dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, the missing clinical data may limit our power to detect correlations between the brain morphometric measurements and the cognitive and clinical data. The strong results of this multisite genetic-first neuroimaging study provide a robust characterization of 16p11. 2 deletion and duplication effects on neuroanatomy. The deletion and duplication of the same genetic interval may affect brain regions in opposing ways, but other structures are preferentially altered by one of the two CNVs. The morphometric effect sizes are comparable to those previously recorded on cognitive traits. Results are generalizable across sites, computational methods, age, sex, and ascertainment for psychiatric or neurodevelopmental disorders. This suggests that these brain alterations are related to the risk conferred by the CNVs rather than the clinical manifestations observed in carriers.
ACKNOWLEDGMENTS AND DISCLOSURES This work was supported by  Simons Foundation  Grant Nos. SFARI219193  (to RLB) and  SFARI274424  (to AR); a  Bursary Professor fellowship  of the  Swiss National Science Foundation (SNSF)  (to SJ);  SNSF  Grant No. 31003A160203  (to AR);  SNSF   Sinergia Grant  No. CRSII33-133044  (to AR);  SNSF National Centre of Competence in Research Synapsy  and project Grant No. 32003B_159780  (to BD);  Foundation Parkinson Switzerland and Foundation Synapsis  (to BD);  European Union Seventh Framework Programme  ( FP7/2015-2018 ) Grant No. 604102  ( Human Brain  Project) (to BD); the  Roger De Spoelberch and Partridge Foundations ; a  Canada Research Chair in neurodevelopmental disorders  (to SJ), and a chair from the  Jeanne et Jean Louis Levesque Foundation  (to SJ).
This research was enabled by support provided by  Calcul Quebec  (http://www. calculquebec. ca) and  Compute Canada  (http://www. computecanada. ca). We thank all of the families at the participating Simons Variation in Individuals Project (VIP) sites, as well as the  Simons VIP Consortium. We appreciate obtaining access to imaging and phenotypic data on SFARI Base. Approved researchers can obtain the Simons VIP population dataset described in this study by applying at https://base. sfari. org. Statistical support was provided by data scientist  Steven Worthington , at the  Institute for Quantitative Social Science, Harvard University. We are grateful to all families who participated in the 16p11. 2European  Consortium. The authors report no biomedical financial interests or potential conflicts of interest.
EEG data preprocessing Data preprocessing and analysis were performed using Matlab (MathWorks Inc) along with the FieldTrip toolbox for Matlab  (Oostenveld et al. , 2011). Data were first downsampled to 250 Hz. Independent component analysis (ICA) was used to identify and remove ocular artifacts from the data  (Jung et al. , 2000) , and trials with artifacts remaining after this step were excluded by visual inspection. I extracted data epochs time locked to the first button press after the start of the trial. Each data epoch covered the window of time extending from -3. 5 to ϩ1. 0 s relative to the button press.
Data analyses and statistics Behavioral I define the "waiting time" (or just "wait time") as the amount of time, in seconds, that elapsed from the beginning of the trial until the subject first pressed the button. 'W' time  (Libet et al. , 1983)  was the time, relative to the onset of the button press, that the subject reported first having been aware of the urge or decision to press the button. This was recorded by taking the clock time at which the subject reported having had the urge and subtracting it from the clock time of the actual movement. The correlation between waiting time and W time was computed using Pearson's correlation coefficient applied both to the pooled data from all subjects and also separately for each subject. In the latter case, the resulting r values across subjects were tested for significance (difference from zero) using Wilcoxon's signed rank test.
EEG EEG data epochs were sorted according to waiting time into the lower 33rd percentile (short waiting time) and the upper 33rd percentile (long waiting time), and averaged together within each group to compare the shape of the RP for short and long waiting times. Differences in the amplitude of the RP at each time point were tested for significance using a signed rank test and then subjected to a cluster-based permutation test to correct for multiple comparisons. Unless specified otherwise, the RP was measured at electrode C1 (C2 if left handed).
Generation of pink noise Normally, when simulating an accumulator process, a series of computer-generated Gaussian-distributed pseudorandom numbers is used to instantiate the (white) noise term. For optimal performance it is best to generate the entire time series of pseudo-random numbers all at once at the beginning of the simulation, and then step through it, rather than generating a single random number on each iteration. Having the entire time course of the noise in hand allows one to change the spectral properties of the noise as desired. Power-law or 1/f noise has spectral power that is inversely proportional to frequency, roughly following a negative-sloping line in log-log coordinates  (He et al. , 2010 ; Fig. 1 ): P ϰ 1/f ␤ with ␤ commonly referred to as the "1/f exponent. " For the simulations reported here, I first generated a time series of Gaussian pseudorandom numbers and then altered the spectrum of this noise as follows: first the signal was converted into the frequency domain via a fast Fourier transform (fft in Matlab); then a given (negative) slope was imposed on the power spectrum by multiplication with a log-linear function having the desired slope; and then the signal was converted back into the time domain via an inverse Fourier transform (ifft in Matlab).
Data fitting For separate runs of the simulation, the average time course of the model output or the stochastic input to the model was fit to the average RP across subjects. The time courses were time locked to the first crossing time, which is of course always determined based on the output of the accumulator. For both the RP-as-input and RP-as-output simulations the fitting procedure included a scaling factor whereby the amplitude of the simulated RP was scaled to that of the empirical RP. In each case, the normalized distribution of first-crossing times of the model was simultaneously fit to the normalized empirical waiting time distribution. Fitting was performed using multidimensional unconstrained nonlinear minimization (Nelder-Mead; function fminsearch in Matlab) on the mean-squared error between the simulated and empirical data.
The interpretation of these components is unambiguous, with one notable exception for the PAY time series, whose IMF6 exhibits mode mixing; i. e. , it has a total range that overlaps some of the modes in the high-frequency band. Nevertheless, it will be subsequently shown that it is indeed these components that account for variability at the 1-year timescale. Lastly, the components indicative of low-frequency variability on timescales greater than 1 year are discussed. The intrinsic timescales found in these IMFs seem to match once more those pertaining to the so-called quasi-biennial oscillations (QBOs) that have been observed in solar activities and proxies with periodicities between 0. 6 and 4 years  (Bazilevskaya et al. , 2015; Kolotkov et al. , 2015; Vecchio et al. , 2012) , as well as in meteorological data like  Harrison (2008)  who identifies a 1.
This suggests that what makes our brains non-random is not in the firing pattern but lies in the respective timing of the firing activity between different neurons. In the more recent "Neuronal Melodies," the same approach was applied to human neuron recordings and was published on the Internet Archive by Alain Destexhe in 2012 (http://www. archive. org/details/NeuronalMelodies). Here, a dataset of 92 neurons was used-these neurons were simultaneously recorded in the temporal cortex of human subjects, and the main originality of these data is that excitatory and inhibitory neurons could be discriminated  (Peyrache et al. 2012; Dehghani et al. 2016). This allowed us to create a musical sequence with two instruments, a woodblock for excitatory neurons and a xylophone for inhibitory cells. From these melodies, one can hear that the distributed firing activity is almost identical during dreaming compared to wakefulness, which emphasizes the high similarity between these two different brain states.
How to Convert Neural Recordings into Music? To convert neural recordings into music, and in particular spikes recordings, we will proceed by examples. We take advantage of the fact that neuron spikes are impulses, well defined in time, so we can use them to trigger particular musical "events". These events are traduced through the MIDI protocol as "notes" having fixed length and fixed velocity and mapped onto the keyboard (initially, white keys of the C-major diatonic scale) in a random order of cells identification (from 1st to 100th cell). This MIDI mapping into a sequencer framework (Ableton, Live9 software) will allow easy affectations and routings of each event to any kind of sound synthesis by conventional MIDI manipulations. This way, when a neuron emits a spike, a sound is played, in a way that each neuron has his private sound, as shown in the examples below.
Early attempts  (Aertsen and Erb 1987)  focused only on awake activity in monkey and did not compare with sleep stages. Future musical experiments will explore the remaining set of data recorded during the occurrence of epileptic seizures and the exploration of additional parameters in present in electrophysiological signals (LFPs) to drive other modulations in sound synthesis. Moreover, other scales and melodic modes such as Ionian, Dorian, Mixolydian, or Aeolian among others should also be explored further in conjunction with these recordings of neural activities. Future approaches should examine other brain signals, such as the local field potential and electrocorticogram, which are also available in human microelectrode recordings. The electroencephalogram (EEG) and magnetoencephalogram (MEG) are also among possible signals to be exploited.
Acknowledgements Work supported by the  Centre National de la Recherche Scientifique (CNRS)  and the  European Community  (The  Human Brain  Project, grant nb. FP7-604102  and  H2020-720270 ). The songs described here are freely available on different sites on the Web, such as in Soundclick (http://www. soundclick. com/spikiss),The Internet Archive (https://archive. org/details/Spikiss, https://archive. org/details/Spikiss-Sleep,https://archive. org/details/Spikiss-Rem),or SoundCloud (https://soundcloud. com/search?q=spikiss).
W heat was domesticated in the Fertile Crescent over 10,000 years ago  1. Today, wheat is cultivated more extensively than any other food crop, covering more than 244 million ha and yielding more than 750 million tons annually (Food and Agriculture Organization Corporate Statistical Database (FAOSTAT)). It is a major component in human diet, providing starch, proteins, vitamins, dietary fiber, and phytochemicals  2. Wheat crops are subjected to numerous types of biotic and abiotic stresses, with fungal diseases comprising one of the most serious threats to wheat production. Yellow (stripe) rust disease of wheat is caused by the fungus Puccinia striiformis f. sp. tritici (Pst). Due to the rapid evolution of the pathogen and development of new virulent races, severe yield losses occurred during recent decades in Europe and worldwide  3.
Quantification of fungal biomass within infected leaves. After inoculation with Pst isolate #5006, leaf segments (middle, second leaf) of the resistant Avocet + Yr15 and susceptible Avocet S NILs were cut every 2 days (1-14 dpi) from different plants at each time point, with eight biological replicates collected for each line at each time point, and used for quantification of fungal biomass by chitin measurement  50. Three technical replicates were made for each tissue sample. Fluorescence was measured on a SpectraMax M2e Microplate Reader (Molecular Devices, Sunnyvale, CA, USA), using 485-nm excitation and 535-nm emission wavelengths, a 1. 0 s measurement time, and a cross pattern of well-scanning, yielding an average measurement per well. Statistical analyses of parameters associated with the accumulation of fungal biomass were determined by the maximum likelihood estimation method (SPSS) following the logistic population growth model.
All adjustments were applied equally across a whole image using the free software paint. net (https://www. getpaint. net/). Development of a high-density genetic map. We assigned Yr15 to deletion bin Sat0. 31 of chromosome arm 1BS by developing and mapping dominant marker uhw250, as well as cleaved amplified polymorphic sequences  51  markers uhw252 and uhw254, based on expressed sequence tags assigned to Sat0. 31  52  (Supplementary Table  2 ). Using the GenomeZipper approach  24, 51  , which infers collinearity with orthologous regions of B. distachyon, O. sativa, and S. bicolor (Supplementary Table  3 ), eight additional markers were developed and the Yr15-containing region was reduced to a 0. 3 cM interval between flanking markers uhw264 and uhw259 (Supplementary Fig. 4a ). Single-nucleotide polymorphism (SNP) markers RAC875_c826_839 and BS00022902_51 derived from the 15K wheat SNP array (Trait Genetics GmbH, Gatersleben, Germany) were converted to Kompetitive Allele-Specific PCR (KASP) markers using PolyMarker (http://polymarker.
4b-d ). The BAC-end sequence (BES) of clone TaaCsp364O11 was used to develop marker uhw267. Screening of the CS library with uhw267 yielded clone TaaCsp691F7. The BES of clone TaaCsp729H14 was used to develop marker uhw268, which was mapped distal to uhw259 (Supplementary Fig. 4c ). These efforts generated two BAC contigs, flanking Yr15, with a gap between them, which was bridged using a 1BS physical map constructed from a CS 1BS-specific BAC library  24. The contigs of the CS 1BS physical map that cover the region corresponding to Yr15 were assembled as follows  24. A wheat BAC library composed of 55,296 BAC clones was constructed from CS 1BS. In total, 49,412 high-quality fingerprints, representing 14. 4 1BS equivalents, were assembled into 57 long scaffolds covering 83% of chromosome 1BS using the LTC software  24.
Two of these proteins were used for further phylogenetic analysis (B. distachyon BRADI2G38370. 1 and O. sativa LOC_Os01g20880. 1). The analysis included 21 protein sequences. All sequence positions that contained gaps, as compared with WTK1-1B from G25, were eliminated from the analysis. Therefore, the analysis was conducted using 263 amino acid residues out of the 665 of the full-length WTK1 predicted from the DNA sequence of clone G25-141. The evolutionary history of WTK1 was inferred using the neighborjoining algorithm  58  based on evolutionary distances computed by the Poisson correction method  59. The quality of the derived phylogeny of WTK1 was assayed using a bootstrap test with 10,000 replicates  60. The analysis was performed with MEGA7 (https://www. megasoftware. net/). Distribution of WTK1 among various Triticeae species.
The following light ranges were used: GFP (Ex 488 nm, Em 500-543 nm); 4′,6diamidino-2-phenylindole (Ex 405 nm, Em 430-550 nm); chlorophyll autofluorescence (Ex 488 nm, Em 673-725 nm). A WTK1 cDNA clone was prepared as described under Isolation and sequencing of full-length WTK1 cDNA. All expression vectors were created with the Multisite Gateway Three-Fragment Vector Construction Kit (Invitrogen). The 5′ entry clone was prepared by the BP reaction between pDONRP4-P1R and a 35S promoter fragment amplified from pBI221 (GenBank accession #AF502128. 1) using primers attB4F35S and attB1R35S (Supplementary Table  9 ). The entry clone for the full-length WTK1 was created by carrying out the BP reaction between pDONR221 and the PCR product amplified from the WTK1 cDNA clone with primers attB1FKinase and attB2RYr15 (Supplementary Table  9 ).
The entry clone for the N-terminal and C-terminal kinase-like domains were similarly prepared using primers attB1FKinase and attB2Rkinase, and attB1Freg and attB2RYr15 (Supplementary Table  9 ), respectively. The 3′ entry clone was prepared by carrying out the BP reaction with pDONRP2RP3 and a GFPnos fragment amplified from pVEC8_GFP (GenBank: FJ949107. 1) by PCR using primers attB2FGFPnos and attB3RGFPnos (Supplementary Table  9 ). The final clones used for transient transformation and expression in protoplasts were created by the LR reaction between the respective entry clones and the destination vector pDESTR4-R3 according to the instructions of the Gateway Kit. Analysis of WTK1 kinase-like domains. A BLASTP search of the NCBI nonredundant protein database was used to assign the WTK1 kinase-like domains to a specific kinase superfamily and to search for proteins similar to WTK1  62.
trichocarpa (2). In addition, the sequences of WTK1 from G25, RPG1 30 , and MLOC_38442. 1  31  were included in the analysis to represent the tandem kinase structure, for which a function was assigned or proposed. Predicted protein sequences were aligned with MUSCLE (https://www. ebi. ac. uk/Tools/msa/muscle/). The evolutionary relationships between predicted proteins having a tandem kinase structure was inferred by the maximum likelihood method based on the JTT matrix-based model  65. The bootstrap consensus tree was calculated from 1000 replications  60. Branches were merged when a node was found in <50% of the bootstrap replicates (Supplementary Fig. 11a ). The analysis was performed with MEGA7 (https://www. megasoftware. net/). Graphical representation of amino acid conservation based on sequence logos was performed using WebLogo 3 (http://weblogo.
Acknowledgements This research was supported by the  European Community Framework projects BioExploit  ( CT-2005-513949 ) and  TriticeaeGenome  ( FP7-212019 ), the  COST Actions TritiGen  ( FA0604 ),  SUSTAIN  ( FA1208 ), and Impact of Nuclear Domains on Gene Expression and Plant Traits (INDEPTH,  CA16212 ), the  CSC-IT Center for Science (Finland) , the  Israel Science Foundation  ( 1719/08 ), and the  US-Israel Binational Agricultural Research and Development Fund  ( IS-4628-13 ,  US-4916-16 ). We thank the  International Wheat Genome Sequencing Consortium (IWGSC)  for providing access to unpublished wheat genome sequences and the  Global Yellow Rust Gene Bank  for providing Pst races. We also thank  P. Auvinen ,  P. C. Bailey ,  S. Barinova ,  H. Belcram ,  A. Dahan ,  Z. Gerechter-Amitai ,  A.
Discovering meaningful gene interactions is crucial for the identification of novel regulatory processes in cells. Building accurately the related graphs remains challenging due to the large number of possible solutions from available data. Nonetheless, enforcing a priori on the graph structure, such as modularity, may reduce network indeterminacy issues. BRANE Clust (Biologically-Related A priori Network Enhancement with Clustering) refines gene regulatory network (GRN) inference thanks to cluster information. It works as a post-processing tool for inference methods (i. e. CLR, GENIE3). In BRANE Clust, the clustering is based on the inversion of a system of linear equations involving a graph-Laplacian matrix promoting a modular structure. Our approach is validated on DREAM4 and DREAM5 datasets with objective measures, showing significant comparative improvements.
Graphical models (GM), such as (probabilistic) Boolean or Bayesian networks, are particularly well investigated  [14] ,  [15] ,  [16] ,  [17] ,  [18]. For instance, they estimate the covariance matrix, directly providing the GRN from its inverse  [19]. Integrating additional knowledge on miRNAs, authors in  [20]  determine both transcriptional and post-transcriptional regulations through dynamic Bayesian network. In the recent work of  [21] , authors developed the RegNetwork tool to provide GRNs by combining knwoledge from various databases such as TRED, KEGG or TFBS, for instance. GENIE3 uses random forests to solve a set of regression problems  [22]. Methods that provide a complete weighted network require a selection of meaningful relationships between genes (GRN), ideally relevant to biological interpretation.
Merging is also possible for TF genes that also exhibit a strong weight with a TF. This copes with the fact that not all TFs are known in real biological datasets. Unfortunately, the cost function in  (10)  is NP-hard. It can be harnessed with random walker algorithm  [30]. Cluster labels are obtained by relaxing simpler binary sub-problems. Binary label values relaxed in [0, 1] are interpreted as probabilities. Maximally probable outcomes finally yield optimal cluster labeling. We seek clusters attached to TFs. This label restriction to T is tackled by defining the set {s (1) ,. , s (T ) }, with T binary vectors of length T. To emulate the second term in  (10) , their components are set to s (1) ,. , y (T ) } be a set of T vectors. For all t ∈ T, y (t) ∈ [0, 1] G contains the probabilities for nodes to be assigned to cluster t.
The energy writes, in its simplest form, E(y) = 1 2 y Ly, which can also be expressed as i,j∈V 1 2 ω i,j (y i -y j ) 2. Equation  (11)  follows the same pattern with the Laplacian matrix of the graph in Figure  2(b). The minimization of  (11)  amounts to solving T -1 systems of linear equations admitting a unique solution. The maximum probability arising from sub-problem t, t ∈ T, defines each node label as illustrated in Figure  3(c ). The optimal cluster labeling y * = (y * i ) 1≤i≤G is given by ∀i ∈ V, y * i = argmax t∈T y (t) i. (12) An approximate solution to Problem (5) yields the GRN after few iterations of alternating optimization between (  8 ) and  (11) , less than 20 with our datasets.
Metrics and methods BRANE Clust is evaluated by comparing inferred networks to reference ones on several in-silico and real microarray datasets from DREAM4  [5]  and DREAM5  [6]  challenges. Evaluations are based on the computation of Precision (P) and Recall (R) measures. Their computation can be performed in different fashions. DREAM4 or DREAM5 estimate them based on weight ranking on complete graphs. This cannot be used in our context since BRANE Clust infers a binary-valued network. We therefore opted for the method proposed in  [10]. If #TP, #FP and #FN respectively denote the number of True Positive edges (in both inferred and reference networks), False Positive (inferred but not in reference network) and False Negative (not inferred but in reference network), P and R are defined by: P = #TP #TP + #FP , and R = #TP #TP + #FN.
GRN construction methods are then compared by constructing Precision-Recall (PR) curves. The latter are obtained by spanning the same range of thresholds for all the compared methods. Since we use normalized weights, λ is chosen to vary linearly between 0 and 1. Several methods -classical thresholding, BRANE Cut, BRANE Clust (hard and soft) -are finally ranked according to the highest Area Under the PR curve (AUPR). We compare them on two kinds of input weights obtained from state-of-the-art inference methods: CLR  [10]  and GENIE3  [22]. Both methods integrate knowledge about TFs. These two methods are frequently used as benchmarks  [11] ,  [13] ,  [23] ,  [28]. Notably, GENIE3 was the best performer in the used DREAM4 and DREAM5 datasets.