dataesr/scientific-paragraphs-categorization huggingface dataset:
All visualizations are based on interpolated data onto the MNI template. The different conditions of the source reconstructed data were compared based on cluster-based permutation statistics as described below. Cluster-Based permutation statistics. Statistical evaluation was done using non-parametric cluster-based permutation tests  49. First, we computed F-statistics to quantify the effect of context constraints (three levels: HC, MC, LC) for each sensor and time point. These F-statistics were used to define the clusters for the non-parametric statistical testing: clusters consisted of samples whose F-values were above threshold (threshold: F-value associated with a p-value of 5%) and were adjacent in space and time. Cluster-level statistics were computed by taking the sum of F-values within each cluster.
Mean sea level The rise and fall of the tides along the coast is a complex process that influences the establishment of a shoreline indicator. The tidal datums refer essentially to high tide or low tide. Different tidal data are used successfully as shoreline indicators. We can cite the mean sea level, the mean high water line, and the mean spring high water line, among others. [  74, 75]  Mean high water line  [76] [77] [78] [79] [80]  Mean spring high water line, mean high water spring tide  [81, 82]  Mean higher high water line  [83]  Mean low water line  [81, 84]  Mean low water spring tide mark  [85]  Lowest astronomical sea level  [86, 87]  Virtual reference lines.
The performance of artificial neural networks is well established. However, they require the extraction of a feature vector. The convolutional neural networks (CNN) make it possible to abstract this step of feature extraction, by taking images as input. Currently, CNNs are widely used for deep learning algorithms. Deep learning can achieve state of the-art accuracy for many applications by taking the advantages of the larger data set (Big Data) that we are now able to analyse. It is the object of intense research in image processing applications. However, according to our knowledge, a shoreline detection method using deep learning has not yet been proposed. When applied to images in which the different regions are clearly recognizable, the classification techniques mentioned above give good results.
Principal Component Analysis The PCA allows the use of smaller databases and reduction of noise  [120, 124]  Object-oriented classification It reduces salt-and-pepper effects commonly noted in pixel-based remote sensing image classification. [120, 128, 134]  Texture analysis-based methods Texture analysis groups together a set of techniques allowing quantifying the different grey-levels present in an image in terms of intensity and distribution in order to calculate a number of parameters characteristic of the texture to be studied. It is a very important task, which is useful for image segmentation and object detection. [135].
Discussion Shoreline detection approaches can be classified according to the resolution of the images that are used. It is obvious to remark that a high number of methods have been developed using Landsat data. This is due to the accessibility of these data, which are available from the USCG web site. In addition, Landsat images cover all the areas of the earth and allow diachronic studies over a long period. Another satellite whose data are also commonly used is SPOT, since it is one of the oldest satellites with a wide coverage. As for high-resolution satellites data, they are used in a fewer number of publications due to the high cost of these products. The state of the art also shows that almost all shoreline detection methods use commercial tools and do not rely on the development of automated process.
Whole-genome and whole-transcriptome sequencing and analysis The genomes and whole transcriptomes of Leishmania (Mundinia) isolates were sequenced as described previously  [35, 51, 52]  using the Illumina HiSeq and NovaSeq technologies with TruSeq adapters for the libraries preparation, respectively, at Macrogen Inc. (Seoul, South Korea). 43 and 47 million 100 nt paired-end raw reads on average were produced for genomes and transcriptomes, respectively (see statistics below). The genome completeness and annotation quality were assessed using BUSCO software  [53]. The raw reads were trimmed with Trimmomatic v. 0. 32  [54]  with the following settings: ILLUMINACLIP:TruSeq3-PE-2. fa:2:20:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:75, quality-checked with FASTQC program v. 0. 11. 5, and then assembled de novo with the Spades Genome assembler v.
3. 10. 1 with the default settings and automatic k-mer selection (k-mers of 21, 33 and 55 were used)  [55]. The Trinity assembler v. 2. 4. 0  [56]  was used to reconstruct the transcriptomes de novo with the minimal contig length of 150. Resulting genome assemblies were investigated for potential contamination using the Blob-Tools software implementing Bowtie2  [57]  for genome read mapping and Hisat2 for transcriptome read mapping  [58] , both with the default settings. Only those read pairs were used where at least one read was present in some contig with the transcriptome read coverage higher than 10 or in a contig with Leishmania, Leptomonas, or Trypanosoma term in first 100 best Diamond hits. Other read pairs were filtered out (Additional file 1: Figure  S1 , Additional file 2: Figure  S2 , Additional file 3: Figure  S3 , Additional file 4: Figure  S4 , Additional file 5: Figure  S5 , Additional file 6: Figure  S6 ).
Resulting assemblies (CovPlots, Additional file 7: Figure  S7 , Additional file 8: Figure  S8 , Additional file 9: Figure  S9 ) were further inspected and curated manually. Parameters of the genome assemblies were estimated using QUAST v. 4. 5  [59]. Raw reads were submitted to NCBI SRA under accession numbers SRX5006814, SRX5006815, and SRX5006816 (Bioproject: PRJNA505413) for L. (M. ) enriettii MCAV/BR/1945/LV90, L. (M. ) macropodum MMAC/AU/2004/AM-2004, and L. (M. ) martiniquensis MHOM/MQ/1992/MAR1, respectively. Genome annotation was performed with the Companion software  [60]  using transcriptome evidence, Leishmania major as a reference organism, and pseudochromosome contiguation with default settings. Transcriptome evidence was generated with the Cufflinks, mapping was performed with the Hisat2 with --dta-cufflinks parameter  [58].
Synteny analysis Synteny analysis was performed using SyMAP v. 4. 2  [61]  with the following settings: minimum size of sequence to load, 500 bp; minimum number of anchors required to define a synteny block, 7; synteny blocks were merged in case of overlaps, and only the larger block was kept if two synteny blocks overlapped on a chromosome. In case of Leishmania (Mundinia) genomes sequenced in this study, pseudochromosome level assembly built using Companion software with L. major Friedlin genome as a reference was used for the analysis instead of scaffolds in order to reduce computational time.
Genome coverage analysis and ploidy estimation Per-base read coverage was calculated for fifty longest scaffolds and all pseudochromosome level sequences using BEDTools v. 2. 26. 0 genomecov tool  [62]  on the read mappings generated with Bowtie2 as described above. Mean genome and scaffold/pseudochromosme coverage was calculated using a custom Python script. Ploidy was estimated based on relative coverage values: mean coverage for each of the fifty longest scaffolds and all psedochoromosome level sequences was divided by mean genome coverage and ploidy was inferred under the assumption that the majority of chromosomes are diploid. Coverage plots for 50 longest scaffolds were generated using weeSAM tool v. 1. 5 (http://bioinformat ics. cvr. ac. uk/blog/weesam-version-1-5/).
Inference of protein orthologous groups and phylogenomic analyses Analysis of protein orthologous groups was performed on a dataset containing 41 trypanosomatid species (including four representatives of the subgenus Mundinia, Additional file 16: Table  S1 ) and a eubodonid Bodo saltans as an outgroup, using OrthoFinder v. 1. 1. 8 with the default settings  [65]. Out of a total 551 OGs containing only one protein for each species, 92 were selected for the phylogenomic inference according to the following criteria: i) average percent identity within the group ≥60%; ii) maximum percentage of gaps per sequence in the alignment before trimming -40%; iii) maximum percentage of gaps per sequence in the alignment after trimming -10%. The amino acid sequences of each gene were aligned using Muscle v.
3. 8. 31 [66]. The average percent identity within each OG was calculated using the alistat script from the HMMER package v. 3. 1  [67]. The alignments were trimmed using tri-mAl v. 1. 4. rev22 with the "-strict" option  [68]. The resulting concatenated alignment contained 32,460 columns. The maximum likelihood tree was inferred in IQ-TREE v. 1. 6. 3 with the JTT + F + I + G4 model and 1000 bootstrap replicates  [69, 70]. For the construction of the Bayesian tree PhyloBayes-MPI 1. 7b was run for over 9000 iterations under the GTR-CAT model with four discrete gamma categories  [71]. Every second tree was sampled and first 25% of them were discarded as "burn-in". The final tree was visualized using FigTree v. 1. 4. 3 (http://tree. bio. ed. ac. uk/soft ware/figtree/). Gains/losses and expansions/contractions of protein families were analyzed using the COUNT software with Dollo's and Wagner's (gain penalty set to 3) parsimony algorithms, respectively  [72].
For gene ontology (GO) annotation of gene families gained/lost/expanded/contracted at certain nodes Blast2GO Basic software  [73]  was used with the maximum number of BLAST hits set to 10 and other settings left as default. Assignment of KEGG IDs to the proteins of interest was performed via BlastKOALA server with a target database of eukaryotes and prokaryotes at the family and genus levels, respectively  [74]. The analysis of OGs shared among Leishmania was performed using UpSetR package  [75].
Analysis of amastin repertoire Amastin sequences of L. major Friedlin, Trypanosoma brucei TREU927, and Trypanosoma cruzi CL Brener Esmeraldo were downloaded from the TriTrypDB release 41 and used as queries in BLAST search with an Evalue threshold of 10 -20 against a database of annotated proteins of Crithidia fasciculata, Endotrypanum monterogeii, Leishmania braziliensis MHOM/BR/75/M2904, Leishmania (Mundinia) spp. , Leptomonas pyrrhocoris H10, and Trypanosoma grayi ANR4. The resulting sequences were aligned using Muscle v. 3. 8. 31 with the default parameters  [66]. P-distances were calculated using MEGA 7 software  [76] , and the hits with p-distance to the α-amastin of T. brucei (Additional file 17: Table  S2 ) exceeding 0. 9 and query coverage < 50% were excluded from further analyses.
The resulting alignment was trimmed using TrimAl v. 1. 4. rev22 with the '-gappyout' option  [68]. Maximum-likelihood phylogenetic tree was inferred on the final dataset containing 384 sequences and 436 characters using IQ-TREE v. 1. 5. 3 with the VT + F + G4 model and 1000 bootstrap replicates  [69, 70].
Analyses of other proteins within OGs gained/lost at certain nodes For the identification of putative phosphatydylinositol glycan class Y proteins (PIG-Y), we have performed sensitive homology searches using the HMMER package v. 3. 1  [67]  and a model build using aligned sequences of trypanosomatid annotated as PIG-Y from the TriTrypDB release 41  [78]. Phylogenetic analysis of PIG-Y was performed similarly to amastins, with the JTT + I + G4 model as best-fitting and excluding sequences with p-distances to the reference set higher than 0. 8 (Additional file 20: Table  S5 ). The analysis of ferrochelatase sequences was performed similarly (Additional file 21: Table  S6 ), with the JTT + I + G4 phylogenetic model.
The absence of collapsed repeats and highly similar genes in the obtained assemblies is supported by the absence of regions of double coverage (i. e. , regions covered by two or more synteny blocks) as compared to publicly available genomes (Additional file 11: Figure  S11 , panel B). Annotated proteins of all representatives of the genus Leishmania within our dataset cluster into 8657 OGs. Most of these groups (83%, 7175 OGs) are shared among all four subgenera (Fig. 1 ). Mundinia spp. appear to possess the lowest number of the subgenus-specific OGs (~100), while the representatives of L. (Leishmania) have ~500 such groups.
The only exception is L. panamensis with a partial sequence of unknown function returning a short hit to the GPI-PLC. This hit resulted in erroneous inference of the putative GPI-PLC presence at the L. (Leishmania) node by the Dollo's parsimony algorithm. Putative GPI-PLC have been identified in all species within our dataset, except for dixenous Leishmaniinae, C. expoeki, and Phytomonas spp. In trypanosomatids, phosphatidylinositol N-acetyl-glucosaminyl-transferase, the enzyme catalyzing the first step of GPI biosynthesis, is composed of seven proteins: phosphatydyl-inositol glycan class A (PIG-A), PIG-C, PIG-H, PIG-Q, PIG-P, PIG-Y, and dolichyl-phosphate mannosyl-transferase polypeptide 2 (DPM2)  [85]. All these proteins were identified in L. (Mundinia), with the exception of DMP2 and PIG-Y being absent from the genome of L.
To do so, the Principal Component Analysis (PCA) is applied to achieve this goal. Once the feature extraction is done, we can apply the classification algorithm to classify the data. To do so, we have to train a classifier. A classifier is a function that maps an unclassified piece of data to a class by applying an induction algorithm, which builds a classifier from a given dataset  [9]. In order to train the classifier, we have to apply a proper classification algorithm  [10]  on the training set obtained from the previous step. One of such method is the k-Nearest Neighbor algorithm (kNN)  [10]. The advantages of applying the kNN classification algorithm is obvious: There is no existing model to classify the physical effects but a collection of correctly classified effect instances that is labelled by the domain experts.
Discussion and conclusion In this paper, we proposed a new approach to classify the physical effects based on machine learning. More specifically, it is based on the use of Wikipedia and kNN method. The proposed method can be applied to instantiate the physical effects database automatically which contributes the reusability of domain ontologies and the independence from domain experts. The preliminary result showed that we have successfully applied the proposed method to classify the physical effects, and achieved the mean accuracy of 0. 995 when k=5. This encouraging result enables further directions of research: To largely populate the physical effects into two classes by a bigger data set; To populate the relations of the physical effects ontology with more refined classifications. However, the proposed method relies on labelled data which is sometimes difficult to obtain, therefore, there is a need to find some ready to use dataset in order to instantiate the physical effects ontology with more individuals.
Reviewer's comments#7: As far as the results are concerned, the values of the statistical average are reported in table 1, however without even a practical example it is difficult to assess whether the quality of the result is really effective or useful for the TRIZ community. Author's response#7: We agree with the reviewer's point of view, however, it is a preliminary result that we present. We have not test the proposed approach on a large dataset yet, therefore, we could not yet provide its application in instantiating the ontology. It will be subjected to future works.
isr. umich. edu/), which prospectively surveys a representative sample of over 20,000 older Americans, will offer valuable multifaceted information to address key questions about the challenges, but also the opportunities, of ageing. Platforms such as the Gateway to Global Aging (https://g2aging. org/), the Global AD Association Interactive Network (http://www. gaain. org/) and the European Medical Information Framework (http://www. emif. eu/) offer the required infrastructure to pool population surveys and patient-level data to support innovative, large-scale research. These and other similar multinational efforts help to harmonise data across the individual studies and to foster collaboration and data sharing. These efforts will help to address critical questions in relation to clinical, genetic, omics and other data, which are also relevant for research on reserve and which can currently not be answered in any single cohort.
CR-related activation patterns should optimally moderate between these brain measures and cognition. Concerted efforts to harmonise research in the reserve field have to be increased, including regular expert meetings (e. g. Alzheimer's Association Reserve and Resilience Professional Interest Area), consensus conferences (e. g. Conference on Cognitive Reserve in the Dementias) and the publication of evidence-based guidelines  [14, 182]. Secondly, along with consensus on concept definitions and research approaches is the need for replication of research findings in 'test bed' datasets. For example, if a particular resting fMRI pattern is considered a candidate for a neural representation of CR, it optimally should moderate between brain and cognitive measures. It would be ideal to have standard, shared sets of data that could be used for these types of analyses.
Thus, as groups begin to collect extensive longitudinal datasets, establishing opportunities for sharing and for an application of results across datasets will be crucial. The continued improvement of IT platforms, such as the Global Alzheimer's Association Interactive Network and the European Medical Information Framework, will improve the availability of larger and more diverse sets of data. Thirdly, advances will be facilitated by collaboration and comparison of results by different laboratories. The substantial heterogeneity of human genetic and environmental factors dictates such collaboration, which can only be achieved after similar methods of data collection and analysis are agreed upon by the research community. Pooling of data into publicly accessible repositories will empower more scientists globally to work on the valuable information sources.
Databases such as the International Alzheimer's and Related Dementias Research Portfolio (https://iadrp. nia. nih. gov/about), which collates and categorises the portfolios of major funding organisations for areas of shared priorities as well as areas of opportunities to inform coordination and collective efforts that seek to advance dementia research, help to coordinate funding strategies and leverage resources in order to maximise the impact on public health and to avoid duplication of effort and reduce inefficiency. However, such efforts will need to be proceeded by the establishment of appropriate ethical, legal and social rules and agreements accepted across regional and cultural boundaries, as advocated by the World Dementia Council (https://worlddementiacouncil. org/our-work/our-work), for instance.
Acknowledgments The authors wish to thank  Ms Christin Schumann ,  Department of Psychiatry and Psychotherapy, University Hospital of Ludwig Maximilian University Munich  for her invaluable help with organising the first International Conference on Cognitive Reserve in the Dementias. The Alzheimer's Association Professional Interest Area population-based research working group includes the following collaborators:  Robert Perneczky ,  Department of Psychiatry and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, Munich, Germany ;  Catherine Robb  and  Chinedu Udeh-Momoh ,  School of Public Health, Imperial College London, London, UK ;  Richard N. Jones ,  Department of Neurology, Warren Alpert Medical School, Brown University, Providence, RI, USA ;  Allison R. Kaup ,  UCSF Department of Psychiatry, San Francisco VA Medical Center, San Francisco, CA, USA ;  Darren Lipnicki ,  School of Psychiatry, UNSW Medicine, Randwick, NSW, Australia ;  G.
The relatively consistent number of vacant rooms offers a large spatial database of choices in the area. The public space remains the constant in this equation: Yeovillites meet on the street, at work, in the park or in community yards. One informal trader told us that she changes accommodation once a year or more, but has occupied the same spot on the pavement for nearly a decade. We learned that she is the rule rather than the exception. This suggests that the urban compounding process is best triggered in the public open space, through investment in the design of places that people can share. The public edge could enhance these emerging urban compounds and improve their operations from the outside in, before engaging with the sensitive construct of private spaces and multiple actors and agents.
Acknowledgements Acknowledgements Scott J (1998) Seeing like a state: How certain schemes to improve the human condition have failed. New Haven, CT:  Yale University Press. Silverman M (2011) Architect, input at the  Yeoville Studio Housing Roundtable 9, 23 November, University of the Witwatersrand, Johannesburg. Silverman M & Zack  T (2007) Land use management in the city of Johannesburg. Case study: Hillbrow and  Berea. Unpublished  study funded by the  Ford Foundation  and  Urban Landmark , prepared for CUBES and  Planact. Silverman M & Zack  T (2011) Presentation on land management at the  Yeoville Studio Housing Roundtable, 23 November, University of the Witwatersrand, Johannesburg. Stroebel E (2011)  Johannesburg Housing Company , input at the  Yeoville Studio Housing Roundtable, 16 November, University of the Witwatersrand, Johannesburg.
Observations and data reduction To study the broadband spectral behavior of NGC 300 ULX1 we considered the 2016 joint NuSTAR  (21-12-2016, ObsID: 30202035002)  and XMM-Newton (17-12-2016, ObsIDs: 0791010101,0791010301) observation. We calculated the phase of each event based on the NS ephemeris (reference date: MJD=57738. 65661582, P=31. 718 s, Ṗ=55. 3×10 -8 s/s). We extracted background subtracted light curves and phase-resolved and phaseaveraged spectra from both telescopes. Both phaseresolved and phase-averaged spectra were analyzed simultaneously for the telescopes covering a spectral band between 0. 3-30 keV. For the analysis of the two XMM-Newton observations, we only considered the EPIC-pn detector  (Strüder et al. 2001)  which has the largest effective area of the three EPIC detectors, in the full 0.
3-10 keV bandpass, and had registered more than ∼80000 photons for each of the observations considered, providing sufficient statistics for continuum spectral fitting of both phase-resolved and phasedaveraged analysis. The data were handled using the XMM-Newton data analysis software SAS version 16. 1. 0. and the calibration files released 1 until December 13th, 2017. Following standard extraction guidelines we filtered out any potential high background flares by extracting 10<E<12 keV light curves with a 100 s bin size and placing appropriate threshold count-rates for high energyphotons. Using these, we removed any time intervals affected by high particle background. The pn detector was operated in imaging mode during both observations. For the spectral extraction we considered a circular extraction region with a radius 35 ′′ centered at the Chandra source coordinates  (Binder et al.