dataesr/scientific-paragraphs-categorization huggingface dataset:
We also simulated a Gaussian additive noise on abundance data and found that it had a disproportionate effect on the least abundant MOTUs, thus distorting the taxa-abundance distribution: parameter inference was biased if the standard deviation of the noise was larger than the abundance of the least abundant MOTUs. Here again, it is possible to correct for this type of noise in empirical datasets by subsampling the sequence reads. Additive noise can be considered to model the abundance noise generated by the sequencing step or by a single PCR cycle, while the succession of several PCR cycles produces a multiplicative abundance noise. Another potential bias is due to the indirect relationship between the number of DNA barcode sequences in the sample and the number of sampled individuals. In particular, in the case of multicellular individuals, some of them may contribute disproportionately more than others.
However, these sources of bias may be generally seen as the accumulation of mutations during replication. In ecology, the only type of replication taken into consideration is demography, but DNA metabarcoding data are also the result of cellular and PCR replication processes. Since the assumptions of the neutral theory are generic and apply to any collection of replicating, mutating, and potentially dispersing entities, we could replace individual organisms by DNA barcodes as our basic replicating entities, and reinterpret the neutral parameters accordingly. As a consequence, we expect the taxa-abundance structure predicted by neutral theory to be robust as long as the DNA barcodes do not differ too much in their replicating, mutating and dispersing abilities. This study demonstrates that inferring the parameters of Hubbell's neutral model from the taxa-abundance distribution is possible even in noised biodiversity datasets.
We tested this hypothesis for a range of biologically plausible noise terms on simulated metabarcoding data, and we provide guidance for neutral parameter inference from such data. Our results indicate that whether an environmental DNA dataset really reflects the sampled community depends on noise intensity. They also suggest that this question can be answered by computing simple metrics on a benchmark dataset and comparing them to our simulations. The only way to quantify the noise level is to conduct careful benchmarking experiments, which will depend on the exact sampling and analysis protocol.
Acknowledgements We thank  Ryan Chisholm ,  Fabien Laroche  and  James O'Dwyer  for fruitful discussion. This work has benefited from " Investissement d'Avenir " grants managed by the  French Agence Nationale de la Recherche  (CEBA, ref. ANR-10-LABX-25-01  and  TULIP, ref. ANR-10-LABX-0041 ) and from anadditional  ANR  grant ( METABAR  project; PI P. Taberlet).
0. 1 pM), and is present in all mammalian body fluids including cerebrospinal fluid  7. Its concentration (in normal conditions B10 mM in serum) increases on inflammation. The X-ray structure of rabbit haem-haemopexin has been solved (PDB codes 1QHU and 1QJS). It is made up of two homologous halves (four-bladed b-propeller architecture, defining the matrix metalloproteinase MMP fold) connected by a ca. 20 residues linker. Haem is located at the boundary of the two MMP domains, two histidine residues (one from the linker, one from the C-terminal domain) acting as iron axial ligands  8. The system that allows haem uptake from haemopexin (Hxu system), comprises three proteins: HxuB and HxuA, which together form a two-partner-secretion (Tps) system  [9] [10] [11]  , and the TonB-dependent haem receptor HxuC.
Data analysis showed that a ca. 1:1 complex was formed in the latter case, and that the heat evolved during the interaction was almost equal to that observed with the full-length protein ( À 48. 5 versus À 52. 7 kcal mol À 1 in this particular experiment). This indicates that NtHpx likely provides most, if not all, of the bonds responsible for the interaction with HxuA, with little or no contribution from the C-terminal domain. The affinity constant measured for NtHpx is smaller than that determined for the fulllength protein (Supplementary Table  3 ). The results were confirmed by SEC; NtHpx formed complexes with HxuA but CtHpx did not (see Supplementary Fig. 2b, d, e ). Transmission Electron Microscopy Negative Stain. We used negative stain transmission electron microscopy (TEM) to obtain additional topological information about the interactions between HxuA and haemopexin.
5200005 keV, l ¼ 1. 775520 Å), as determined by an X-ray fluorescence scan. Two Eu sites were located using the programme SHELXD (ref. 43). Phasing was done with the programme Phaser  44  , and initial automatic model building proceeded with ARP/WARP (ref. 45). The model was then improved through iterative cycles of manual model adjustment and building with COOT (ref. 46). Final refinement was performed with autoBUSTER (ref. 47). Residues Ser1, Gly117-Gly121, Pro273, Lys274, Gly344-Thr349, Phe565-Gly571, Ile723-Asn728, Arg785-Lys793 and Ala818-Gln884 were not seen in the electron density map, and were not incorporated into the model. The structure of HxuA in complex with the N-terminal domain of haemopexin was solved by molecular replacement with Phaser 44 by using the structure of native HxuA and the N-terminal domain of haemopexin (PDB entry code 1QHU) as a template.
5% (742/769) of all residues are in favoured (98%) regions, and 100. 0% (769/769) of all residues are in allowed (499. 8%) regions. Regarding HxuA in complex with N-ter haemopexin, 94. 7% (908/959) of all residues are in favoured (98%) regions, and 99. 7% (956/959) of all residues are in allowed (499. 8%) regions. Atomic coordinates and structure factors have been deposited in the Brookhaven Protein Data Bank under the accession numbers 4RM6 for HxuA and 4RT6 for HxuA in complex with N-ter haemopexin. Isothermal titration calorimetry. Proteins were dialyzed against 20 mM Na-phosphate buffer pH 7. 0; the concentration of haemopexin and its fragments in the syringe was B100 mM and the HxuA concentration in the titration cell was B10 mM. ITC experiments were performed at 25 °C using the high precision VP-ITC system (MicroCal, GE Healthcare)  49.
In total, 35 individual 7 ml aliquots were injected from the 250 ml syringe into the 1. 41 ml titration cell, with a 6-min interval between injections, unless otherwise stated. Heat signals were corrected for the respective heat of dilution and normalized to the amount of compound injected. The enthalpy of binding (DH), affinity constant (Ka) and molar binding stoichiometry (n) were directly obtained from the titration curve fitted using the single-site binding model of the Origin7software (OriginLab), even though there might be heterogeneity in the HxuA binding sites. HxuA concentrations were measured according to the experimentally determined molar extinction coefficient. Given the protein concentration in the titration cell (B10 mM), the size of the injection and the limitation of the technique itself (Ka x substrate concentration between 1 and 1,000)  49  , the absolute value of the Ka determined should be taken with caution.
Besides, as there are conformational changes in the interactants on interaction the values should be taken as apparent Ka's. TEM and image processing. For negative stain TEM, the protein solution was diluted at 10-20 mg ml À 1 in PBS buffer. Immediately afterwards, 4 ml aliquot was adsorbed onto glow discharged carbon film coating copper EM grids, washed with three droplets of pure water and subsequently negatively stained with 2% (w/v) uranyl-acetate. The prepared grids were imaged using a Philips CM10 TEM (FEI Company, Eindhoven, the Netherlands) operating at 80 kV. The images were recorded by the 2k Â 2k side-mounted Veleta CCD camera (Olympus, Germany) at a magnification of Â 130,000. The pixel size at the sample level was 3. 7 Å. Image processing was carried out using the EMAN2 software package  50.
Acknowledgements we thank  Andrew Thompson  from the PROXIMA-1 beamline at SOLEIL for data collection and help for initial model building,  Franc¸oise Baleux  from the  Unitede Chimie des Biomolecules from Institut Pasteur  for expert technical assistance in aminoacid analysis and mass spectrometry,  Emmanuel Frachon  from the  Plate-forme Proteines Recombinantes from Institut Pasteur for protein production , and  Pascal Arnoux  for critical reading. We thank  Shirley Mueller  for reading the manuscript. B. C. and V. V. are indebted to the  Research Federation FRABio (Univ. Lille, CNRS ,  FR 3688 ,  FRABio, Biochimie Structurale et Fonctionnelle des Assemblages Biomoleculaires ) for providing the scientific and technical environment conducive to achieving their contribution. This work was supported by grants from  ANR  ( HEMESTOCKEXCHANGE ANR-12-BSV3-0022-01  and  LABEX DYNAMO ANR-11-LABEX-0011-01 ).
After generating the output data (m 2 , σ m2 ) by simulating the circuit's dynamics via the GA, the optimal input distribution will be constructed according to Eq. (19). We shall then let the circuit process an input signal sampled from that distribution and calculate the MI for both the transcriptional and posttranscriptional channels (see the flowchart in Fig. 4 ). Channel capacities will then be compared, with the goal of clarifying the conditions under which the former can be more effective than the latter.
In particular, MAML1 and MEF2C (coding for transcription factors that activate musclespecific gene expression) compete with linc-MD1 for miR-133 and miR-135 respectively  [39]. Taken together, the available evidence indicates that miRNA activity depends on the miRNA:target ratio, on miRNA target site abundance and on miRNA binding affinities. Further analyses of high throughput datasets confirm this observation  [30, 36]. One may therefore question how said factors may influence miRNAmediated post-transcriptional control. The problem however arises of quantifying the degree of control that can be exerted through miRNAs. Taking the 'channel' analogy more strictly (as done before for simpler regulatory elements  [9, 45, 52] ), one may resort to information theoretic concepts and tools to characterize precisely how well miRNAs can process fluctuations coming from the modulator nodes and transfer them to the target nodes.
Perturbed thyroid development and altered T3 to T4 ratio reported in the current study (Table  1 ) may therefore operate through a similar mechanism. Since all fetal ovaries were collected at Day 140, the profile of transcripts and proteins expressed at this stage reflects a snapshot of mechanistic changes that, by virtue of developmentally regulated gene expression, may not relate temporally to the profile of Day 140 follicles. Furthermore, we recognize that the use of whole fetal ovarian tissue rather than isolated ovarian cell types provides an interpretational limitation on the data generated. Nevertheless, some useful insights into potential mechanisms of impaired ovarian development can be inferred. For example, transcript analyses highlighted cellular processes critical for follicle assembly and development (Fig.
At slaughter, data and tissues were collected and processed as described previously  11. In brief, fetal anogenital distance (AGD) was measured and fetal ovary weights recorded. Ovaries were either fixed in neutral buffered formalin (NBF) for histological analysis or snap frozen in liquid nitrogen and stored at -80 °C for protein and RNA extraction. Maternal and fetal liver samples were wrapped in aluminum foil and stored at -20 °C until analysis for selected EDCs. EDC determinations and hormone assays. Soil samples (approximately 50 cores; 5 cm diameter; 5 cm depth) were collected from the pastures at the beginning of the main period of animal exposure, pooled within plots and analyzed for selected EDCs to provide an index of animal exposure. Soil and liver samples were analysed to determine concentrations of selected polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), polycyclic aromatic hydrocarbons (PAHs) and diethyl hexyl phthalate (DEHP) as previously described in  9.
Antibodies used were (i) Lamin A/C 1/200, mouse (ab49721, Abcam); (ii) UCHL1, 3 μg/ml, rabbit (LS-B489/9526, Lifespan Biosciences), (iii) PD1A3 (ERP57); 1:1000 rabbit (ab13507, Abcam); (iv) TAGLN, 1 μg/L, goat (AHP2034, ABD Serotec) (v) GSTM3, 1:100 rabbit (15214-1-AP, ProteinTech), (vi) GSN, 1 μg/ml mouse (ab55070, Abcam), (vii) HSPA9 (GRP75), 1:200, rabbit (ab53098, Abcam), (viii) COPS4, 1:25 rabbit (HPA036894, Sigma), (ix) BLVRA, 1:100 rabbit (H00000645-A01, Abnova). Sections were visualized with Bond Refine DAB (Leica Microsystems Newcastle Ltd. ), washed in water, and counterstained with hematoxylin. IgG-negative control sections, exposed to non-immune serum of the relevant species in the absence of primary antibody, included in all immunohistochemistry runs showed no positive immunostaining.
chem. agilent. com/earray/) using the default options (Base Composition Methodology, one oligo par target, 60bp length, 3′ Bias Design). The array was enriched with transcripts isolated and identified in the fetal developing sheep ovary by subtractive cDNA library construction (SSH)  48  and completed with control genes known to be expressed in the female and male developing gonads. New oligos added to the array as well as native Agilent oligos maintained on the array were annotated using the Sigenae SigReannot tool  49. The final version of annotation showed that the array contained oligos which can be linked to about 7,500 different genes. Labeling and hybridization were performed at the "Plate-forme Biopuces et Sequençage" (http://www-microarrays. u-strasbg. fr/). RNA integrity and quantity was checked, respectively, by Bioanalyzer (Agilent) analysis and Nanodrop ND-1000 UV measurement.
Labeling and hybridization were carried out using the Quick Amp Labeling kit for one-color labeling (Agilent, 5190-0442) and the One-Color RNA Spike-in Kit (Agilent, 5188-5282) according to the manufacturer's instructions with 200 ng of total RNA starting quantities. Arrays were scanned with the Agilent DNA Microarray Scanner Model G2565B and image analysis performed with Agilent Feature Extraction software v9. 5. 3. 1. In order to verify the quality and hybridization reproducibility of the array, hybridization tests with ovary RNAs from control animals were performed using one-color labelling and standard hybridization: 70-80% of spots were significantly hybridized and replicate experiments showed good reproducibility. Confirmatory transcript expression by real-time PCR was determined as previously described  47.
In brief, RNA was treated using RQ1 RNase-free DNase (Promega, Southampton UK) prior to cDNA synthesis using the Transcriptor First Strand cDNA Synthesis Kit (Roche, Welwyn Garden City, UK) following the manufacturers instructions. Standard curves were generated using 1:5 serial dilutions of pooled cDNA. The cDNA samples were diluted 1:50 in dH 2 O prior to analysis by qPCR. Oligonucelotide primers and hybridisation probes were designed using Primer3 software. The DNA probes were synthesised with a 5′ -FAM fluorophore and a 3′ -TAMRA quencher (Eurofins, Ebersberg, Germany). Genbank accession numbers and probe and primer sequences are listed in Supplementary Table  S8. All qPCR samples were run in triplicate and included a no temple control. qPCR was conducted using the LightCycler ® 480 Probes Master (Roche) according to the manufacturers instructions.
The 20 μl reaction vol- umes contained 1 × Probes Master mix, 0. 5 μM of each primer, 0. 1 μM probe and 5 μl cDNA sample. The thermal cycling conditions were 10 minutes at 95 °C (initial denaturing), 45 to 55 cycles of 15 seconds at 95 °C, 30 seconds at 60 °C and 1 second at 72 °C (data acquisition). The housekeeping genes GAPDH, HPRT and YWHAZ were tested for stability using geNorm, NormFinder and ANOVA analysis. The qPCR data was analysed using Roche LightCycler480 software and normalised using the geNorm method. Validation of transcriptomic data by qPCR. Transcripts differentially expressed by array in the 0-140 day exposure group compared to controls were ordered on the basis of greatest positive and negative fold change. Nine genes, including the two most differentially expressed transcripts, were selected to validate the changes observed using the microarray.
A linear mixed model was used to account for litter size effects [fixed effects: number of fetuses and treatment, random effect: ewe]. Tissue EDC concentration data were log 10 transformed prior to analysis. Mean concentrations were compared using ANOVA, with the main effects being treatment and age (maternal vs fetal). For gene array, data processing and analysis were conducted using Biocondutor packages suite (http://www. bioconductor. org/index. html) and LIMMA package  50  with the R statistical program. Raw median signal from Feature Extraction array files was used as non-processed signal and log2 transformed. Background was then subtracted locally and intra-array normalization performed by subtracting the array median signal from each spot signal on the same array. Multiple testing corrections were applied [Benjamini-Hochberg method  51  ] and differentially expressed transcripts were considered under a False Discovery Rate (FDR) of 5%.
for details). Mass lists in the form of Mascot Generic Files were created automatically and used as the input for Mascot MS/MS Ions searches of the NCBInr database using the Matrix Science web server (www. matrixscience. com). The default search parameters used were: Enzyme = Trypsin, Max. Missed cleavages = 1; Fixed modifications = Carbamidomethyl (C); Variable modifications = Oxidation (M); Peptide tolerance ± 1. 5 Da; MS/MS tolerance ± 0. 5 Da; Peptide charge = 2+ and 3+ ; Instrument = ESI-TRAP. Statistically significant MOWSE scores and good sequence coverage were considered to be positive identifications.
Acknowledgements We thank  George Corsar  and  Jim MacDonald  for the management of experimental animals. We thank  Ms Evelyn Argo ,  Mr Ian Davidson  ( Proteomics Facility ),  Ms Gillian Milne  ( Histology & Electron Microscopy Facility ) and  Mrs Margaret Fraser  ( Institute of Medical Sciences ) at the  University of Aberdeen  and  NHS Grampian  for expert technical assistance. We thank  Elodie Poumerol  ( UMR1198   INRA , transcriptome analysis),  Christine Kerr  and  Mark Osprey  ( James Hutton Institute, chemical analysis ) for technical support. This work was funded by the  European Commission Framework 7 Programme  (Contract No  212885 , http://www. abdn. ac. uk/reef/).
To overcome all these issues, the following two strategies have been proposed: the development of analytic techniques that can reliably detect HFOs in continuous wide bandwidth EEGs recorded from microelectrodes and conventional clinical electrodes and the creation of infrastructure projects that facilitate the sharing of wide bandwidth data  [9]. Specifically in terms of the first strategy, several methods for the automatic detection of HFOs have been recently developed  [11] [12] [13] [14] [15] [16] [17] [18]. Nevertheless, despite efforts to develop a reliable procedure to correctly identify HFOs, there is currently no consensus about the one method that performs best in all contexts. As solution to this, it has been suggested that different algorithms for HFO detection should be tested across similar datasets to improve the performance of automatic HFO detection methods, and these algorithms should permit the change of multiple parameters on the go if meticulous studies are required,  [9, 14].
Consequently, some groups have developed customized interfaces to test their own algorithms  [25, 26] , or they have joined professional EEG companies to develop proprietary software  [27]. Unfortunately, these applications currently remain insufficiently documented or non-available to the general public, restricting their use and validation and making difficult the further support for comprehensive HFO analyses. To achieve a practical and reliable tool for HFO analysis, we developed RIPPLELAB, a MATLAB open-source application (Mathworks1, Natick, MA, USA). This tool integrates several methods for HFO detection within an easy-to-use graphical user interface (GUI), which assists clinicians and researchers in the identification, selection and validation of HFOs. In particular, RIPPLELAB allows users to implement different quantitative measures to optimally identify and classify HFOs, reducing significantly the time required to visualize and validate putative events.
Broadly speaking, HFO detection methods can be classified in three groups: manual review, supervised detection and unsupervised detection. Manual review is performed by visual inspection of an expert with required knowledge on electrophysiological recording and signal processing to distinguish putative events from filtered artifacts  [33]. Supervised detection relies on detection methods with high sensitivity and low specificity detection, which is complemented with manual review. Finally, unsupervised detection requires methods with high sensitivity and high specificity to be effective, which is difficult to achieve in databases with dissimilar characteristics  [4, 9]. HFO detection by manual review is performed by visual inspection of EEG recordings in parallel with a frequency representation of the same recording.
Originally, this detection methodology was implemented with 1-min segments of EEG signal. In addition to this, we included the possibility to process the data thresholds in epochs of time specified by the user. The flowchart of the MNI algorithm is presented in  Fig 4. of electrophysiological data, especially at high frequency ranges. RIPPLELAB has been released under GNU Public License version 3, and the source code and documentation can be found in https://github. com/BSP-Uniandes/RIPPLELAB/. The code was originally written in MATLAB version 7. 12, and it is compatible with later versions. The tool was developed with a modular design, allowing expert users to modify and integrate new developments. RIPPLELAB was developed exclusively using MATLAB scripts; therefore, the compilation of native libraries or external functions is not required.
This multi-platform tool can be used on OS X, Linux and Windows 32 and 64-bit architectures, and it can be installed as a MATLAB App in versions The complete procedure for detection and analysis of HFOs through RIPPLELAB consists of several steps that are briefly presented in the following subsections and are summarized in Fig 6. They include different options for the general display and pre-processing of electroencephalographic data. Specific information about how to run the software is given in detail in the RIPPLELAB's user manual, which is distributed along with the source code. Step 1: Importing files and selecting channels. RIPPLELAB can import several file types including European Data Format, ( Ã. rec, Ã. edf), Nicolet files ( Ã. eeg), Neuralynx files ( Ã. ncs), EPILEPSIAE file format ( Ã.
Distinctively, the HIL detector focuses on events at the frequency range between 180 and 400 Hz, and the MNI method centers its detection over the range from 80 to 450 Hz. Another difference across methods is their definition of event length. Specifically, the STE and MNI detectors state 6 ms and 10 ms, respectively, as event minimum lengths. A comparison of the implemented methods with different parameters was already performed in  [14] , where it was indicated that each of the detections methods can be optimized according to the database and the HFO characteristics that the researcher requires. The problem of selecting the optimal parameters is not trivial, and it has not been solved yet  [9]  When selecting a method to implement, we first suggest setting the Frequency Limits parameter to the range of interest, and subsequently carry out the detection with all methods over a short-time interval of a well-known signal (e.