dataesr/scientific-paragraphs-categorization huggingface dataset:
29 FIG. 7. 297 FIG. 7. Reconstruction of zebrafish early embryonic development from multiharmonic imaging data. (A) Top: three orthogonal views of an original dataset combining third harmonic generation (THG; blue) and second harmonic generation (SHG; green). Bottom: THG and SHG details. (B) Cell segmentation after application of a viscous watershed algorithm. Top: viscous filtering applied to the original SHG image. Bottom: resultant cell segmentation. (C) The SHG channel is used to identify cell mitosis, and the integration with the cell segmentation enables cell linage reconstruction (top) and the representation of the spatial deployment of the cell lineage tree (bottom). (A, B) are partly from Luengo-Oroz et al. 51 and (C) from Olivier et al. , 58 reprinted with permission. Color images available online at www.
Finding conformational transitions Experimental setup The proposed method was applied to compute conformational transition paths for the ten proteins listed in Table  3 , and represented in Figure  4. For each protein, at least two experimental structures corresponding to different conformations are available in the Protein Data Bank (PDB)  [41]. The difference between these conformations involves large-amplitude domain motions. The ten proteins are varied in size and topology, as well as in the type of domain motions they undergo. This heterogeneity is important to analyze the reliability and scalability of the method. Each iteration of the algorithm that computes the transition path performs a RRT exploration, as mentioned in the previous section. In the current implementation, such a local exploration runs until the protein moves 0.
As mentioned before, a more sophisticated nearest neighbor algorithm should be implemented. The performance of the method could also be enhanced by applying simplified distance metrics (e. g. [16, 44] ). The use of an appropriate simplified distance metric could reduce computing time while preserving good exploration properties of the algorithm. A closer look at adenylate kinase Adenylate kinase (ADK)  [45]  is a widely studied protein involved in signal transduction. The structure of ADK is composed of three domains known as: LID, CORE and NMPbind. Several works tend to show that the LID and NMPbind domains undergo large-amplitude conformational changes with respect to the CORE domain, which remains stable  [46, 47]. Some of these works (e. g. [47] ) also suggest that the conformational transition between open and closed states of ADK proceeds in two steps: The open conformation of ADK (PDB ID 4AKE), the closed conformations (PDB ID 1AKE) of ADK, and several intermediate conformations obtained with our method are represented in Figure  7  The figure shows significant conformational changes of the LID and NMPbind domains, as expected.
The motion of these two regions is also illustrated in Figure  8 , which represents the displacement of the residues along the conformational transition. Two darker regions, involving residues 20-60 and 130-160, indicate the parts of the protein that undergo larger displacements. These regions correspond to the NMPbind domain and LID domain, approximately. Figure  8  also shows that residues 20-60, corresponding to the NMPbind    The open conformation of ADK (PDB ID 4AKE), the closed conformations (PDB ID 1AKE) of ADK, and several intermediate conformations obtained with our method are represented in Figure  7  The figure shows significant conformational changes of the LID and NMPbind domains, as expected. The motion of these two regions is also illustrated in Figure  8 , which represents the displacement of the residues along the conformational transition.
Prior to microarray data preprocessing, it was confirmed that the quality of all microarray data satisfied both the Affymetrix-recommended and CSC/IC Microarray Centre in-house quality control criteria. Processing of microarray data was performed using Bioconductor packages in R. Individual E10. 5 embryos (wild type or mutant) were hybridized on individual arrays. For each array, 25-mer probe hybridization intensity data were preprocessed using the Robust Multichip Average (RMA) algorithm in the "affy" R package  (14). The arrays were preprocessed together as they showed a similar data structure, judging from box plots of perfect-match probe hybridization intensities. The RMA algorithm was chosen as it provides more precise measurements for probes with lower expression values and eventually leads to higher sensitivity and specificity in detecting differential gene expression.
These strengths of the RMA algorithm are especially important in the context of this study, as the changes in gene expression resulting from derepression of Xi alleles would be no more than 2-fold. After preprocessing, probe set-based expression data were log 2 transformed. For preprocessed probe sets, their fold changes in expression (still log 2 transformed) were calculated by subtracting the mean for control samples from the mean for mutant samples. Detection of differential expression was carried out by using linear models and specifically the empirical Bayes methods  (15)  implemented in the R/Bioconductor package "limma. " Multiple-testing correction was carried out in order to control the false-discovery rate (FDR) using the methods of Benjamini and Hochberg  (59)  as implemented in the R/Bioconductor package "multtest.
" To map probe sets to the genome, Ensembl microarray probe mapping data (from Ensembl release 49, April 2007 Genebuild, NCBI mouse assembly m37, 23,493 genes) were used. Any probe set which was mapped to more than one gene was considered to be nonspecific and was discarded from analysis. In this project, 25,794 out of 45,037 probe sets (57. 27%) were mapped to unique locations in the genome, corresponding to 15,539 genes (66. 14%) in the Ensembl gene set. Combining differential expression (at the probe set level) and probe set mapping data, two categories of differentially expressed probe sets were defined: (i) those which were mapped to unique locations in the genome and with FDRs of Յ10% (the more stringent category) and (ii) those which mapped to unique locations in the genome with FDRs of between 10% and 30%.
The sequences of the primers used for Fig. 2E  can be found in references 2 and 16. A melting curve test was performed for each experiment to ensure the specificity of amplification. Data were normalized to expression of several housekeeping genes (␤-actin, Idh2, Hmbs, and Gapdh). A representative example is shown in the figures. Each amplicon was analyzed in duplicate at least three times on cDNA preparations from individual embryos. Bisulfite treatment and analysis by Sequenom or sequencing. Genomic DNA was isolated from individual embryos and somatic tissues (brain or spleen) using standard methods. Bisulfite conversion of DNA was performed using the Epitect bisulfite kit (Qiagen) according to the manufacturer's instructions. All primer sequences and PCR conditions for amplifying from bisulfite-treated DNA are available upon request.
The final PCR product in each case was loaded on a gel to ensure accurate amplification of a single band of correct size. For Sequenom analysis, all primers incorporate a T7 promoter tag and PCR products were analyzed by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry after in vitro transcription and specific cleavage by Sequenom Inc. (17). For each amplicon, three independent DNA samples were analyzed, except for somatic tissues, where only one sample was analyzed. For conventional bisulfite analysis, PCR products were gel purified and subcloned into TOPO TA vector (Invitrogen). Individual col-onies were analyzed by direct sequencing using the M13R universal primer. Accession number for microarray experiments and differential expression analysis. Details of sample preparation and hybridization for microarray analysis have been deposited in ArrayExpress under experiment accession number E-MTAB-921.
5 embryos. (D) Expression levels of three X-linked genes were measured by RT-qPCR in wild-type (wt) and Dnmt3b mutant (Ϫ/Ϫ) E9. 5 embryos and directly compared with data obtained for wild-type and Smchd1 Ϫ/Ϫ embryos from panel C. Methylation levels, shown as a reverse histogram, were measured for the Eda2r, Atrx, and Gpc3 CGIs in wt and Dnmt3b Ϫ/Ϫ E9. 5 embryos and wt and Smchd1 Ϫ/Ϫ E10. 5 embryos by Sequenom. (E) Expression levels of nine X-linked genes and three autosomal targets were measured by RT-qPCR in female (F) and male (M) wild-type (wt), Dnmt3b ϩ/Ϫ , and Dnmt3b Ϫ/Ϫ E9. 5 embryos. Methylation levels, shown as a reverse histogram, were analyzed using bisulfite Sequenom analysis for five genes in female E9. 5 wt and Dnmt3b heterozygous and homozygous mutant embryos. For all genes, graphs represent the mean from at least 3 individual embryos Ϯ standard deviation of the mean (except for Dnmt3b E9.
The methylation patterns of the CGIs linked to several Pcdha variable exons and Pcdhb genes were assessed using Sequenom bisulfite analysis of male and female E10. 5 embryos. The results obtained showed 40 to 80% methylation in wild-type E10. 5 embryos and in several cases significant hypomethylation in Smchd1 Ϫ/Ϫ embryos (Fig. 9A  and B ), suggesting that Smchd1 plays a role in establishing or maintaining normal methylation levels in this region. These findings were substantiated using conventional bisulfite sequencing of selected loci (data not shown). This analysis also demonstrated that methylation of Pcdha and Pcdhb CGIs occurs equally on both parental alleles. These observations do not rule out random allele-specific methylation re-stricted to the limited population of Purkinje neurons, in which individual Pcdh genes are expressed.
ACKNOWLEDGMENTS We thank  Claire Senner  and  Sara Norton  for help with dissecting embryos, the  CSC/IC Microarray Centre  for technical assistance, and all members of the  Brockdorff and Heard labs  and  Irina Stancheva  for stimulating discussion and advice. N. B. is funded by the  Welcome Trust ,  United Kingdom  ( PRF 081385 ). Work in E. H. 's lab is supported by an  ERC Advanced Investigator Award , the  ANR ,  EU   FP7  Integrated Projects SYBOSS, MODHEP, and the  EU EpiGeneSys Network of Excellence. A. -V. G. was supported by an  HFSP postdoctoral  fellowship. We declare that we have no conflict of interest.
While our investigation builds on existing work in speech and video search, inter-item linking for multimedia collections is a much less well established, and our approaches to this task are thus more exploratory in nature. Our experimental search and hyperlinking methods explore use of audio and visual content, examining both search ef-fectiveness and the behaviour of linking when using different media streams  [19]. In the remainder of this paper, Section 2 reviews the context of our evaluation approach based on related work in the field, Section 3 outlines the MediaEval 2012 Search and Hyperlinking task and dataset, Section 4 provides details of the different search and hyperlinking generation techniques used in our study, Section 5 presents the results of our experiments, and Section 6 summarizes our findings and discusses potential future research directions.
Further the user experience of browsing through the collection is enriched by the list of potential hyperlinks to the segment retrieved in the Search sub-task stage. In the following subsections we overview the description of the video dataset, and then continue with descriptions of the individual sub-tasks. Blip10000 dataset The Blip10000 dataset created by the PetaMedia NoE  [18]  contains 14,838 Creative Commons videos from blip. tv, and corresponding user provided metadata. The data comprises a total of ca. 3,260 hours of data and is divided into development and test sets, of 5,288 and 9,550 videos respectively. Additionally, two transcripts were provided for all the videos in the collection, by LIMSI/Vocapia  [15]  and LIUM  [26]. The full Blip10000 dataset used at Me-diaEval 2012 contains videos in different languages.
Howthis is conceptually distinct from the first step in the search and hyperlink sub-tasks. ever, since the main focus of our current study is to define suitable techniques and evaluation methods for a complex search and hyperlinking user experience, we wished to work only with a monolingual English language dataset. To this end, we use a subset of the test set classified as English language by LIMSI/Vocapia, and transcribed by English language versions of both ASR systems. This resulted in a corpus to 4,890 video files. The transcripts generated by the ASR systems differ in various ways, especially in the number of terms with a total collection frequency equal to 1-10, see Figure  2. In addition to spoken transcripts, the dataset was indexed with various visual information: shot boundaries of average shot length circa 30 seconds  [14]  with a single visual keyframe for each shot, concept-based descriptors based on a list of 589 concepts detected using the on-the-fly video detector Visor  [2] , and face detection results  [3].
The concept list was created by taking the tags of the Tagging task set at MediaEval 2011 and calculating a score for each tag a representing its corporeality using the algorithm described in  [17]. A threshold was chosen to cut the list off at a reasonable length. MediaEval 2012 Test Query Set The MediaEval 2012 test query set consists of 30 textual queries. These include query statements both in a natural language sentence (NLS) and in the form of a search engine request (SER) style: e. g. Query 3: textual fields: NLS: "Curtis Baylor of Allstate gives a small piece of planning advice for small business using his basic three factors. ", SER: "interviews with business professionals"; multimodal features: face: "yes", colours: "dark", video content: "Chair, Man, White Shirt". The NLS query set was used for all experiments reported in this paper.
Search Sub-task The main goal of the search sub-task is to find the exact topically coherent passage in the video collection relevant to the given query. Failure to achieve this task can negative impact on automatic hyperlinking performance and the overall user experience. According to the problem setup we have two types of queries: textual and multimodal (textual information is enriched with video content information). In this section we describe our approaches with respect to the following aspects: segmentation method, use of metadata, and use of different retrieval models. The overall list of the runs and naming convention is shown in Table  2. First we put the segmentation method with the first letters of the name, 'M' distinguishes runs that use the metadata, and then the retrieval model used is named (for example, Shot segmentation using Metadata and the TF-IDF IR model is named 'ShM tf idf').
5 seconds is considered to be a pause), segmentation into the range of different segment lengths with window overlap of different size. Another source for potential segment boundaries based on the video channel are the shot boundaries. Retrieval models We experimented with three different retrieval models: BM25 [25], Hiemstra's Language Model  [11]  with default parameters, and TF-IDF. We used the Terrier 3  IR system for runs based on time segmentation (types 'T(M)', 'TOv(M)'), and the Lucene IR system for runs based on sentences and speech segments (types 'S(M)', 'SpS(M)') In all methods standard stop words removal and Porter stemming was carried out. Metadata exploitation Since ASR transcripts contain errors, and may not contain all relevant information for the videos, any available metadata becomes potentially useful to compensate for this information loss.
Hyperlinking Methods Since approaches to the hyperlinking sub-task are not as well established as those for the search sub-task, multiple approaches were implemented in order to get a broader perspective on potential solutions. We used all modalities of the provided dataset: metadata, information based on the audio (transcript words, transcript sentences and segmented speech units) and video (shots boundaries, visual concepts) processing. Additionally an enrichment tool (DBpedia Spotlight  [21] ) was used to identify named entities (NE) in the transcript and link these to Linked Data resources. Visual features were also enriched by clustering the information from SIFT descriptors into 500 clusters corresponding to visual words, as described in  [29]. All approaches use the following main processing stages: 1.
Retrieval of potential links to videos or video segments, when the retrieval units might be different from the target units (the whole video instead of the shot). 2. Re-ranking of the returned potential links. 3. Segment extraction. In cases where the retrieval units do not correspond to the target units. The difference in implementation and ordering of these stages accounts for the differences in the results of the reported runs. Table  3 , gives an overview of the representation of data and retrieval techniques used for the runs. As in case of search sub-task, determining the boundaries of the target segment of a video link is important because they shown to the user. When naming the runs we start with the first letters of the target segment (even though at retrieval stage those segments might be part of larger units), then after an underscore we add the types of the data used and conclude with adding the letter 'M' in case of use of metadata, for example the run that target to find the segments of shot size and uses speech transcript, visual words, and metadata is called 'Sh TVWM'.
Retrieval Methods for Hyperlinking Different retrieval techniques were used to identify and rank potential segments for hyperlinking. Videos and queries were represented as TF-IDF vectors with alternative compositions consisting of: transcript words only ('TrT T'); transcript words and metadata ('TrT TM' and 'Sh TM',). Another approach was to have several TF-IDF vectors for each representation and to combine the ranking results later in the process, this approach is labeled run 'Sh TVWM' and combines two vectors: transcript words and metadata, and visual words. In run 'Sh TNE(M)', the TF-IDF vector uses Named Entities (NE) instead of words: a new weight called TF-IS (term-frequency-inverse-support) is used which depends on the number of URLs linking to this NE in an RDF graph of DBpedia Spotlight; and the metadata tags are ranked using the Jaccard similarity measure.
Hyperlinking Sub-Task As described in Section 3 the relevance assessment for the hypothesized hyperlinks was conducted after completing the runs. Table  6  shows the results of the runs using different transcript types, with MAP values for two types of qrels. The run using only visual features for retrieval (SpS VS) has the highest MAP on its "run" qrel and the lowest on the "unified" qrel, this means that it is oriented to retrieve links based on video stream features, but cannot retrieve the other types of links. Use of visual words in combination with the transcript and metadata (runs 'Sh TVWM VisualReranking' and 'Sh TVWM RankReranking') decreases the result in comparison with the simple run based only on transcript and metadata ('Sh TM'). We assume that visual features used are too low level to improve the results.
CONCLUSIONS AND FUTURE WORK This paper has described our investigation into a scenario modeling multimodal search and automated hyperlinking of multimedia content, for a situation where a user wishes to search for a remembered specific item in a collection, but does not remember where it is located, and subsequently wants to follow hyperlinks to related videos in the collection that would enrich their browsing experience. We are among the first to work on both sub-tasks of multimedia search and hyperlinking creation using multimodal aspects of the collection with one scenario in mind, using a standard benchmark collection as the dataset. We explored the space of possible approaches to the proposed search sub-task by varying segment length, use of metadata, and different retrieval models, in order to better understand and address the search sub-task for textual and multimodal queries.
We investigated various use of audio and video features for the hyperlinking sub-task, and carried out several combination runs using search sub-task output and hyperlinking methods. Use of visual features for the search sub-task impacted negatively on the results: multimodal queries received lower scores for the same methods, and shot segmentation did not outperform time or transcript based results. Our proposed pooled strategy for evaluation of the hyperlinking sub-task did not include the distinction at the stage of relevance assessment whether the audio or the visual features were the basis for the decision. However, it did allow us to assess separately the performance of each individual method of hyperlink creation and compare this with a unified list of relevant data gathered from all runs.
ACKNOWLEDGMENTS This work was supported by  Science Foundation Ireland  (Grant  08/RFP/CMS1677) Research Frontiers Programme 2008  and (Grant  07/CE/I1142 ) as part of the  Centre for NExt Generation Localisation (CNGL)  project at DCU; cofunded by  European Commission 's  Seventh Framework Programme  ( FP7)  as part of the  AXES  project ( ICT-269980 ) and ( FP7-269980 ); by the  Dutch COMMIT program ; by  Ghent University, iMinds , the  IWT Flanders , the  FWO-Flanders , and the  European Union , in the context of the iMinds project SMIF; by the  Czech Science Foundation  (grant no. P103/12/G084 ).
Assessment of tissue sparing at the injury site Tissue sparing was analyzed 8 weeks after SCI, at the center of the lesion, 2 mm rostral and 2 mm caudal to the epicenter. Sections were stained with LFB-HE. The measurements were carried out on coded slides using ImageJ software (NIH). Cross-sectional residual tissue was normalized with respect to total cross-sectional area and the areas were calculated every 500 lm within the rostrocaudal boundaries of the injury site.
We photographed the entire transverse section at 10Â primary objective using tiling and stitching software (StereoInvestigator). Each scan was performed using identical laser power, gain and offset values. The immunodensity of each image was measured using NIH ImageJ software. The relative fluorescence values were expressed as fold increases over the baseline (uninjured cord). We divided the integrated density by the sample area to calculate the mean density per unit area. This calculation was performed to compensate for the different sizes of the region of interest in the spinal cord. The final immunodensity values were then averaged for each group  [16].
3% Triton X according to manufacturer's instructions for 2 h. Tissues were then incubated for 10 min with the NovaRED substrate kit (Vector Laboratories) for visualization of antibody binding. The slides were then dehydrated through an alcohol series, cleared in xylene and coverslipped. We photographed the entire horizontal section at 10Â primary objective using tiling and stitching software (StereoInvestigator). Using ImageJ Software (NIH), we measured the integrated density of BDA labeling in a traced area. Background intensity from an area with no BDA positive staining was subtracted from the intensity value to correct for nonspecific reactions. The density values at the BDA labeled region were normalized to the values obtained from control cord.