each age group.
To study the correlation between HW styles and age, we analyze for each category the size of seniors' group ( 5 ) and the size of elders' group ( 6 ) with respect to the whole population within this category. The higher the percentage is, the more characteristic the category of the aged people is. As shown in Table  3 , only 3 categories contain a significant amount of aged writers (these are categories 1, 4, and 6). Figure  5  shows the age distribution for each category. The displayed histograms show the percentage of each age group in each category relatively to the initial balanced age distribution. For example, if age group  4 in category 1 takes value 2, this group is twice more represented in category 1 than in the balanced dataset. Figure  6  shows the distribution of the center of each category in the 2nd layer over the 1st-layer clusters and the pairwise distance histogram, and Figure  7  shows some HW words of the most typical writer in each category (usually the writer whose representation is closest to the category center), when characterized by speed.
Entropy Efficiency Measures. We measure the global entropy efficiency of the clustering as defined in (3) in terms of age distribution, on the balanced dataset with the same number of writers in the 6 age groups as described in Section 3. 1. The reduction of entropy is used as a measure of how efficient the clustering is across layers in detecting HW styles that describe age tendencies. The result is shown in Table  4 , where we can observe how the 2-layer approach reduces the entropy at each layer, which means that our clustering detects HW styles with different age distributions. Having a lower entropy efficiency in layer 2 than in layer 1 demonstrates that the stability of each writer HW style across words gives additional information for characterizing HW evolution through age. Table  5  shows the entropy efficiency inside each of the categories of the 2nd layer as computed by  (2).
96, even though the true process features eigenvalues (at the steady state) that are actually of modulus 1. 28. Hence, while the researcher would not perceive any problem, she would in fact have obtained a significantly biased estimate of the forces governing local dynamics. Second, a similar problem arises if a researcher estimates the model by non-linear methods, but throws out all parameterization that imply local instability (as would be the case if estimation was done using a standard package like Dynare). The researcher would then infer local stability even when local instability is present. Whether this bias is large or small cannot be determined a priori. One needs to estimate the model allowing for limit cycles to know whether this problem is present and, if so, how substantial it is.
This point was illustrated in Section 3. -National Financial Conditions Index: Chicago Fed National Financial Conditions Index, Index, Quarterly, Not Seasonally Adjusted, obtained from the FRED database, (NFCI). Sample is 1973Q1-2015Q2. -National Financial Conditions Risk Subindex: Chicago Fed National Financial Conditions Risk Subindex, Index, Quarterly, Not Seasonally Adjusted, obtained from the FRED database, (NFCIRISK). Sample is 1973Q1-2015Q2. -Policy rate : computed from the FRED database as Moody's Seasoned Baa Corporate Bond Yield (BAAFFM) minus the spread (BAAFFM). Sample is 1954Q3-2015Q2. The real policy rate is obtained by subtracting realized inflation, computed using the "Consumer Price Index for All Urban Consumers: All Items" (CPIAUCSL from the FRED database).
For such variables, the breakdown between low-frequency trend and cycle is potentailly less problematic if the series can still be considered stationary. In contrast, if one focuses on quantity variables, for example GDP, one needs to believe that the detrending proceedure used to make it stationary is allowing one to isolate the relevant cyclical properties. This is certainly questionable as the detrending proceedure most often changes the spectral properties dramatically. To see this, in Figure  18  we report the same information we reported before regarding the spectral density, but in this case the series is real per capita GDP. Here we see that the spectra of the non-detrended data and of the filtered data have very little in common with each other. Since it does not make much sense to report the spectral density of non-detrended GDP (it is clearly non-stationary), in Panel (a) of Figure  19  we focus on the spectral density of GDP after removing low-frequency movements using the various high-pass filters.
H. 8 Alternative Estimators As another robustness test, we estimate the spectrum using the SPECTRAN package (for Matlab), which is described in ?. The spectrum is computed in this case as the Fourier transform of the covariogram (rather the periodogram as we have done thus far). Smoothing is achieved by applying a window function of length M to the covariogram before taking its Fourier transform. 19 Three different window shapes are proposed: the Blackman-Tukey window, Parzen window, and Tukey-Hanning window. The width of the window used in estimation is set as a function of the number of samples of the spectrum. In the case where no zero-padding is done (N = 270), these "optimal" widths correspond to lengths of, respectively, M = 68, 89, and 89 quarters for the three methods. 20 igure  29  shows the estimated spectrum of Non Farm Business hours for the three windows and with or without zero-padding (N = 270, 512, or 1024).
Franck Portier acknowledges financial support by the  ADEMU  project, ' A Dynamic Economic and Monetary Union ', funded by the  European Union 's  Horizon 2020 Program  under grant agreement No  649396. _________________________ The ADEMU Working Paper Series is being supported by the  European Commission Horizon 2020 European Union funding for Research & Innovation , grant agreement No  649396. Franck Portier  acknowledges financial support by the  ADEMU  project, " A Dynamic Economic and Monetary Union ," funded by the  European Union 's  Horizon 2020 Program  under grant agreement No  649396.
Patients and methods In this case-control prospective study, consecutive patients were recruited who had a diagnosis of FD, which had been confirmed genetically and/or by measurement of α-GAL activity in leukocytes (normal values: 10-20 µkat/kg). Healthy controls, matched (1:1) for age and sex, were also enrolled. The study was approved by Comité de Protection des Personnes Sud Méditerranée and was conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Data collected included the presence of hypo-or anhidrosis, renal dysfunction (defined as an estimated glomerular-filtration rate of ,60 mL/min by the Modification of the Diet in Renal Disease [MDRD] formula) or any other causes of neuropathies, ongoing ERT. Patients and controls underwent a clinical examination to determine the amplitude of the left sensory peroneal nerve (µV).
The proposed system is evaluated on the MOBIO database  (McCool et al. , 2012). This database, as well as results on face, voice and multimodal fusion, is publicly available. Moreover, MOBIO database was recorded on mobile devices, which provides a good estimation of challenges of mobile environment. For every video, database providers extract one still image and manual eyes positions are given only for those still images. This allows us to compare the performance of face verification system when face and eyes detection is performed automatically against using the manual positions of the eyes. Using the obtained results we develop an iPad proof-of-concept bi-modal biometric application. Despite being early prototype, this application processes full video and performs bi-modal biometric verification in a time of 2-3s (with input video's length of 10s).
Database and Experimental Setups The MOBIO database  (McCool et al. , 2012 ) is a bimodal (face/speaker) database recorded from 152 people. The database has a female-male ratio of nearly 1:2 (100 males and 52 females) and was collected from August 2008 to July 2010 in six different sites from five different countries. In total 12 sessions were captured for each individual. The database was recorded using two types of mobile devices: mobile phones (NOKIA N93i) and laptop computers (standard 2008 MacBook). In this evaluation, only the mobile phone data are used, with a sampling rate of 16kHz. Note that a manual annotation of eyes position for the only one image per video data is given. More details about the MOBIO database can be found in  (McCool et al. , 2012). Based on gender of the claimant, two different evaluation protocols for male and female were generated.
This is intended to better exploit the 16KHz range of the MOBIO database. Feature warping and energy-based voice activity detection are performed. The UBM model is trained with 512 Gaussians. SPro 4. 1  (Gravier, 2009)  and ALIZE 2. 0  (Bonastre et al. , 2008)  software are used to develop the proposed system. For the face verification baseline system, Gabor filters are applied with 5 scales and 8 orientations convoluted with the face images. An anisotropic smoothing pre-processing is performed and the distance between the eyes is fixed to 50. The SudFrog software  (Petrovska-Delacrétaz et al. , 2009)  is used to develop the system.
Manual Bi-Modal System In this section, the manual positions of the eyes are used, which means that the face detection and the automatic 2D facial landmark location modules described in section 2. 1 are not considered. The projection space for face verification system is learned on MOBIO training set along with FRGC database  (Phillips et al. , 2005). 50 users are used for each database, where 30 and 20 images per user are respectively taken from MOBIO and FRGC data. In a similar way, UBM model for speaker verification is learned on MOBIO training set and VOXFORGE data  (MacLean, 2012) , where 255 female (2h) and 264 male (3h50) files are present. Table  2  shows the EER and HTER computed respectively on the development and evaluation partition for the baseline uni-modal systems, using the manual positions of the eyes.
Fully Automated Bi-Modal System In this section we compare performance of the bimodal system when it uses still images from MOBIO dataset against fully processed videos. In both cases face region and eyes position are found automatically without any intervention from the user side. Still images are used "as is" without any modification, and videos are processed as described in section 3. 1. For full video processing system, two strategies of subset selection are defined: "random" frames and "thirds" frames (frames from the beginning, middle and the end). The best score of three frames from face verification system is fused with the score provided by the speaker verification system according to the fusion strategy from section 2. 3. The ERR and HTER computed using still-images and the full videos processing with "random" and "thirds" strategies selection for the face verification and the bi-modal systems are shown in Table  3.
CONCLUSIONS AND PERSPECTIVES In this paper, we propose a bi-modal biometric verification system based on voice and face modalities. The face verification part is based on LDA modelling while the speaker one is using an UBM-GMM method. Two versions of the face verification system are developed. In the first version, we suppose that only one still-image is used, while in the second version, system processes all video frames. The proposed fully automated system is evaluated on the MOBIO corpus. The results show that the full video processing gives a relative improvement of performance around 10% for the face system. In addition, the fusion between face and speaker systems relatively improves the performance by 30 to 60% comparing to the best uni-modal system. Additionally, full video processing decreases number of media where face could not be found from 25 to 0 files for enrolment data and from 118 to 9 for test data.
Potentially, it allows using more medias for score fusion and improves overall system performance. After verifying our bi-modal system on the publically available mobile data corpus (MOBIO), we develop an iPad prototype bi-modal biometric application (demo video is available at https://vid. me/wPJk). This application performs bimodal biometric user verification in a time of around 3s. Future works will be dedicated to conduct more experiments with bi-modal verification with full video processing on a mobile device using existing iPad prototype application. Moreover, different quality measures to select frame subset (such as "mouth close" and "eyes open",) will be tested to improve the verification results.
Characterizing trophic networks is fundamental to many questions in ecology, but this typically requires painstaking efforts, especially to identify the diet of small generalist predators. Several attempts have been devoted to develop suitable molecular tools to determine predatory trophic interactions through gut content analysis, and the challenge has been to achieve simultaneously high taxonomic breadth and resolution. General and practical methods are still needed, preferably independent of PCR amplification of barcodes, to recover a broader range of interactions. Here we applied shotgun-sequencing of the DNA from arthropod predator gut contents, extracted from four common coccinellid and dermapteran predators co-occurring in an agroecosystem in Brazil. By matching unassembled reads against six DNA reference databases obtained from public databases and newly assembled mitogenomes, and filtering for high overlap length and identity, we identified prey and other foreign DNA in the predator guts.
Good taxonomic breadth and resolution was achieved (93% of prey identified to species or genus), but with low recovery of matching reads. Two to nine trophic interactions were found for these predators, some of which were only inferred by the presence of parasitoids and components of the microbiome known to be associated with aphid prey. Intraguild predation was also found, including among closely related ladybird species. Uncertainty arises from the lack of comprehensive reference databases and reliance on low numbers of matching reads accentuating the risk of false positives. We discuss caveats and some future prospects that could improve the use of direct DNA shotgunsequencing to characterize arthropod trophic networks.
Using feeding trials, Srivathsan et al. [22]  and Paula et al. [31]  identified diet composition using an alternative metagenomic shotgun-sequencing pipeline of matching unassembled reads from faeces or gut contents with reference databases, and filtering for high overlap length and identity. The sensitivity and specificity of this method depends on the DNA reference databases. As there is no limitation to the identification of taxa imposed by specific molecular markers, the outcome of this approach promises a broader identification of foreign species, including prey, internal parasites and associated microbiomes. In this work, we aimed to test the power of this alternative DNA shotgun-sequencing pipeline to construct a qualitative trophic interaction network of generalist arthropod predators that are considered to be effective biocontrol agents.
These include three species of ladybirds (Coleoptera: Coccinellidae) and an earwig (Dermaptera: Forficulidae), which feed on various herbivorous insects, in particular aphids and moths, and potentially also on the juvenile stages of each other. We used the Illumina platform for shotgun-sequencing of DNA isolated from gut contents of field-collected specimens and an unfed predator as a control. In parallel, we constructed DNA reference databases through mining of publicly available sequence information, including partial and complete mitochondrial and nuclear genomes, and barcode sequences, complemented with sequences of taxa expected to interact with these predators in the sampled habitat. We were able to identify the taxonomic composition of foreign DNA in the guts of these predators. This includes those species directly preyed upon, but also produced a broader picture of the associated organisms, such as parasitoids and microbiomes, for the identification of the trophic interaction links with good taxonomic breadth and resolution.
[31]. The quality assessment for each dataset was done using FastQC v0. 10. 1 (http:// www. bioinformatics. babraham. ac. uk/projects/fastqc/). Library index adapters were trimmed using Trimmomatic v0. 30 (ILLUMINACLIP:2:30:10)  [32]. The reads were passed through a quality control check using PrinSEQ v0. 19. 2  [33] , with a minimum Phred quality score of 20. The retained reads after quality control were converted to Fasta format using a CAT and Perl script  [31].