dataesr/scientific-paragraphs-categorization huggingface dataset:
These update rules do not guarantee nonnegativity, and therefore they are not sufficient. Thus, and to meet this constraint, the maximum, between each element of the updated matrix and a very small positive value ε (usually set to the default MATLAB epsilon value), is kept  [11]. Accordingly, the final gradient-based iterative update rules for the first proposed method read A 2 ← max A 2 -ϕ A 2 ∂J ∂ A 2 , ε (11) S ← max S -ϕ S ∂J ∂ S , ε (12) For the proposed Multi-Part-NMF method, multiplicative and iterative update rules are derived from the gradient-based update rules (  9 ) and  (10). These rules are obtained by formulating the learning rate matrices ϕ. as functions of the used matrices so as to obtain a multiplicative update and to preserve the nonnegativity constraint. Indeed, each gradient expression of J with respect to θ (which corresponds to one of the variable matrices A 2 or S) can be written as the difference of two nonnegative functions such that ∂J ∂θ = ∂J ∂θ + - ∂J ∂θ - (13) The function.
∂J ∂θ + is obtained by keeping the terms of (  7 ) or (  8 ) having a plus sign, whereas ∂J ∂θ is obtained by keeping the terms having a minus sign. The multiplicativity and nonnegativity constraints can be satisfied by initializing θ with a nonnegative value and choosing the value of the learning rate matrix ϕ θ according to ϕ θ = θ ∂J ∂θ + (14) where denotes the element-wise division. Thus, the following learning rate matrices are obtained ϕ A 2 = A 2 A 1 S S T + A 2 S S T (15) ϕ S = S A T 2 A 1 S + A T 1 A 2 S + A T 1 A 1 S + A T 2 A 2 S (16) Accordingly, and considering each update rule (  9 ) and (  10 ) takes the following form: θ ← θ ∂J ∂θ - ∂J ∂θ + (17) the final designed multiplicative and iterative update rules for the A 2 and S matrix variables are A 2 ← A 2 X S T A 1 S S T + A 2 S S T + (18) S ← S A T 1 X + A T 2 X A T 2 A 1 S + A T 1 A 2 S + A T 1 A 1 S + A T 2 A 2 S + (19) where is a very small and positive value, again usually set to the default MATLAB epsilon value, which is added to the denominator of each multiplicative update rule to prevent possible division by zero.
Realistic Synthetic Hyperspectral Data Four sets of urban environment spectra (photovoltaic panels, tiles, grass and trees: see Figure  1 , with spectral variability, are considered to generate, according to the linear mixing model (  1 ), realistic synthetic hyperspectral data. To validate the robustness of the tested methods to spectral variability in the experiments carried out and described hereafter, one randomly selected spectrum, from each of these four sets, is used to create each pixel of the considered realistic synthetic image. The above pure spectra contain 214 samples (after removing the atmospheric absorption spectral bands and based on those considered in the real-airborne hyperspectral image described in the next subsection), ranging from 400 to 2500 nm, and are obtained by ground measurements using a spectrometer.
Description of Tests The proposed Grd-Part-NMF (with fixed learning rate matrices set to 10 -3 for each element of these matrices) and Mult-Part-NMF approaches are tested on the considered data sets. The standard gradient-based (with the same fixed learning rate matrices) and multiplicative NMF methods (Grd-NMF and Multi-NMF)  [11, 24, 25]  are also considered in the experiments performed for comparison purposes. The maximum number of iterations considered in these NMF-unmixing-based methods is set to 1000. The threshold value of the convergence criterion (  20 ) is set to the default MATLAB epsilon value (i. e. , 10 -6 ) for all these used methods after running them with different threshold values (between 10 -3 and 10 -9 with a geometric step of 10 -1 , thus including the chosen value), and after noticing that the obtained performances are almost the same.
Epidemiological data All input parameters were derived from literature search. The Markov cohort consists of the German population 25 years and older with spasticity after stroke (Statistisches Bundesamt (Destatis) 2013a). Data for the annual probability of stroke was identified from a German patient registry  (Kolominsky-Rabas et al. 1998). Based on the incidence of stroke derived from  Kolominsky-Rabas et al. (1998)  we estimated the number of strokes in the German population. The prevalence of spasticity after stroke was estimated for a mean value of 27. 5%  (Lundström et al. 2008; Watkins et al. 2002). It was assumed that at least half of the patients with spasticity after stroke have lower limb spasticity and half of the patients have ULS (Urban Additionally it was assumed that 38% of these patients with ULS are suitable for treatment with botulinum toxin  (Ward et al.
Cost data Costs were calculated from the perspective of the German statutory health insurance system. Social costs such as productivity loss are not considered. In the model, costs for the cohort are evaluated for a 5-year horizon. The cost analysis includes outpatient and inpatient costs. Outpatient costs such as physician contacts and drug prescriptions are obtained from  Bock et al. (2015)  and from the LAUER-TAXE® (2017), a German register for pharmaceutical drugs. For each drug prescription the biggest package size was assumed. Inpatient costs were calculated by the number of days of hospitalization multiplied by the costs per day of hospitalization from  Bock et al. (2015)  (Table  4 ). The costs for medical therapy per year and strategy for the base case are shown in Table  5. All cost data are expressed in Euro, using 2013 as reference year.
Data processing The timbre was characterized by audio descriptors applied on a given musical note: C3-sharp (277. 2 Hz). C3-sharps were manually extracted using Audacity (https://www. audacityteam. org/). A 6 th order high pass Butterworth filter was applied with a cut off frequency of 40 Hz to remove the background noise. Then, the Matlab Timbre Toolbox was used  (Peeters et al. 2011). As recommended by  Peeters et al. (2011)  we used the less redundant (0. 8 median absolute rank correlation threshold), most musically significant Timbre Toolbox audio descriptors: RMS energy Ã , attack Ã and decay duration Ã , energy modulation (frequency Ã and amplitude Ã ), spectral centroid, spectro-temporal variation, spectral flatness, spectral crest, fundamental frequency, noisiness Ã , inharmonicity, tristimuli, odd-to-even harmonic energy ratio ( Ã indicates that only these descriptors were applied to the filtered signal).
The gradient of the cost function J with respect to the unknown parameters P was calculated analytically. For the gradient descent method, an initial guess was required. We arbitrarily chose A = 10, the default values τ out,i = 6 ms and τ close,i = 150 ms for all i, and τ i constant τ i = τ 0 ∈ R. Since the initialization was the same for all the nodes, the initial torso potentials were zero. The optimization ended when the cost function J and its gradient remained constant. The code was in Matlab and not parallel. Computational time was quite long and similar for all the cases, namely about one day. A more flexible stopping criterion and parallelism would reduce computational time.
This approach leads to slightly different extracellular potentials within a few hundred micrometers from the surface only  (Potse et al. , 2006). All simulations were performed with a recent version of the Propag-5 software (Krause et al. , 2012) on a BullX cluster machine. We had access to the activation times on the epicardium and the endocardium (named reference ATs in the following). Repolarization times were computed from extra-cellular potentials as the time with highest positive slope during the repolarization phase.
We solved the inverse problem with a constant factor A over the whole heart. However, this factor (proportional to the amplitude of the AP) may not be constant, e. g. , in the case of ischemia. We attempted to consider a distributed factor, more relevant from a physiological point of view. In that case the method was not converging, or converged to both positive and negative amplitudes. So far we did not add noise to the testing data. Even if the models to create the data and to solve the inverse problem are different, it would be helpful to assess the robustness of the method. Validation data were created from a volumetric heart mesh with a much higher density than Mesh1 and Mesh2. The reference  values (AT, RT) were the values on the mesh nodes. In contrast, the inverse problem on a surface leads to values that contain information averaged over a considerable volume.
Quantitative real-time PCR (qPCR) was carried out in triplicate in a LightCycler 96 instrument (Roche, Mannheim, Germany) using 100 ng cDNA, 0. 5 mM each primer (Table  S1 ) and qPCR BIO SyGreen Mix Lo-ROX (PCR Biosystem, London, UK) in 10 ml of total volume. The act1 gene (ACLA_095800) was used as reference gene  (Bohle et al. , 2007; Verheecke et al. , 2015). Threshold cycle (C t ) determination was performed using LightCycler 96 software (Roche, Mannheim, Germany). Expression levels were calculated according to the 2 -DDCt method  (Schmittgen & Livak, 2008). Results are presented as the mean of three independent data. Statistical analyses were performed in GraphPad Prism 5. 0 (GraphPad Software, San Diego, CA, USA).
In silico analysis for transcription factor binding sites The 5′ untranslated region (UTR) of eight genes of fungal CSaβ defensins were analysed to identify putative transcription factor binding sites (TFBS) (Table  S2 ). 1,500 bp upstream of the translation start site were analysed by the Saccharomyces cerevisiae promoter database of the Cold Springs Harbor Laboratory (http://rulai. cshl. edu/SCPD/) (Zhu & Zhang, 1999)  and the TRANSFAC 6. 0 database using the Patch 1. 0 program (http://gene-regulation. com/cgi-bin/pub/programs/patch/bin/patch. cgi) (Matys et al. , 2006). The act1 gene (ACLA_095800) of A. clavatus was included as negative control (GenBank accession number: NW_001517095. 1; Region: 176194-177694).
Identification of putative regulatory elements in the 5′ untranslated region The 5′ untranslated region of aclasin was analysed to identify putative regulatory elements. Several putative TFBSs were found, including TATA box, STRE, Yap1 response element   ACLA_044200 ), which encodes a mycelial bifunctional catalase-peroxidase, was used as a positive control for a gene upregulated by oxidative stress. Relative aclasin mRNA expression was determined by qPCR according to the 2 -DDCt method. The act1 gen (ACLA_095800) was used as reference gene and the untreated condition as control. Controls have a value of 1. Data represent the mean (± standard deviations) of three biological replicates. Relative expressions were analysed by ANOVA followed by Bonferroni post-tests in comparison to the control for heat shock and oxidative stress, and student t-test for the osmotic assay ( Ã , P < 0.
Figure1Relative aclasin mRNA (ACLA_006820) expression during mycelial growth in A. clavatus DSM 3410. A. clavatus (10 5 conidia/ml) was incubated in MEB at 28 ºC and 150 rpm. Biomass represents the mycelial dry weight. For RNA analysis, mycelia samples were collected at 24, 36, 48, and 96 h of incubation. Relative aclasin mRNA expression was determined by qPCR according to the 2 -DDCt method. The act1 gene (ACLA_095800) was used as housekeeping gene. Normalised gene expression values for each time were compared with normalised gene expression at 24 h of incubation. Data are means and standard deviations from three independent experiments. Aclasin relative expressions were analysed by ANOVA followed by Bonferroni post-tests (P > 0. 05). Full-size  DOI: 10. 7717/peerj. 6290/fig-1.
2, respectively. Note that Cutadapt was used only for 5= adaptor clipping. After trimming, 26,533,605 PE reads (255ϫ) and 1,325,112 MP reads (13ϫ) were used for de novo genome assembly using SOAPdenovo2 v2. 04 (8), with a k-mer value of 77, as estimated with kmergenie version 1. 67 (9). Gap closure was performed using GapCloser v1. 12 (8). The final assembly comprised 17 scaffolds larger than 5 kb (N 50 of 3. 9 Mb, obtained with three scaffolds) for a cumulative length of 20,519,037 bp. A single scaffold of 48,435 bp corresponded to mitochondrial DNA. The remaining 16 scaffolds were suitable for automatic annotation using Rapid Annotation Transfer Tool (RATT) (10) with the Y. lipolytica E150 genome sequence as a reference (genome sequence available at http://gryc. inra. fr). Manual curation was performed with transcriptome sequencing (RNA-Seq) reads (BioProject accession number PRJEB29941) mapped to the assembly with TopHat2 (11).
Data availability. The draft genome sequence of Yarrowia lipolytica strain H222 has been deposited in DDBJ/ENA/GenBank under the accession number GCA_900537225. The version described in this paper is the first version. The accession number for the project is PRJEB28424, and for the reads, ERR2767096 (PE), ERR2767094 (MP), and ERR2767095 (MP). The accession numbers of the 17 scaffolds are UTQH01000001 to UTQH01000017. Genome sequences and annotations are also available at the GRYC server (http://gryc. inra. fr).
Method The results presented herein are based on the analysis of the 2012-13 Pakistan Demographic and Health Survey  [8]. PDHS 2012-13 is the third nationwide survey that was organized under the supervision of the Global Demographic and Health Survey Program. The significance of the survey is that it covers the overall population of the country (see Table  1 ). For the results analysis, the statistical software STATA was used. Used as continuous variable DM (household decision-making about visits to family or relatives) 1 for only woman, 2 for both woman and man, 3 for only man.
Ethical Considerations Ethical approval was obtained from the Economics Department of Islamia University Bahawalpur, Punjab, Pakistan (Ref # 69-2017/PREC). To measure child malnutrition, the composite index of anthropometric failure (CIAF) was taken as a dependent variable and generated by the combination of three anthropometric indices (variables) in the PDHS data set, such as stunting, underweight, and wasting. These indices are expressed according to standard deviation units (Z-score < -2 standard deviation) from the multicenter reference growth study  [8]  (PDHS report 2012-13); these three measures may not provide an inclusive estimation individually since they can overlap-that is, a child who is stunted could be underweight or wasted or both  [14]. However, CIAF is an aggregate measure of the prevalence of malnutrition and incorporates all undernourished children.
The report indicates the economic pressure that forces families to live together. The education of the mother is the most important socioeconomic factor that can increase the chances of empowerment at home. In PDHS, the mother's education is categorized into four parts that help to provide a complete picture of its effects on child nutrition and the empowerment of women. Mothers being better educated helps to empower women and reduces the probability of malnutrition-particularly wasting-among children  [17]. Women's employment is used to measure the development of positive health outcomes. In the PDHS data set, questions related to women's employment status help to ensure complete coverage in the formal and informal sectors. The variable used in this model is an indicator for women's empowerment that affects health outcomes of both the children and the mother.
Anti O-GlcNAc (RL2) or anti-OGT antibodies were added during 30 min at 4 • C in darkness. Cells were washed one time and incubated with FITC-coupled anti-mouse or FITCcoupled anti-rabbit secondary antibodies. Cell were washed one time with PBS. Finally, samples were analyzed with a FACScalibur cytometer. All data were analyzed by FlowJo X software.
ACKNOWLEDGMENTS We would like to thank the  LABNALCIT-UNAM  ( CONACYT ) technical support in the acquisition and analysis of flow cytometry samples, especially to  MS Erick Christian Ivan Espindola Arriaga. We want also thank to  Dr. Miguel Tapia Rodríguez  from the Microscopy unit of IIB-UNAM and Dr. Stephan Hardivillé  from the  University of Lille  for the kindly donation of Ac5SGlcNAc and  Dr. Chan Lagadec  from th  Cell Plasticity and Cancer team  ( INSERM U808 ) for helping in FACS access. GF-G is a Ph. D. student in the  Programa de Doctorado en Ciencias Bioquímicas ,  Universidad Nacional Autónoma de México (UNAM) , and was granted with a Fellowship by  DGAPA, UNAM , Mexico and by  CONACYT.
Tyr719*) (unique assay Id: dHsaMDS423261257, supplemental methods). The HEX probe was used to detect the control sequence (Gene Id: 23394). The annealing temperature for various sets of primers and probes was established by previous gradient runs. After droplet generation, PCR was performed in a C1000 as follows, 95 °C 10 min, 40 cycles of 94 °C 30 s, 54 °C 1 min, 98 °C 10 min. The droplets were analyzed with a QX200 instrument, and the data were processed by the Quantasoft software, with the rare event detection (RED) mode according to the digital MIQE guidelines  [32].
Variant calling Trimmed reads were mapped to the human genome (Ensembl's GRCh38) using STAR v2. 4. 2a  [34]  with default parameters and twopassMode set to basic. Reads were then deduplicated using Picard, except for the olfactory bulb data, where it was not performed due to the short read length (60 bp). Mapped reads were further processed with GATK's v. 3. 7  [35]. SplitNCigarReads. Next, variants were called using GATK's HaplotypeCaller with ploidy set to 10 except for chromosomes 10 and 15 in GSE95587, the fusiform gyrus tissue dataset, where it was set to 8 due to technical limitations that did not allow to use ploidy10. Variants were filtered with the following values for SNPs and Indels respectively: QD < 2. 0, FS > 30. 0, MQ < 40. 0, MQRankSum < -12. 5, ReadPosRankSum < -8. 0 and QD < 2.
Gene expression omnibus (GEO) datamining Dataset GSE95587  [38]  was identified as containing a large cohort (N = 117) with a high number of reads covering each position  (32)  including AD postmortem brain tissues from fusiform gyri section I (supplemental methods), this was accompanied by a larger dataset GSE125583 (N = 289) with a lower coverage  (20) , fusiform gyri tissue section II (Fig. 1 ). Two additional libraries included GSE67333hippocampal RNA-seq (late onset AD)  [39]  and GSE53697-DLPFC (advanced AD)  [40] ; however, these two libraries should be taken cautiously due to small sample sizes (Fig. 1 ). For different cell type mutation analysis, the GSE125050 superior frontal gyrus dataset was used including endothelial cells (CD31+), astrocytes (GFAP+) myeloid cells (CD11b+), and neurons (NeuN+) (total sample size 113).
Fluorescence recovery after photobleaching (FRAP) Differentiated N1E-115 cells were co-transfected with mCherry-Tau and GFP conjugated to full-length ADNP or mutated ADNP, and imaged 48 h after transfection. An ROI (region of interest) for photobleaching was drawn in the proximal cell branches. mCherry-Tau was bleached with a 587 nm argon laser and fluorescence recovery was at 610-650 nm. Immediately after bleaching, 80 images were collected every 0. 74 s. Fluorescence signals were quantified with ImageJ (NIH), obtained data were normalized with easyFRAP  [41] , and FRAP recovery curves were fitted by a two-phase exponential association function using GraphPad Prism 6 (GraphPad software, Inc. , La Jolla, CA). Samples with R 2 < 0. 9 were excluded.
Statistics Analyses (SPSS 23, Chicago, IL) for postmortem brain tissues employed either ANOVA or Kruskal-Wallis tests (>two groups) followed by nonparametric Mann-Whitney U test. Multiple testing corrections employed Bonferroni. Correlations employed Spearman. Live-cell imaging and FRAP results were analyzed by SigmaPlot 11 (Systat Software, Inc. , San Jose, CA) with two-way ANOVA, followed by Fisher LSD. Additional information can be found in the Supplemental file.
Public dataset validation: somatic mutations in multiple genes with AD specificity and enhancement To extend the single gene findings, postmortem olfactory bulb cDNA samples (19 AD, 20 controls) all presenting tauopathy and ~80% exhibiting amyloid pathology (Table  S1 ) were subjected to RNA-Seq. We then discovered an ADNP c. 2187_2188insA, p. Arg730Thrfs*4 mutation (same position as p. Arg730*-c. 2188C>T). Results were validated by examination of reads aligned to the area of c. 2187_2188insA using Integrative Genomics Viewer (IGV) (Fig. S4 ). To extend and validate the findings for ADNP in the olfactory bulbs (Table  S3 ) and the hippocampus to other brain areas we have resorted to datamining of hippocampi, DLPFC and fusiform gyri public RNA-seq databases (Tables S4-S6a,b). As seen in Fig.
d Mutation analysis of RNA-seq variant calling of four postmortem brain areas: olfactory bulb (our own primary data), Hippocampus (GSE67333), Dorsolateral prefrontal cortex (DLPFC) (GSE53697) and Fusiform gyrus tissue section I (GSE95587) and Fusiform gyrus tissue section II (GSE GSE125583). The pie charts represent distribution of mutations into three different groups: controls (Cont. ) only, AD only and both in AD + controls (AD + Cont. ). The Box plots represent (e) mutation numbers per subject and (f) mutation frequencies per subject for each group in the four brain areas. Statistical analyses (Table  S6b ) were performed by the nonparametric Kruskal-Wallis test for the hippocampus and DLPFC and ANOVA for the olfactory bulb and the fusiform gyrus with post hoc Bonferroni for multiple comparisons.
1d ). When the three groups of mutations (control exclusive, shared and AD unique mutations) were compared the number (Fig. 1e ) and frequencies (Fig. 1f ) of mutations per subject were significantly higher in the shared group of mutations. Further comparisons of control subjects carrying AD shared mutations to AD subjects carrying shared and unique AD mutations showed highly significant increases in number (Fig. 1e ) and frequencies (Fig. 1f ) per AD vs. control subjects. Similar results were obtained by RNA-seq datamining of public datasets (hippocampus, DLPFC, and fusiform gyrus I (142 subjects) + an additional cohort of fusiform gyrus II with 89 shared subjects with fusiform gyrus I plus 200 unique subjects, Fig. 1g ). (Only the comparison concentrating on AD-mutation number per subject in the fusiform gyrus I were similar in controls and AD subjects).
S7 ). Interestingly, several DLPFC subjects also showed mutations in genes associated with familial AD (amyloid precursor protein-APP and presenilin 1-PSEN1 (Table  S5 , Fig. S7 ). Overlapping of all identified mutated genes in the four brain areas with cytoskeletal genes (Tables S3-S6a,b S8a) revealed about 25-70% uniquely mutated and four shared genes (Fig. 3a ). Overlapping all mutated genes with cytoskeletal genes, while not showing a proportional increase in AD vs. control (Table  S8b ) did show an ADspecific MT-based processes gene group and a control specific gene group associated with cell projection organization (Fig. S7 ). Further overlap with autism spectrum disorder (ASD, autism) database (https://gene. sfari. org/database/human-gene/) and ID database (http://www. ccgenomics. cn/IDGenetics/gene.
php?dataset=IDGD_gene_detail) identified unique and shared mutated gene mutations (Tables  S9-S11 ), with ADNP singled out as a gene associated with cytoskeleton/autism/ID/AD (Fig. 3a , b, Table  S9 ). To better understand the cellular distribution of the mutations, an additional database was mined GSE125050  [42]  (Fig. 3c-e , Table  S12  a, b). The results per cell mirrored the tissue findings (Fig. 1e, f ) with neurons showing the least mutations. Furthermore, mutation frequencies and mutation numbers/subject were significantly correlated only in the AD-derived cells (Fig. 3e ). IGV analysis of the large GSE95587 (fusiform gyrus I) cohort with high sequencing coverage (Fig. 1g , Fig. S8 ) revealed significantly more c. 2187_2188insA ADNP mutation carriers (~50%) among the AD subjects than controls (15%, Fig.