dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated data In order to test the performance of our proposed method under controlled noise structures, we first applied the method to synthetic data, in which noise, motion artifacts, and evoked response were simulated. We simulated serially correlated noise via tools in Matlab's Econometrics toolbox with an AR(5) model resulting in a signal with standard deviation of 0. 05µM, which was comparable to the real data set discussed in the next section. The AR model order of 5 used for simulated data was close to the median optimal model order of 6 for the experimental data. The AR coefficients were taken as the median values across subjects from an AR(5) model fit to the experimental data. Two sets of AR coefficients were used in the simulations based on AR(5) model fits to the HbO 2 and Hb channels, respectively.
Experimental data In addition to the completely synthetic dataset, we used experimental "resting state" data from a study involving 22 normal, healthy children (3-5 years old) as a source of real physiological noise and motion artifacts. All subjects provided written parental informed consent, and this data was collected as part of a larger study of functional brain activity  [27]. Each "resting state" scan lasted approximately three minutes and was acquired while the children watched a short video on the computer (Disney films). Although this is not true resting state, there were no specific cued tasks during the scan, thus serving as an effective estimate of background physiology for the purpose of this study. The experimental data were acquired at 690nm and 830nm wavelengths at a sampling rate of 4Hz with a probe consisting of 24 source-detector measurements over the prefrontal cortex.
The optical density changes were converted to oxyand deoxy-hemoglobin prior to input into the analysis method via the modified Beer-Lambert law. No other preprocessing was performed. In order to test the performance of the various regression methods, a known simulated evoked response was added to the experimental "resting state" data on a subset of the channels (50%), and an ROC analysis was performed. Evoked responses were generated using the event-related (1s on, 19s off, 15 trials) and block design (10s on, 20s off, 10 trials) paradigms. Each channel of data was concatenated with itself in order to accommodate the length of the experimental designs (300s). We simulated evoked responses with amplitudes ranging from 0. 01-0. 1M on the oxy-hemoglobin channels. Evoked responses were added at half amplitude to the deoxyhemoglobin channels.
Analysis methods We tested the performance of the proposed AR(P)-IRLS algorithm as well as OLS with and without AR(1) pre-whitening. Additionally, we tested wavelet based motion correction  [11]  followed by OLS regression (Wavelet-OLS below) and spline interpolation based motion correction  [10]  followed by OLS regression (Spline-OLS below) in order to validate the performance of the proposed method's ability to correct motion artifacts. The wavelet based motion correction was implemented using matlab function "hmrMotionCorrectWavelet" that is included in the HOMER2 software package (www. nmr. mgh. harvard. edu/DOT/resources/software. html) using an interquartile range of 1. 5, as suggested in the function's documentation. The spline based motion correction was implemented using the functions "hmrMotionCorrectSpline" and "hmrMotionArtifactByChannel" in HOMER2.
Although this approach to pre-whitening via an AR model based transformation is common practice in linear regression, this exact approach is not widely used in neuroscience applications. In fMRI analysis (which typically deals with data sets on the order of 64x64x30 = 122,880 voxels) the estimation of an AR model for each voxel is not practical due to the high computational time required. For example, in the fMRI analysis program SPM8  [21]  a global estimate of an AR(1) model is included in the parameterization of the covariance matrix in the context of restricted maximum likelihood (ReML) rather than fitting each voxel independently. In fMRI analysis, this approximation is justified because the time between subsequent measurements is much longer (∼2-3s) than fNIRS, such that serial correlations are less of a concern.
In the case of fNIRS data, which has both higher sample rates and lower number of time series measurements, these approximations are no longer appropriate. The use of simulated AR noise with simulated artifacts allowed us to control the type, number, and amplitude of motion artifacts. The simulated artifacts were relatively large compared to experimental data and provided a good test of the AR(P)-IRLS method's ability to correct for motion artifacts. The AR(P)-IRLS method outperformed spline interpolation based motion correction  [10]  and wavelet based motion correction  [11]  methods when motion artifacts were present. Despite showing visual improvements in the recovered hemodynamic response functions, such as in Fig. 2 , Wavelet-OLS and Spline-OLS did not offer any statistical advantage in the ROC analysis with the experimental data.
It is possible that the parameters used for these methods were not optimal for the experimental data set in this study. Further ROC analyses should be performed for data collected under a wide range of experimental conditions and subject populations to validate and generalize the comparison of these methods. In simulations without artifacts the algorithm did not negatively affect the analyses. The practical implication of this is that the algorithm can be applied to every data set without making a subjective judgment about whether artifact correction is needed or not. The method will improve performance when data is contaminated by motion and preserve normal performance when data are free of artifacts. Overall, our results suggest that application of robust regression methods after pre-whitening represents a reliable method for correction of motion artifacts.
Gene expression analysis by quantitative PCR qPCR assays were carried out on the Light-Cycler 480 System (Roche). The 5 µl-volume reaction consisted of 1X Light-Cycler 480 master mix, 0. 5 µM of each primer and 1 µl of cDNA diluted at 1/8 in sterile ultra-pure water. qPCR assays were performed in triplicate, and primer pair efficiencies (E) were calculated by five serial dilutions of pooled cDNA ranging from 1/2 to 1/64 in sterile ultra-pure water, in duplicate with each primer pair. Primer pair efficiencies were calculated from the given slopes in LightCycler software according to the equation: E = 10[-1/slope]. List of primers used to amplify the 42 immune related genes and the three reference genes are presented in Table  S1. qPCR assays were submitted to an initial denaturation step of 15 min at 95°C followed by an amplification of the target cDNA (35 cycles of denaturation at 95°C for 10 s, annealing at 57°C for 20 s and extension time at 72°C for 25 s) and fluorescence detection.
Relative expression was calculated using the method described by Pfaffl  [21] , using the mean of the measured threshold cycle (Cq) values of three constitutively expressed genes (Cg-EF1 [GenBank AB122066], Cg-RPL40 [GenBank FP004478] and Cg-RPS6 [GenBank HS119070]) to normalize the measured Cq values of target genes.
Polymorphism detection: melting curves analysis and sequencing At the end of the qPCR reaction and after an initial 10 s denaturation step at 95°C, a melting curve was obtained from a start temperature of 65°C to a final temperature of 95°C, with an increase of 0. 06 °C/s with continuous fluorescence detection. When variation of 0. 5°C on the melting temperature was detected between samples, a classical PCR was performed with a different pair of primers which amplified the complete open reading frame (ORF) using Taq DNA polymerase according to manufacturer's protocol (Promega). List of primers are presented in Table  S2. Then, 1X Syber Green was added to the PCR product and melting temperatures of amplicons were obtained by applying melting curve program on the Light-Cycler 480 System (Roche).
The data were analyzed using Light Cycler 480 software version 1. 5. 0. 39 and the 2nd derivative max algorithm. Gene copy number was calculated by absolute quantification. Standard curves for gDNA and pDNA of each gene were constructed from the mean Cq values using linear regression, from which slope and correlation coefficients were calculated. Using an estimation of the C. gigas genome size of about 824 Mb  [22] , the number of genome equivalents represented in 20 ng of gDNA was then calculated. Quantification was finally achieved by plotting the Cq values on the standard curve obtained with the serial dilutions of pDNA.
Sequence and statistical analysis The multiple alignments were generated using the BioEdit program  [23]  and translated sequences were obtained using ExPASy Translate tool  [24]. Hierarchical clustering was determined using Multi Experiment Viewer  [25]. Prediction of signal peptide was performed with the SignalP program  [26]. The amino acids under selective pressure were detected by the ratio of the rate of non-synonymous substitutions (dN) to the rate of synonymous substitutions (dS) for each codon, calculated with Selecton web server  [27] , based on M8 evolutionary model which allows for positive selection. Statistical significance of results (p<0. 05) was assessed using the likelihood ratio test (LRT) which compares the log likelihood of M8 model to the log likelihood of M8a alternative model, which allows for negative and neutral selection.
Statistical analyses were performed using STATISTICA software version 7. 1 (StatSoft) using Shapiro-Wilk test to test the normality of the data (significant value: p<0. 05); Mann-Whitney U test to compare melting temperature between oyster lines at the level of oyster groups (significant value: p<0. 05) and for expression analysis, Student's t-test to compare melting temperature between oyster lines at individual level (significant value: p<0. 05), Fisher test to compare variance of melting temperature within oyster lines at individual level (significant value: p<0. 05), and Spearman's rank correlation coefficient to compare expression level and gene copy number (significant value: p<0. 05 or p<0. 1).
05 and Fisher test, p>0. 05), suggesting the absence of polymorphism on these genes (Figure  3 ). By ORF sequencing. To gain more evidence about the polymorphism predicted by the melting temperature analysis, we sequenced the entire ORF of the AMPs Cg-Defh, Cg-Defm and Cg-Prp amplified from three groups of ten oysters per line. A mean of 13 exploited sequences for each AMP per oyster line seems to confirm that Cg-Defh, Cg-Defm and Cg-Prp present a higher polymorphism in the L line compared to the H line (Figure  4 ). Sequences are available at NCBI: Cg-Defh-L line (GenBank ID: JF766743-JF766761), Cg-Defh-H line (GenBank ID: JF766762-JF766768), Cg-Defm-L line (GenBank ID: JF766718-JF766731), Cg-Defm-H line (GenBank ID: JF766732-JF766742), Cg-Prp-L line (GenBank ID: JF766769-JF766782) and Cg-Prp-H line (GenBank ID: JF766783-JF766798).
Although we confirmed the existence of the major polymorphic sites in the public databases for these AMPs and we identified new variants, we cannot exclude the presence of other polymorphic sites that only greater sequencing efforts can determine. Strikingly, Cg-Defs and Cg-Prp display higher variability of gene expression and/or sequence polymorphism in the L line. Indeed, compared to the L line, H oysters showed a lower number of genes and a lower gene expression for Cg-Defs, and lower sequence polymorphism for Cg-Defm and Cg-Prp. One explanation could be that the selection process of oyster lines has decreased the genetic diversity of the H line, selecting only one expressed variant with better performance. Higher numbers of genes and/or higher expression of AMPs could be detrimental to the host, supported by the evidence that a higher number of AMP gene copies is not necessarily related with an enhanced immune protection.
Acknowledgements We are very grateful to  Delphine Destoumieux-Garzón  for helpful discussion,  Timothy Green  for the English corrections of the manuscript,  Arthur Escalas  for his advice in statistical analysis and  Marc Leroy  for technical assistance. Oyster used in this work were produced and maintained in controlled conditions through technical facilities of the  Station de Bouin-Laboratoire Génétique et Pathologie , and the  Aquaculture Platform located at Ifremer Palaeovas. Data used in this work were partly produced through molecular genetic analysis technical facilities of the  "Plate-forme génotypage séquençage CeMEB  ( Centre méditerranéen de l'Environnement et de la biodiversité )"  And the '"Plate-forme qPHD UM2/Montpellier GenomiX ". P. Schmitt was supported by a doctoral funding from  Program Becas Chile-CONICYT.
Fleshy fruits are important worldwide crops that are rich sources of useful and functional compounds in the human diet. Although fruit ripening has been extensively studied, early fruit development has not been paid much attention despite its contribution to the sensorial and nutritional quality of the fruit. This study aimed at identifying candidate genes involved in early fleshy fruit development that can contribute to the control of final fruit size and composition by comparative analysis of tomato and grape genes. By mining public sequences and microarray database, we identified 23 transcription factors belonging to 14 classes (AP2-EREBP, ARF, bHLH, bZIP, C2C2-GATA, FHA, GeBP, GRAS, HB, LIM, MYB, PBF-2-like, SBP and WRKY) as candidate regulatory genes for early fruit development. The function of these candidate genes will be confirmed by several reverse genetic approaches using the miniature tomato cv.
Identification of common genes between tomato and grape To identify common genes between tomato and grape, unigene data sets of the Tomato SGN Unigene (SGN build Tomato200607 #1) and the NCBI Vitis vinifera UniGene (Build #9) were retrieved from SGN and NCBI ftp site, respectively. Using the TBLASTX program  (Altschul et al. 1990 ) with a cutoff e-value at 1e-10, the unigene sequences of tomato were compared with those of grape and vice versa. Reciprocal best hit pairs were determined as common gene pairs.
Transcriptome data mining Publicly available microarray data of fruit development series in tomato and grape were obtained from TFGD (http://ted. bti. cornell. edu/cgi-bin/TFGD/miame/experiment. cgi?ID= E025; data set E025;  Osorio et al. 2011)  and PLEXdb (http:// www. plexdb. org/modules/PD_browse/experiment_browser. php?plex_name= GrapePLEX; data set VV5;  Deluc et al. 2007) , respectively. For TOM1 cDNA spotted array data set, the printtip LOWESS normalized data were used for further expression analysis. For grape microarray data set VV5, only the data of Cabernet Sauvignon berries from well-watered plants were used. The Affymetrix raw CEL files were processed for background correction, quantile normalization and a median polish with the robust multiarray averaging (RMA) method using RMAexpress program  (Bolstad et al.
2003). Differentially expressed genes during fruit development were determined by one-way ANOVA with a false discovery rate (FDR) correction (p<0. 05; Benjamini and Hochberg, 1995). Pearson's correlation k-means clustering of the differentially expressed probes corresponding to the common genes were performed using TM4: MeV 4. 3 software (http://www. tm4. org; Saeed et al. 2003).
Comparative transcription analysis of common genes To identify common genes expressed in early stage of fruit development in tomato and grape, data-mining of publicly available microarray data of tomato and grape was performed. Among the 8,229 unigenes common to both tomato and grape, 2,503 unigenes (represented by 2,757 spotted probes) were present on tomato TOM1 array and 4,977 unigenes (represented by 6,203 spotted probes) were present on grape Genechip array. ANOVA analysis with a FDR cutoff of 5% identified 1,713 probes in TOM1 and 3,922 probes in grape Genechip corresponding to genes differentially expressed throughout fruit development in tomato and grape, respectively. Among these, 885 probes representing genes included in both the tomato TOM1 and the grape Genechip were used for subsequent analysis.
On arrival at the Children's Hospital Oakland Pediatric Clinical Research Center, the study participants emptied their bladders, if able, and consumed a bolus of 750 mL drinking water within 5 min. In the 30 min after the water bolus, study staff collected and reviewed the week's diet records and downloaded physical activity data from the BodyMedia armband. Within 1 h after the water bolus, post bolus urine was collected. Blood was collected 90 min after the water bolus.
Dietary intake The diet records were reviewed each week and entered into Nutrition Diet Systems (NDS-R) software  (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008)  Nutrition Coordinating Center, University of Minnesota, Minneapolis, MN) by one certified NDS diet interviewer. The records were reviewed for completeness, and missing details were addressed to the participant during the clinic visit. The interviewer probed for beverage intake. The NDS data were used to estimate the 7-day mean daily intake of drinking water, water from other beverages, water from food, and total water (the sum of drinking water, water from other beverages, water from food, and metabolic water) for each participant. Drinking water was defined as tap, spring, mineral, or unsweetened sparkling water.
Other beverages were defined as any beverage other than drinking water, following the food group codes in the NDS software. Food water was estimated as the difference between total dietary water and water from beverages. Metabolic water was estimated from the estimated protein, fat, and carbohydrate intakes, assuming that oxidation of protein, fat, and carbohydrate yield 0. 41, 1. 07, and 0. 6 mL water/g, respectively  (Buskirk and Puhl 1996). Intake of drinking water intake was expressed in absolute terms (mL) as well as relative to total water intake to index the proportion of hypo-osmotic water in the diet. Most beverages other than water have an osmolality over 285 mmol/kg, the threshold for antidiuretic hormone (ADH) release  (Verbalis 2003). The NDS data were also used to estimate 7-day mean daily intake of total energy, protein, carbohydrate, sodium, and caffeine intake.
Statistics Stata software (Stata SE, version 9. 2; StataCorp, College Station, TX) was used for all analyses. For each variable studied, within-person change (fixed effects) models that allow each participant to serve as his own control, were used to test for statistically significant change over time relative to the 2-week baseline period. Each parameter was the dependent variable in the model with time as independent variable. To account for the U-shaped intervention design (A-B-C-A), time was represented either as a set of dummy variables (one for each period or week), or as continuous time with a timextime interaction term. Time was expressed as a continuous variable to test for linear trends across periods A-B-C. Given the small sample size and multiple parameters tested, the analysis goal was to only identify variables that showed both a statistically significant U-shaped change in the fixed effect model (*P < 0.
mL water bolus as hypo-osmotic stimulus Each week, study participants were given a 750 mL bolus of drinking water as hypo-osmotic stimulus following overnight water restriction. Figure  3  describes the mean AE SEM urine osmolality before and after this bolus. Despite significantly lower first morning urine osmolality 1 The data presented represent the mean AE SEM of 2 weekly measurements per period per participant, n = 5 except for week 8 where n = 4. 2 The serum indices were determined from blood collected 90 min after a 750 mL bolus of drinking water, following overnight water and food restriction. 3 The Day urine indices were determined from urine collected on the day before each clinic visit, after waking until 11 PM. The Day mean values include all time points for all participants. The same pattern of results is observed when the analysis is restricted to Day urine samples from complete 24-h collections only.
Experimental evaluation We evaluate the performance of the proposed MAP estimation algorithms compared to the conventional ML estimation algorithms and to two baseline approaches for the separation of two-channel convolutive mixtures of three sources. We target a semi-informed scenario where the relative positions of the sources and the microphones are known, but nothing is known about their absolute position in the room nor about the source signals. The reverberant character of the data calls for the use of fullrank spatial covariance matrices and subsource mixing matrices, i. e. , R j = 2 for all j. We do not constrain the source variances v j (n, f ), so as to measure the improvement due to the priors alone. The full Matlab code for our experiments can be downloaded from  [36].
45 × 3. 55 × 2. 5 m as in  [37] , and the microphone spacing and the source-to-microphone distances are fixed to d = 5 and r = 50 cm, respectively. We generated room impulse responses via the image method  [33]  using the Roomsimove toolbox c for four reverberation times: T 60 = 50, 130, 250, or 500 ms, which we convolved with 10 s speech signals sampled at 16 kHz. For each T 60 , 6 mixture signals were generated using speech signals from the Signal Separation and Evaluation Campaign (SiSEC)  [37] : 2 mixtures of English and Japanese male speech, 2 mixtures of English and Japanese female speech, and 2 mixtures of male and female speech, resulting in 24 mixture signals in total. Training data were generated in a similar fashion by simulating room impulse responses for 20 random source directions of arrival for each of 20 random microphone pair positions and orientations for the same d and r as above.
Learned hyper-parameter values Regarding training, preliminary experiments showed that the functions (  21 ) and (42) are concave in practice. Hence, we maximized them using Matlab's fmincon optimizer (Mathworks Inc. , Natick, MA, USA). The resulting hyperparameter values are shown in Table  1. As expected, the total power of echoes and reverberation σ 2 rev = σ 2 1 + σ 2 2 strongly increases with T 60 , such that the direct-to-reverberant ratio is 14 dB lower when T 60 = 500 ms than when T 60 = 50 ms. The variance of the inverse-Wishart prior, which is inversely related to m  [32] , decreases with T 60. The ratio σ 2  1 /σ 2 rev decreases with T 60 , which indicates that the echoic and reverberant part of the impulse responses becomes more and more diffuse.
The attenuated total reflection (ATR) mode was used with a split pea attachment (Harrick-Scientific Corp. ) equipped with a silicon hemispherical internal reflection element. The maximum depth of analysis was were measured manually on digital images using Archimed and Adobe Photoshop software. A cell shape factor was defined as the ratio of the real perimeter to the apparent perimeter if the cell was considered a circle with a similar area: S = P/[2 × (II × A)] 1/2 , with S: Shape factor, A: Cell area (μm 2 ) and P: Cell perimeter (μm). Statistical analysis. Data are presented as mean ± standard errors. Cell culture results were analyzed using a non-parametric Kruskal-Wallis one-way analysis of variance with Dunns's post hoc test. Morphological data were evaluated by an unpaired Student's t-test.