dataesr/scientific-paragraphs-categorization huggingface dataset:
Phase II Study The study design, participants and procedures have been described before and the trial was registered with Clinicaltrials. gov (NCT 00621764)  [17]. Briefly, this phase II, open-label crossover study of JE-CV booster vaccination with hepatitis A vaccine as a safety control was conducted in Thailand in 100 children aged 2-5 years who had been vaccinated at age 12-18 months with 2 doses of a mouse-brain derived inactivated JE vaccine (Beijing strain), according to the national immunization schedule at that time. Participants were randomly allocated to receive a single dose of JE-CV (0. 5 mL) subcutaneously, followed 28 days later by hepatitis A vaccine (0. 5 mL given intramuscularly), or vice versa, using an interactive voice response system based on randomization lists prepared by the study sponsor for each center and age stratum using the block method.
Acknowledgments. We thank and acknowledge the participation of the infants and parents in Bangkok, Thailand, as well as the contribution of investigational staffs at the  Infectious Diseases Unit, Department of Pediatrics, Chulalongkorn Hospital , the  Division of Infectious Diseases, Faculty of Medicine ,  Siriraj Hospital, Mahidol University , and the  Department of Tropical Pediatrics, Faculty of Tropical Medicine, Mahidol University , all in Bangkok. We also acknowledge with thanks the contributions of  Arunee Sabchareon , and of  Céline Petit  and  Jason Smith  from  Sanofi Pasteur Global Clinical Immunology.
Background: The Short Physical Performance Battery (SPPB) is a well-established tool to assess lower extremity physical performance status. Its predictive ability for all-cause mortality has been sparsely reported, but with conflicting results in different subsets of participants. The aim of this study was to perform a meta-analysis investigating the relationship between SPPB score and all-cause mortality. Methods: Articles were searched in MEDLINE, the Cochrane Library, Google Scholar, and BioMed Central between July and September 2015 and updated in January 2016. Inclusion criteria were observational studies; >50 participants; stratification of population according to SPPB value; data on all-cause mortality; English language publications. Twenty-four articles were selected from available evidence.
Search strategy Appropriate articles were found using the Medical Subject Headings (MeSH) strategy and searching in MEDLINE, the Cochrane Library, Google Scholar, and BioMed Central. The search strategy was created by RP. The terms searched were: ((short physical performance battery) OR (SPPB) OR (lower limb strength) OR (standing balance) OR (walking speed) OR (chair stand)) AND ((mortality) OR (death)). Only articles published in the English language and in peer-reviewed journals were selected. The research was carried out between July 2015 and January 2016. Independent reviewers (RP, GC) analyzed the titles and abstracts of the articles and determined which of them warranted the examination of the full text. Studies included in the analysis had to have the following characteristics: (1) observational (non-randomized) study;  (2)  inclusion of more than 50 subjects; (3) reporting the stratification of patients/population according to SPPB cut-points; (4) presenting data on all-cause mortality in relation to the value of SPPB expressed as hazard ratio (HR) or odds ratio (OR).
Data extraction, definition, endpoint, and contact with authors Independent reviewers (GC, RP, and SV) completed the database, which contained information about the journal, year of publication, authors, baseline characteristics of study population, follow-up length, SPPB cut-points, and source of mortality data. The primary endpoint was all-cause mortality. Additional analyses were performed after stratification of studies according to the following criteria: (1) mean age of the study population (≤75 years versus >75 years); (2) setting (general population versus outpatients versus hospitalized patients); (3) geographical region (North America versus Europe versus Asia); (4) follow-up length (≤1 year versus >1 year and ≤5 years versus >5 years). To obtain standardized data, the authors of all the selected papers (n = 24) were contacted.
The chi-square test was used to test differences between subgroups. To estimate the percentage of deaths that could be attributed to poor physical function, the percentage attributable risk (%AR) was calculated  [37]. Finally, a random effect meta-regression analysis was performed to assess the effect of some potential confounding factors (age, sex, previous history of cardiovascular disease, previous history of cerebrovascular disease, diabetes, hypertension) on the results. Publication bias was appraised by graphical evaluation of funnel plots and through Begg and Mazumdar rank correlation, Egger's regression intercept, and Duval and Tweedie trim and fill  [36]. Statistical analyses were conducted using ProMeta software (Internovi, Cesena, Italy) and RevMan 5 (the Cochrane Collaboration, the Nordic Cochrane Centre, Copenhagen, Denmark).
Search results and study selection After removal of duplicates, 725 titles were identified by the databases search (Fig. 1 ). Overall, 529 items were excluded after the first evaluation of the title and abstract, as they failed to meet the prespecified inclusion and exclusion criteria. Of the remaining 196 records examined, 134 were excluded because they focused on other outcomes or on other physical performance measures. An additional 5 were not retained because they were not original papers but reviews, and 31 because they were study protocols. Twenty-six studies were examined as full papers. Two of these were excluded because they were based on the same study sample used in Lai et al. [15]. The corresponding authors of the retained 24 records were contacted. As previously explained, standardized information was obtained for 17 of them  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] , and these studies were included in the final qualitative and quantitative analysis (Fig.
Study limitations Our results suffer from those limitations that are inherent to all meta-analytic techniques including particularly heterogeneity in populations and variable endpoint definitions across studies. We could analyze data only from authors who replied to our request and, even if statistical analyses do not show the presence of publication bias, this could not be completely excluded. Secondly, we decided to report SPPB score in classes (0-3, 4-6, 7-9, 10-12) and not as a continuous variable. Finally, we only evaluated the association between SPPB and mortality. Additional studies are needed to show that adoption of SPPB into a prediction model improves discrimination of mortality and to evaluate its clinical utility in the practice setting. Nevertheless, this is a meta-analysis on a large sample, including more than 16,000 patients.
Acknowledgements Not applicable. Funding B. Vaes and  D. Legrand  participated in the BELFRAIL study ( B40320084685 ), which was supported by an unconditional grant from the  Fondation Louvain. The  Fondation Louvain  is the support unit of the  Université catholique de Louvain  and is charged with developing the educational and research projects of the university by collecting gifts from corporations, foundations, and alumni. J. Verghese  participated in The Einstein Aging Study, which was supported by  US National Institute on Aging  grants ( P01 AGO3949  and  R01 AGO25119 ). Dr. Verghese  received funding support from  National Institute on Aging  grants ( R01 AG039330 ,  R01 AGO44007 ,  AGO44829 , and  R01 AG036921 ). C. Wang  received funding support from  National Institute on Aging  grants ( P01 AGO3949 ,  R01 AG039330 ,  R01 AGO44007 ,  AGO44829 , and  R01 AG036921 ).
Data analysis was performed using the Trans-Proteomics Pipeline (TPP)  (Deutsch et al. 2010)  with SEQUEST and the International Protein Index (IPI) human database version 3. 72. Data were also converted to the Ensembl protein human database. A probability of identification for each protein was determined using the ProteinProphet algorithm  (Nesvizhskii et al. 2003 ) integrated in the TPP. We only report proteins with a probability of identification >0. 9. Proteins identified in a mock immunoprecipitation performed in parallel were subtracted from the list. The TAL1 immunoprecipitation MS experiment was performed twice, and we report results of both experiments in Supplemental Table  S1. For immunoprecipitation experiments followed by Western blot analysis, we added 100 µg/mL ethidium bromide to the NE to prevent nucleic acid-mediated protein interactions.
6. 2), allowing for a maximum of two mismatches. Using this method, we obtained 122. 5 million unique reads. To detect regions of enrichment, we used modelbased analysis of ChIP combined with massively parallel sequencing (MACS) version 2. 0. 10 using default values with a mock ChIP as a background and a P-value threshold for peak significance of <1 × 10 -10. At this P-value cutoff, 17,396 UTX peaks were identified genome-wide. For TAL1 genomic binding, we used our previously published TAL1 ChIP-seq raw data from Jurkat cells (Gene Expression Omnibus accession no. GSE25000)  (Palii et al. 2011b ) that were reanalyzed using the same parameters as the UTX ChIP-seq. At a Pvalue cutoff of <1 × 10 -10 , we identified 2678 TAL1 peaks genome-wide from 7. 6 million unique reads. Notably, we previously published an analysis of this TAL1 ChIP-seq data set based on the rate at which background signal was converted to foreground signal, demonstrating that this number of reads provides sufficient sequencing depth for genome coverage  (Palii et al.
2011b ). Furthermore, this number of TAL1 peaks in Jurkat cells is in line with TAL1 ChIP-seq data published by others  (Sanda et al. 2012). H3K27me3 ChIP and high-throughput sequencing Histone H3K27me3 ChIP was performed in Jurkat cells with and without TAL1 knockdown using a native ChIP protocol  (Brand et al. 2008)  with the H3K27me3 Ab from Millipore (catalog no. 07-449). For qPCR, chromatin-immunoprecipitated DNA was quantified by real-time qPCR with SYBR Green using a standard curve generated with genomic DNA and was normalized by dividing the amount of the corresponding target in the input fraction. For high-throughput sequencing, chromatin-immunoprecipitated DNA was amplified using the KAPA ChIP library kit (KAPA Biosystems), and paired-end sequencing was performed on an Illumina HiSeq 2000.
Paired 50-bp reads were mapped to the NCBI build 37. 1 (hg19) human reference genome using Bowtie2 (version 2. 2. 4), resulting in 81,706,875 and 72,791,410 unique mapped fragments from wild-type and TAL1-knockdown Jurkat cells, respectively. For H3K27me3 ChIP-seq in DND41 cells, we used the publically available data set from ENCODE (Gene Expression Omnibus accession no. GSM1003521) (The ENCODE Project Consortium 2012) generated using the same H3K27me3 antibodies that we used in Jurkat cells  (Millipore,. DND41 H3K27me3 ChIP-seq reads were downloaded from the University of California at Santa Cruz Genome Browser and mapped to NCBI build 37. 1 using the same parameters as were used for the H3K27me3 reads from Jurkat, resulting in 56,918,481 unique mapped reads. Global analysis of H3K27me3 at TAL1/UTX-cobound sites in Jurkat and DND41 cells H3K27me3 profiles were generated using the BEDTools suite (version 2.
RNA-seq analyses RNA was isolated from Jurkat cells using the RNeasy mini extraction kit (Qiagen), including a DNase I digestion step (1) before and after TAL1 knockdown (72-h time point), (2) before and after UTX knockdown (72-h time point), and (3) before and after GSK-J4 treatment (72-h time point), with two biological replicates per library. Libraries were constructed using the TruSeq mRNA enrich stranded RNA library kit (Illumina), and paired-end sequencing was performed on an Illumina HiSeq 2000. Reads were aligned to the NCBI build 37. 1 (hg19) genome reference sequence using TopHat2 (version 2. 0. 12) (Kim et al. 2013) , guided by the Ensembl release 75 transcript model, and reads were assigned to transcripts using HTseq-count (version 0. 6. 1p1). Differential gene expression analyses were performed using DESeq2  (Love et al.
Acknowledgments We thank  Achim Leutz  ( Max-Delbruck-Center for Molecular Medicine, Germany ) for the pcDNA3 plasmid expressing human  TAL1 ,  Paul A. Khavari  ( Stanford University, USA ) for JMJD3 plasmids, and  Cheryl Arrowsmith  ( Structural Genomics Consortium ; http://www. thesgc. org)for initially providing GSK-J4. We also thank  Lawrence Puente  ( Ottawa Hospital Research Institute Proteomics Facility ; http://www. ohri. ca/proteomics)and  Gareth Palidwor (Ottawa Bioinformatics Core Facility ; http://www. ohri. ca/bioinformatics)for MS and bioinformatic analyses. Highthroughput DNA sequencing was performed at the  Molecular Biology and Functional Genomics Core Facility of the Institut de Recherche Clinique de Montreal (IRCM, Canada ). This project was funded with grants from the  Canadian Institutes of Health Research  ( MOP-82813  to M.
We first introduce in Section 2 background on image search engines (2. 1). Emotion definitions and modelling are given in 2. 2. We discuss in Section 2. 2 how emotions have been extracted from digital documents in the past. More details are given specifically on texts and images. 3 is devoted to the description of our proposed system. We first present Support Vector Machines technology (SVMs), used for our classifiers. Then, an image database, called SENSE, is presented and we justify its usage to train our system. Next, we expose a bottom-up saliency model we used to focus on the most important emotional information of each image. Then we detail the whole processing string our system. We show some of its results on our Internet crowded image database of cooking recipes and compare it to other systems.
Image search engines filtering Image research engines often allow filtering their results. This is usually done by extraction and comparison of image features. Those can be deduced from bottom-up or top-down approaches  [15]. Bottomup methods extract low level features on image data, while top-down methods are task-driven ones using supervised learning. Common filtering techniques get information from direct access within the image file metadata. Examples are bottom-up features like the size; dimension proportion ratio: height, width; style: photo, drawing. Top-down classical features extraction from metadata includes: characters, faces, portraits with head and shoulders. The number of indexed images where features were extracted is also a criteria to compare search engines filters. Table  1  shows a comparison on common filters.
Text is linked to language. If computers could interpret texts, they would be able to interpret our emotions expressed. This textual information, is still combed to decrypt human emotions. For instance, Salway and Graham used audio description text streams to extract characters emotions in movies  [41]. Kamvar and Harris extracted emotional information of textual contents from bloggers all around the Internet  [20]. With social media, needs to express emotions brought up emoticons. They were historically defined by small groups of characters, usually two or three, representing a facial expression (:-) :-| :-( (^_^) and (;_;) are examples). Now they are single character encoded ( or ). Emoticons were used to dig up emotions from text documents  [38, 46]. Hu et al. defined different emotional signals extracted from social media datasets (Twitter and Facebook short messages) to built an unsupervised sentiment analysis system  [17].
Proposed system Our proposed method starts by a Query By Image Content (QBIC) and defines an emotional filter system based on low level images features. This filter can then be applied on image search engines results in Web technologies for instance. For that, we processed an emotional score for each image in a set acquired by QBIC. This score is gained by a fusion of five classifier scores. The classifiers are based on Support Vector Machines (SVMs)  [9]  described in Subsection 3. 1 and powered by libSVM  [8]. Classifiers are trained on a low semantic database called SENSE which is described in Eventually, the whole stream of our emotional filtering system is described in details in the last Subsection 3. 4.
SENSE database Several image databases have been used to study emotions  [28, 44, 50]. The most known and used 9  is the International Affective Picture System (IAPS) 10 [23]. As there were no universal emotional image database focused on extraction of emotional impact, Gbèhounou et al. [13]  proposed one. This image set is named SENSE for Studies of Emotion on Natural image databaS. It is composed of images as diversified as possible and with low semantic power. That is to say that the images do not shock or force a strong emotional answer during subjective evaluation in opposition to some IAPS images for example. Each image has been evaluated by users during very short period of time: eight seconds. This allows to look for primary emotion impact of the presented images. In fact, Schyns et al.
demonstrated that emotions on facial expressions  [42]  are transmitted during the very first 200ms after the reception of the observer signal. During this very short period time, semantic interpretation can not be done by the user. On the contrary, image from the IAPS, embed high semantic power. High semantic can introduce a bias on the evaluation of multiple images as strong emotions remains longer that law emotions in the mind of evaluators  [40]. The authors focused to include only low semantic images in their database in order to minimize or to remove that kind of bias during evaluation. The goal was to evaluate each image for its content and not for the remnant of the previous image emotion evaluation. The database is free to use for research and contains 350 images of landscapes, characters, animals, food, drink, historic and tourist buildings images as shown in Figure  4.
Furthermore, the database also contains very few images with human faces (4. 86%). This last point reflects the desire to limit the interpretation of facial expressions which embed bigger emotional reactions. 10 from the Center for Emotion and Attention (CSEA) at the University of Florida The SENSE database was then evaluated in different subjective experiments. Twenty-five subjects participated in the experiments, 28% women and 72% men. Half were aged 18 to 24 years. They took part on voluntary basis and did not receive a financial reward. The test strategy is closely linked to the model of emotions the authors have chosen. They decided to work with a dimensional model which dimensions are: • The nature of the emotion, • The power of the emotion. This emotion modelling is equivalent to Valence/Arousal model in which, the valence allows to distinguish positive and negative emotions and the arousal that varies from low to high defines the arousal body.
Bottom-up saliency model During the emotional evaluation of images of SENSE, the authors realized that a number of pictures were  not clearly classified by all users. This result is not surprising because of the construction of the image database. In fact, images on the database are selected so they do not cause strong emotional answers to observers. Therefore it seems logical that a number of them are not easily emotionally classifiable. A new experiment was then mounted: evaluate the most salient areas for each SENSE image. Maybe focusing on the most salient zones would allow better emotional classification? This experiement was made in  [13]  resulting in a novel SENSE2 database. For each SENSE image, its most salient region counterpart was added to SENSE2. SENSE2 analysis showed a correlation of emotion evaluations from each SENSE image and their salient counterpart in SENSE2.
An image CBIR's Bag of Words (BoW) method  [4]  is used to product visual words from these feature vectors as it was for training of the emotional classifiers in  [12]. Training process is illustrated in Figure  9. Note that emotions are trained offline on the SENSE database. So we classify each image with these five classifiers by the same training sets used with the SENSE database in  [12]  using Support Vector Machine classifiers (SVMs) implemented from libSVM library with linear kernels  [8]. Each classifier outputs a real score between -1 and +1. -1 stands for a negative emotion impact whereas +1 stands for a positive emotion impact. All the scores are merged by a simple addition into a final emotional score. This is illustrated by Figure  10.
Results and discussions A 100, 000 Internet cooking recipes images database was created by a partner lab in our project. Figure  11  shows image examples of its content. That was a good opportunity to test our model on a large scale. This database is full of a large diversity in the shapes and looks of the displayed dishes. We so presumed that its content do not imply hight emotional impact on viewers. This is then why we choose to use the SENSE database as a trainer for our application. Our system can output different views. First, images can be displayed by processed emotional score order; Figure  8  shows some results of our system on this cooking database. We can see how our system is filtering images and that the result lacks of any  emotional validation. But again, the main novelty of our proposition was to apply for the first time an emotional filtering layer on the results of image search engines.
This validation process will be investigated on future works. Another contribution was to use low-level features from the most salient regions only to describe image emotions. In  [14] , authors used a hybrid approach to define a notion of attractiveness in web image search. This is one of the nearest works we could find from our proposed system. They used EXIF metadata associated with trained high level visual features (quality assessment, aesthetic prediction, affective classification) to define their attractiveness ranks. They also confess that their results are still widely improvable. To improve them, they add images EXIF textual metadata to their model. They also look into the image source web pages for other textual information to help in the classification process. Our approach here is different, we want to extract the emotion information only on the image content.
We used an independent data set of expert-verified checklists  24  to delineate each species' geographical range (convex hull, defined as the smallest convex polygon containing all species records) as the basis for calculations of geographic range sizes (i. e. , extent of occurrence  23  ; in 10 6 km 2 ). For each species, we calculated range size (defined as the total area of the convex hull minus total land area) in ArcGis 10. 0 using a global equal-area Behrmann projection. For each species, we also collated the following life-history traits  21, 22  : trophic group, body size (i. e. , maximum adult total length, in cm), home range, mobility, diel activity pattern and schooling behaviour. Some species were occasionally not sampled because two data sets (WCS and PROCFish; Supplementary Table  5 ) used a restricted species list.