dataesr/scientific-paragraphs-categorization huggingface dataset:
These networks have drawn scientists' attention not only in vision sciences, but also in other fields of science (see  [55] ), as a powerful solution for many complex problems. DCNNs are among the most powerful computing models inspired by computations performed in neural circuits. To our interest, recent studies also confirmed the abilities of DCNNs in object recognition problems (e. g. [15] ,  [27] , and  [61] ). Besides, several studies have tried to compare the responses of DC- NNs and primate visual cortex in different object recognition tasks. Khaligh-Razavi and Kriegeskorte  [20]  compared the representational geometry of neuronal responses in human (fMRI data; see  [48] ) and monkey IT cortex (cell recording; see  [49] ) with several computational models, including one DCNN, on a 96-image dataset.
They showed that supervised DCNNs can explain IT representation. However, firstly, their image database only contained frontal views of objects with no viewpoint variation. Secondly, the number and variety of images were very low (only 96 images), compared to the wide variety of complex images in natural environment. Finally, images had a uniform gray background, which is very different from natural vision. To overcome such issues, Cadieu et. al. [27]  used a large image database, consisting of different categories, backgrounds, transformations, and compared the categorization accuracy and representational geometry of three DCNNs and neural responses in IT and V4 of monkey. They showed that DCNNs closely resemble the responses of IT neurons either in accuracy or geometry  [27, 47]. One issue in their study is the long stimulus presentation time (100 ms), which might be too long to only account for feedforward processing.
Using the same image database, we also evaluated eight state-of-the-art DCNNs  [15, 16, 17, 18, 31] , largely inspired by feed-forward processing of visual cortex. Results indicated that these DCNNs can mimic human accuracy (see Fig. 2  to Fig. 5 ). However, the HMAX model, as one of the early successful models, showed very poor performance in almost all experiments. We also showed in our previous study that such shallow feed-forward models fails to achieve human-level accuracy in invariant object categorization  [19]. We further performed layer-specific analysis to investigate how accuracy and representational geometry evolve across consecutive layers in DCNNs. Results illustrated that accuracies tend to increase as images are processed through the layers; however, some layers achieved very similar accuracies.
If some layers do not considerably contribute to the final accuracy, at least in our task, one is tempted to remove it, to reduce the computational load of the DCNN, which is typically very high. For example it has been shown that eliminating one of the middle layers leads to just 2% accuracy drop in Krizhevsky model on the Imagenet database  [15]. More research is needed to systematically evaluate the role of different layers by removing each layer and evaluating the resulting accuracy. However, this should be done using different image databases since these DCNNs were optimized for Imagenet database. Therefore, the layer-specific effect might be database dependent. The layer-specific analysis is interesting as it shows that not only the accuracy, but also the representational geometry evolves through layers.
Future directions Our image database has several advantages for studying invariant object recognition. Firstly, it contains a large number of object images, changing across different levels of variations of position, scale, and in-depth and in-plane rotations, and background. Secondly, we had a precise control over the amount of variations that let us generate images with different degrees of complexity/difficulty; Therefore, enabling us to scrutinize the behavior of humans and computational models, while the complexity of object variations gradually increases. Thirdly, similar to several studies  [47, 27, 74, 75] , by eliminating dependencies between objects and backgrounds, we were able to study invariance, independently of contextual effects. However, there are several effective parameters in invariant object recognition for both humans and models that should be further investigated.
have studied the impacts of the network depth by implementing deep convolutional networks with 11, 13, 16, and 19 layers. To this end, they used very small (3 × 3) convolution filters in all layers, and steadily increased the depth of the network by adding more convolutional layers. Their results indicate that the recognition accuracy increases by adding more layers and the 19-layer model significantly outperformed other DCNNs. They have shown that their 19-layered model, trained on the Imagenet database, achieved high performances on other datasets without any fine-tuning. Here we used the 19-layered model available at http://www. robots. ox. ac. uk/ ~vgg/ research/very_deep/. The structural details of this model are provided in Table.
Research Infrastructures Debate The main change enabled by digital humanities and, in particular, by crowdsourcing is the collaborative dimension of research projects. Infrastructures studies focus on this dimension in order to understand how a global science can be built in a networked environment. Infrastructure is a mix of social and technical conditions that allow large-scale collaboration and according to  Bowker et al. (2010) : Key to any new infrastructure is its ability to permit the distribution of action over space and time. The model of such infrastructures comes from genetics or climate sciences requiring data gathering in a single archive. The result is a global database such as Worldwide Protein Data Bank (http://www. wwpdb. org/about/agreement) and the main challenge is to build standards and create convergence at a global level even if this objective is not easy to reach  (Edwards et al.
, 2011). Digital humanities also require the need for infrastructures, some examples are: Cultures of knowledge (http://www. culturesofknowledge. org/), ClarosNet (http://explore. clarosnet. org/XDB/ASP/clarosHome/), European Holocaust Research Infrastructure (EHRI, http://www. ehri-project. eu). Thus EHRI provides online access to information about dispersed sources relating to the Holocaust through its Online Portal Online Portal, and tools and methods that enable researchers and archivists to collaboratively work with such sources (https://www. ehri-project. eu/about-ehri, accessed 14. 01. 2017) However, the model is not the same in humanities. As van Peursen et al. (  2010 ) stated: This is a fundamental difference between databases as they are used in the humanities and those that are used in the natural sciences.
SORL1 sequencing Sequencing of SORL1 exons 2-48, and at least 15 nt of each exon-intron flanking region, was performed by target enrichment using MASTR technology (Multiplicom, Niel, Belgium). PCR primers flanking each target region were designed using mPCR software (Multiplicom, Niel, Belgium). Target region size for amplification was set at 500 nt. In total, all target regions were covered by 46 amplicons in nine multiplex PCR reactions. Subsequent indexing and sequencing was performed with extension of target-specific primer sequences with universal tag sequences (5′-TCGTCGGCAGCGTCAGATGTG-TATAAGAGACAG-Fwd and 5′ GTCTCGTGGGCTCG-GAGATGTGTATAAGAGACAG-Rev). Optimal annealing temperature and relative amounts of PCR primers for all targets were established for uniform amplification of each target in the multiplex reaction.
Trimming of Illumina adapters from raw sequencing Fastq files was performed by Fastq-mcf. Read alignment and mapping was done against whole reference genome hg19 using the Burrows-Wheeler Aligner  [26]. Variant calling and annotation was performed using GATK version 2. 2  [28]  and annotated using the Genomecomb software pipeline  [40]. Variants with a read depth below 20 reads or with an imbalanced reference/variant allele read depth exceeding 3:1 were considered false calls. All remaining variants with predicted effect on protein sequence were included in subsequent manual read inspection using the Integrative Genomics Viewer software  [41]. In total, 92 % of the SORL1 target sequence was sequenced at >20× read depth for all included individuals. Due to high GC content (74 %), SORL1 exon 1 was sequenced using simplex PCR amplification followed by Sanger sequencing using the BigDye termination cycle sequencing kit v3.
1 on the ABI 3730 DNA Analyzer. Sequences were analyzed using Seqman (DNAstar, WI, USA) and NovoSNP software  [51]. Rare variant validation was performed on genomic DNA by Sanger sequencing, as was segregation analysis of variant p. Tyr1816Cys. Variant position on genomic level was based on Genbank accession number NC_000011. 9, transcript position was based on NM_003105. 5, and protein-level position on NP_003096. 1.
In silico prediction Putative pathogenic effects of coding SORL1 variants were predicted using Polymorphism Phenotyping software version 2 (PolyPhen2, http://genetics. bwh. harvard. edu/pph2/), Sorting Intolerant from Tolerant (SIFT, http://sift. jcvi. org), SIFTindel for frameshift variants (http://sift-dna. org/), and Mutation-Taster (http://mutationtaster. org/) databases. Previous identification of variants was investigated by comparison of identified variants against public databases, including the Database of Single Nucleotide Polymorphisms 141 (http://www. ncbi. nlm. nih. gov/SNP/), the Exome Variant Server (http://evs. gs. washington. edu/EVS/), the International HapMap Project (http://hapmancbi. nlm. nih. gov/), the 1000 Genomes Project (http://www. 1000genomes. org/), and the Exome Aggregation Consortium database (http://exac.
RNA sequencing RNA sequencing data were generated for the p. Gly447Argfs*22 frameshift variant carrier. Total RNA was isolated from Epstein-Barr virus immortalized lymphoblast cells derived from whole blood lymphocytes. RNA isolation was performed using 1. 0 × 10 7 lymphoblast cells with the RNeasy mini kit (Qiagen Inc. , Valencia, CA, USA) according to manufacturer's protocol. Depletion of genomic DNA from the RNA sample was performed by turboDNase treatment (Life Technologies, Carlsbad, CA, USA). RNA quality control to determine RNA concentration and RIN value was performed using the Agilent Technologies 2100 Bioanalyzer. RIN value was measured at 9. 3 in a concentration of 86 ng/µl total RNA. The sequencing library was constructed using Truseq stranded mRNA Library Prep Reagent Set (Illumina, San Diego, CA, USA).
Library preparation was performed using 1 mg total RNA and included poly-A selected RNA extraction, RNA fragmentation, and randomhexamer-primed reverse transcription. Sequencing of prepared libraries was performed using an Illumina HiSeq 2000 sequencer, generating 126,949,218 101-nucleotide pairedend sequence reads. Data analysis was performed using an in-house developed processing pipeline. Removal of read adapters and trimming of read ends was performed using Trimmomatic  [3]. Trimmed reads were mapped against the UCSC human reference genome hg19  [43]  using the Bowtie short read aligner integrated in Tophat2  [21]. Post-alignment QC and filtering of mapped-reads was performed with RSeqQC  [49]. Variant calling was performed by employing GATK  [28] , VARSCAN  [23]  and VEP  [31]  software.
Rare variant burden analysis was performed by collapsing alleles of all rare coding variants across the full SORL1 coding sequence or separately for each functional protein domain using an optimized sequence kernel association test (SKAT-O test), adjusted for sample size <2000. Rare variants association tests were performed using the R package SeqMeta  [9]. SKAT-O meta-analysis was performed using standard beta-weights, and correction for gender and APOE ε4 carrier status of included individuals. Presented SKAT-O meta p values represent minimal p values over ρ as proposed by Lee et al. [25]. Correction for multiple testing was performed using Šidák correction. Functional protein domains were determined according to Pottier et al. [37]. Differences in relative lymphoblast SORL1 expression were calculated using an unpaired nonparametric (Mann-Whitney) test.
03) (Fig. 2b ). In addition to these 8 PTC mutations, we observed 84 rare missense variants in the patient/control cohort. Of the total identified rare missense variants and PTC mutations, 44 (48 %) were only observed in the patient cohort (supplementary table 2). In addition, 19 rare missense variants (21 %) were present in both patient and controls, and 29 variants (32 %) were only observed in controls (supplementary tables 3, 4). One patient carried a frameshift (p. Cys1103Valfs*4) and a missense variant (p. Asp2065Val); three patients and two controls carried double missense variants. Of the rare variants observed in this study, 30  (33 %)  were not previously reported in any of the screened databases, the majority of which [22 (73 %)] were only observed in the patient cohort, while seven (23 %) were only found in controls, and one novel variant was detected in patients as well as controls (supplementary tables 2, 3, and 4).
Conflict of interest The authors declare no conflict of interest with respect to the study and data presented in this paper. Funding The authors thank the personnel of the Genomic Service Facility and of the Bioinformatics Unit of the VIB Department of Molecular Genetics for their support of the genetic analyses. The data generation for this article was in part funded by the Belgium Science Policy Office Interuniversity Attraction Poles program (http://www. belspo. be/), the Alzheimer Research Foundation (S#13023) (http:// alzh. org/), the Flemish Government Initiated Methusalem Excellence Program to C. V. B. , the Research Foundation Flanders (G043211N) (FWO, http://www. fwo. be/), the University of Antwerp Research Fund, the Flemish Government initiated Flanders Impulse Program on Networks for Dementia Research, the MetLife Foundation Research.
Award to C. V. B. , the  EU   FP7  project  AgedBrainSYSBIO  under Grant Agreement No. 305299  (http://ec. europa. eu/research/fp7),and the  King Baudouin Foundation   AB Award  to K. B. The Barcelona IDIBAPS site (RS, AL, EG and OG) was partially financed by a Grant to AL ( PI14/00282 ,  ISCIII ,  Cofinancia Fondo Europeo de Desarrollo Regional (FEDER) ,  Unión Europea , Otra manera de hacer Europa). They are indebted to the  Neurological Tissue Bank of the IDIBAPS Biobanc in Barcelona, Spain , for sample and data procurement and to brain donors and relatives for generous donation for research. The Barcelona Sant Pau site (JC, AL, and JF) was partially supported by Grants from  Instituto de Salud Carlos III  ( PI12/01311 ). The Barcelona ACE site (AR) thanks the controls who participated in this project.
We are indebted to  Trinitat Port-Carbó  and her family who are supporting Fundació ACE research programs. AR is supported by Grant  PI13/02434  ( Acción Estratégica en Salud Instituto de Salud Carlos III (ISCIII) Ministerio de Economía y Competitividad, Spain)  and  Obra Social "La Caixa " (20131209) (Barcelona, Spain). The Prague site (RM and ZR) was partly supported by Grant  IGA   NT12094-5  from  Grant Agency of Ministry of Health and Charles University  Project  PRVOUK P26/1/4. The Lisbon site (AM and MM) was supported by the  Fundação para a Ciência e a Tecnologia  ( SFRH/BPD/29354/2006  to MM) and thank  Gabriel Miltenberger-Miltényi ,  Mafalda Matos  and  Catarina Ferreira  for helpful comments and technical support and  Ana Verdelho  for referring patients to the present study. The Brescia IRCCS Fatebenefratelli site was funded by the  Ricerca Corrente, Italy Ministry of Health.
Methods Genome sequencing, assembly and annotation. Plants were grown and DNA was extracted following the methods described at Supplementary Note 1. Illumina libraries with 0. 17-, 0. 35-, 0. 5-, 0. 8-, 1-, 2-, 5-, 8-and 15-kb inserts were sequenced at BGI-Shenzghen and University of Illinois, Roy J. Carver Biotechnology. PacBio P. axillaris DNA library was sequenced with P4/C2 chemistry. Illumina reads were processed using Fastq-mcf (quality filtering; https://code. google. com/p/ea-utils/wiki/FastqMcf ), PRINSEQ (duplication filtering; http:// prinseq. sourceforge. net/) and Musket (error correction; http://musket. sourceforge. net/). Pacbio reads were processed using the SMRT Analysis pipeline (v. 2. 0. 1; https://github. com/PacificBiosciences/SMRT-Analysis). Both genomes were assembled with SOAPdenovo 30 with different k-mer sizes.
Acknowledgements We dedicate this work to  Tom Gerats  in honour of his lifelong contributions to petunia research, and we thank him for generously sharing his vast knowledge with all of us. We thank  H. Puchta  for advice on Fig. 4,  R. Köpfli  for IT and graphics support, and  K. Esfeld  for carefully reading the manuscript. This work was carried out without dedicated funding. We acknowledge following agencies who support our work on Petunia:  NWO-ALW  grant  022. 001. 018 (M. B);  Marie Curie Independent Fellowship  (L. G. );  Swiss NSF  grant  31003A_159493  and  NCCR Plant Survival  (C. K. );  NWO-TOP  grant  854. 11. 006 (R. K. ) ;  Borsa di Studio Lanzi per Genetica Agraria ,  Accademia dei Lincei  (V. P. );  NWO-ALW  grant  820. 02. 015  (K. V. );  CNRS ATIP-AVENIR  award (M. V. );  Deutsche Forschungsgemeinschaft  grant  DR411/2-1  (U.
The study of phenotypes for essential gene mutants can also be done with collections of thermosensitive yeast strains (10). The details provided in the present chapter should be helpful to set up and perform reproducible GIM screens and identify either direct genetic interactions or specific interaction profiles that indicate which cellular pathways are required for the cells to adapt to the absence of a given gene. Our experience with large scale quantitative data indicate that crucial to the identification of patterns or interesting correlation is easy visual data representation. Several software solutions answer this question including Mayday (13), geWorkBench (14), or Perseus (15). In the end, GIM screen results are only as good as the capacity of the experimenter to deduce useful information from them.
Microarray data analysis 1. We provide here an example of R session commands used to normalize data post-acquisition and to be able to do diagnostic plots. The input text file is the gpr result of image analysis with GenePix Pro. 2. Define a function allowing to extract the ORF names from the name of the probes (to be changed depending on how probes are identified). An example of identifier is YDR439W-U, where U shows that the barcode is the one located in the upstream position of the KanMX4 cassette. The "namesextract" function uses regular expressions to identify and grab ORF information from the identifier. 10. Find the row numbers for cases when signal higher than background: indexU <-intersect(goodx, which(to_norm$UD == "U")) indexD <-intersect(goodx, which(to_norm$UD == "D")) 11. Simple normalization, by median substraction, will not work well in this case, since the dependence between signal intensity and ratio changes is not linear.
Oligonucleotides: U1 5'-GAT GTC CAC GAG GTC TCT; KU 5'-AAG AAG AAC CTC AGT GGC; D1 5'-CGG TGT CGG TCT CGT AG; KD 5'-GGA TCT TGC CAT CCT ATG; U2block 5'-CGT ACG CTG CAG GTC GAC; D2block 5'-ATC GAT GAA TTC GAG CTC; U2-Cy3/5 5'-Cy3/5-GTC GAC CTG CAG CGT ACG; D2-Cy3/5 5'-Cy3/5-CGA GCT CGA ATT CAT CGA T; 2. Custom Agilent microarray (8x15k version for barcodes, Agilent-026035 Scer_barcode_v2_200911, platform GPL18088 at the GEO database, www. ncbi. nlm. nih. gov/geo/) (see Note 1). Extraction buffer: 2% Triton X100, 1% SDS, 0. 1M NaCl, 10 mM Tris-Hcl pH 8, 1 mM EDTA.
Acknowledgments The protocol presented here was set up initially by  Laurence Decourty ,  Gwenael Badis-Breard  and  Alain Jacquier , data management and analysis were performed by  Christophe Malabat , and large scale developments were made possible by the  ANR  funding of the GENO- GIM  project ( ANR-2008-JCJC-0019-01 ).
1. 3. 13 Oligonucleotides: U1 5'-GAT GTC CAC GAG GTC TCT; KU 5'-AAG AAG AAC CTC AGT GGC; D1 5'-CGG TGT CGG TCT CGT AG; KD 5'-GGA TCT TGC CAT CCT ATG; U2block 5'-CGT ACG CTG CAG GTC GAC; D2block 5'-ATC GAT GAA TTC GAG CTC; U2-Cy3/5 5'-Cy3/5-GTC GAC CTG CAG CGT ACG; D2-Cy3/5 5'-Cy3/5-CGA GCT CGA ATT CAT CGA T; 2. Custom Agilent microarray (8x15k version for barcodes, Agilent-026035 Scer_barcode_v2_200911, platform GPL18088 at the GEO database, www. ncbi. nlm. nih. gov/geo/) (see Note 1). Extraction buffer: 2% Triton X100, 1% SDS, 0. 1M NaCl, 10 mM Tris-Hcl pH 8, 1 mM EDTA.
De novo sequencing and genome comparison High molecular DNA of one of the constituent nuclei of Horst U1 (homokaryon H39) was de novo sequenced with the PacBio RSII technology using and p6-c4 chemistry (Pacific Biosciences, Menlo Park, California, USA). The sequence data (1,147,175 reads with a N50 length of 9461 nt, total 6,446,332,774 nt representing a coverage of 200Â) were assembled into scaffolds using HGAP2 and polished with the Quiver protocol; both are part of SMRT Ò Analysis version 2. 3. 0. 140936. p1. 142411 algorithm (SMRT Ò portal, Pacific Biosciences, Menlo Park, California, USA). Resulting scaffolds were further assembled based on SMRT Ò View BridgeMapper (SMRT Ò portal, Pacific Biosciences, Menlo Park, California, USA), linkage map information (see Section 2 on linkage analysis) and the results of a BLAST search (CLC Genomics Workbench version 7.
5. 1, Qiagen, Aarhus, Denmark) of scaffold ends against all scaffolds. Comparison between the genomes of homokaryons H39 and H97 was done using NUCmer (NUCleotide MUMmer) version 3. 1  (Kurtz et al. , 2004)  with the following settings for the whole genome: mincluster = 1000, minmatch = 100; settings for scaffoldcomparison: mincluster = 500 and nosimplify. A graphical presentation of repetitive elements for each chromosome in both H97 v3. 1 and H39 v3. 1 was done by Blasting individual chromosomes against the RepBase database  (Temple et al. , 2008) , using the following settings: ''sequence source" fungi and selecting ''forced translated search". All other options were not used. The graphical output was sized to each chromosome and plotted along the respective chromosome in the MUMmer plots.
130 (http://picard. sourceforge. net/), the reads were reordered and duplicates removed. For the final indel realignment, GATK version 2. 8-1-g932cd3a  (McKenna et al. , 2010)  was used. All variants were called using Freebayes version v0. 9. 18-1-g4233a23  (Garrison and Marth, 2012). The resulting VCF file was filtered retaining only the positions of type ''SNP" with a read depth >5. For the remaining positions, the data was converted to genotypes and only the positions that represent an SNP between H97 and H39 were retained. The dataset was filtered to retain only the positions that contained genotype data for all homokaryons. This final dataset was converted to be used as input for an adapted R-script (originally part of ReCombine  (Anderson et al. , 2011)  and kindly provided to us by Dr.
, 2012). This sequence shows some misassembly as mentioned earlier  (Foulongne-Oriol et al. , 2013). Using the linkage map of Horst U1 (see later), this misassembly was repaired: the left 197 kb of scaffold 12 was transferred to the end of chromosome 1 and the remainder of scaffold 12 was rearranged resulting in the repositioning of internal telomere sequences to both ends of this chromosome; scaffold 14 appeared to be a duplicate of scaffold 12 and has been removed. The new version has been deposited at JGI (H97 v3. 1) and NCBI (see supplementary Table  S1  for accession numbers).