dataesr/scientific-paragraphs-categorization huggingface dataset:
69% for the Scouter dataset and 43. 21% for the PEVID-HD dataset. This represents an improvement of more than 6 percents for the Scouter dataset and more than 11 percents for the PEVID-HD dataset. We conclude that the proposed approach improves the retrieval performance, outperforming some other existing approaches, e. g. , BoW, HoG, color naming, etc. Figure  6  presents several system responses, when we use the best system configuration (Fisher Kernel with PHOW features and Linear SVM). The first query provide five examples of true positives (TP) examples in which the object found by the system are correctly identified according to the ground truth (note the scenario difficulty, different fields of view, object dimension, different object color, illumination, camera noise and other objects around the object of interest).
Anyway there are also two false negatives situations (NT) in which the system is unable to classify correctly (according to ground truth) the object detected due to the signal noise, illumination conditions (insufficient, over exposed), partial object view (out of frame, junction with another object) or dimension too low. A similar example is also provided for the PEViD-HD dataset. Five of them represents frames that contains objects correctly identified according the ground truth. On the other side, we also provide several examples where the system is not able to provide correct response.
F. Computational complexity In this section we discuss the computational complexity of the proposed description framework. We analyze the time for computing each processing step, from feature extraction to video classification. We perform this experiment on the Scouter dataset which contains more than 36,000 of video frames. The run-time is evaluated on a regular PC machine using a 2. 9 GHz Intel Xeon CPU and 24GB of RAM. We do not use parallelization. Experiments were run with SIFT features and Linear SVM classifier. The computational cost per frame is presented in   A processing chain would take 450 ms per frame (12 seconds for 1 second of video, i. e. , 25 frames). However, the most time-consuming components (i. e. , motion detection and SIFT computation) can be computed only once, when the video footage is recorded.
Complete fusion cross section Only twelve experiments have explicitly been designed to measure both complete and incomplete fusion components   4 ) and for the complete fusion data (main inset) as a function of E avail. The full violet curve in the inset and the main panel is due to the best fit of the complete fusion data by the homographic function  (3). The full red curve in the main panel is due to the same kind of fit to CF+IF data. The orange and light-violet background bands around the best fit curve in the main panel and in the inset, respectively are due to the errors on the fit parameters. The dashed blue curve is the difference of both fusion excitation functions. The inset in the main inset displays the ratio of CF and CF+IF best-fit excitation functions as a function of E avail.
Then we used the software of each device to automatically eliminate ectopic values and average the data every 5 s. Heart rate (HR) was continuously recorded beat-tobeat (Polar RS800, Polar Electro, Kempele, Finland) and averaged every 5 s. Small capillary blood samples (0. 5 µL) were collected from finger to assess basal lactate concentration. A sample of lactate at rest was taken upon arrival of the player and before warmup, immediately after the test and 3 min after during the passive recovery period. Net blood lactate values ( [Lact -]) were calculated by the difference between the peak [Lact -] values and rest values. All blood samples were analyzed using a portable lactate analyzer (Lactate Pro, Arkray, Japan) calibrated before each test using a standard strip of provided by the manufacturer  (Pyne et al.
11 The two rounds form a panel of 6,008 households across 242 villages in 15 states. The REDS was initially designed to be representative of rural India, excluding states in significant conflict. 12 Given the long time period covered by the panel, current representativeness of the survey data for rural India can be questioned and we can only make inferences for the baseline panel sample at subsequent dates. In both rounds, the household survey collected data on education, health, marital status, labor supply (main and secondary occupations), and sources of farm and nonfarm income, access to infrastructure and facilities, consumption expenditure and assets, agricultural production, and land owned and inherited. 13 Accompanying village surveys for both rounds elicited community access to facilities, infrastructure, population characteristics, prices, and wage rates by activity for men and women.
Together with household division since 1982, this results in a sample of 7,474 households. The increase in the number of households with baseline information in 1982 and hence in the 1982-1999 panel is explained by household splits over time. 13 Both survey rounds also collected data from women on their time allocation and that of their children. This is likely to be quite noisy data, especially in measuring changes over time (not helped by changes in the questionnaire for reporting time allocation). We decided that the data were not usable for our purpose. 14 There are 122 households for which our electricity indicator is positive in 1982 and zero in 1999. Of these, 42 split off from a mother household that maintains its electricity status over time; 42 split off from an initial household with electricity to a number of households of which none have electricity in 1999.
The number of days worked in each activity is calculated based on the total hours reported in the activity during the last year, divided by eight. Days are then expressed as a household mean for male and female members aged sixteen to fifty-five. The data are unweighted. Source: 1982 and 1999 REDS panel. In choosing controls, we have been guided in part by past literature. In the closest prior study for India, Khandker et al. (2012) controls for household demographic characteristics, maximum education, land and nonland assets, and infrastructure. The REDS allows a similar set of controls. Our base-year household level controls include demographic characteristics (age, age squared, and marital status of the household head, disability/illness in the household, size and age/gender composition, religion, and caste); maximum years of schooling of any adult; and wealth variables (dummies for landownership and whether the house is built with bricks and inherited land amounts).
Finally we include a dummy variable to indicate whether the household split between the survey rounds. Physical proximity to power generating plants is known to have influenced the placement of grid infrastructure (see, for example, Chaurey et al. 2004). For identification purposes, we use data on the location of power generating plants obtained from the 2004-5 CO2 Baseline Database for the Indian Power Sector, published by the Central Electricity Authority of India. This public database has detailed information on each power plant in India, across different types of power (hydropower versus thermal, for example) and generating capacity. The data include the date at which each power plant became active. Using the GPS locations of the REDS survey villages, we calculated the straight-line distance from each village to the nearest power plant for 1965 (as a benchmark before the REDS survey, during the time when rural electricity generation capacity was beginning to expand) and for 1975.
In summary, we find that household electrification increased male labor supply. Men's regular wage work increased, much of it coming from casual wage work, and some from other activities, including leisure. Using the wage rates reported in the village survey we can also test whether there is any sign that village electrification affected wage rates. We used the data on harvest wage rates to compare the changes in mean wage rates for those villages that became electrified between the two survey rounds with those for the villages that already had electricity in 1982. The DD estimate gave a small gain of Rs. 4. 19 per day (in 1999 prices) for women and a small loss for men of Rs. À0. 93; however, neither is significantly different from zero (standard errors of Rs. 3. 87 and 3. 68). We also repeated this analysis using the number of years the village was electrified as the treatment effect; again, there was no significant impact on wages.
In this case, Pt is distributed along the whole lter paper surface and hence, the integrated 195 Pt + signal varies with the size of the DBS ablated, so that direct normalization of the analyte signals with the integrated  195  Pt + signal will lead to erroneous results. In order to avoid this potential problem, the average 195 Pt + signal recorded during the integration interval considered for the analytes was used for normalization instead. The performance of the two different IS deposition methods considered was next compared following the protocol described above for data treatment. For this purpose, analysis of the Lyphochek Whole Blood Control level 2 reference material was carried out using the two different IS deposition strategies. This material was selected as it exhibits comfortable concentration levels for all the analytes.
Acknowledgements J. Rofes  has a  postdoc Marie Curie Fellowship  ( MCA-IEF Project nº629604 ) of the European Comission; he is also a member of the  Atapuerca  Project  CGL2012-38434-C03-01  of the  "Ministerio de Economía y Competitividad de España" (MINECO). B. Martínez-García  has a postdoc Grant (" Contratación para la especialización de personal investigador doctor ") of the  UPV-EHU. N. Garcia-Ibaibarriaga  has a predoctoral Fellowship ( BFI-2010-289 ) of the  Basque Government. We received economical support from the Project  GIU12/35  of the  UPV-EHU. The  "Diputación Foral de Bizkaia " financed all the fieldwork campaigns at AK. Thanks to  Mónica Aguilera  and  Carlos Tornero  for their thoughtful comments on stable isotope topics.
The medium was then replaced, and the cells were stimulated for 24 h with 200 ng/ml LPS or left untreated. The cell supernatants were collected, centrifuged at 500 ϫ g, passed through a 0. 22-m filter to remove cells and immediately frozen in liquid nitrogen. The Rat Cytokine Array Panel A from R&D system was used to probe the cytokines in the secretome of stimulated and nonstimulated NR8383 cells by following the procedures recommended by the manufacturer. The membranes were quantified by densitometry using ImageJ software. Statistical analyses were performed using the paired t test. Error bars represent the S. E. Total and Nuclear Protein Extracts-NR8383-K D and -NT cells were plated on sterile six-well plates until confluent. For LPS stimulation, the cells were starved overnight with Ham's F12K medium supplemented with 2% FBS.
Western Blot Analysis-The total cell extracts (40 g) or nuclear extracts (5 g) were then analyzed by Western blot assays. Primary antibodies were rabbit anti-IRF3, mouse anti-phospho-IB␣, mouse anti-IB␣ (1:1000, from Cell Signaling Technology, Leiden, The Netherlands) and rabbit anti-lamin A (1:1000, from Abcam). Horseradish peroxidase-coupled goat antimouse and goat anti-rabbit secondaries (Jackson ImmunoResearch) were used at 1:30000 and 1:20000 respectively. ImageJ software was used to quantify the bands. Filter-aided Sample Preparation (FASP)-The total protein extract (0. 1 mg) was used for FASP analysis as described previously  (14). We performed FASP using Microcon devices YM-10 (Millipore) before adding trypsin (Promega) for protein digestion (40 g/ml in 0. 05 M NH 4 HCO 3 ). The samples were incubated overnight at 37 °C.
The peptides were separated using an increasing amount of acetonitrile (5-35% over 100 min) at a flow rate of 300 nl/min. The LC eluent was electrosprayed directly from the analytical column, and a voltage of 1. 7 kV was applied via the liquid junction of the nanospray source. The chromatography system was coupled to a Thermo Scientific Q Exactive mass spectrometer that was programmed to acquire in a data-dependent Top 10 method. The survey scans were acquired at a resolution of 70 000 at m/z 400. Data Analyses-All MS data were processed with MaxQuant (25) using the Andromeda (26) search engine. The proteins were identified by searching MS and MS/MS data against the Decoy version of the complete proteome for Rattus norvegicus in the UniProt database (UniProt Consortium. Reorganizing the protein space at the Universal Protein Resource (UniProt.
Nucleic Acids Res. 2012, 40 (Database issue), D71-5. ) (Release June 2014, 33675 entries) combined with 262 commonly detected contaminants. Trypsin specificity was used for digestion mode, with N-terminal acetylation and methionine oxidation selected as variable, and carbamidomethylation of cysteines set as a fixed modification. We allowed up to two missed cleavages. For the MS spectra, an initial mass accuracy of 6 ppm was selected, and the MS/MS tolerance was set to 20 ppm for the HCD data. For identifi-cation, the FDR at the peptide spectrum matches (PSM) and protein level was set to 0. 01. Relative, label-free quantification of the proteins was conducted using the MaxLFQ algorithm  (27)  integrated into Max-Quant with default parameters. The data sets and Perseus result files used for analysis were deposited at the ProteomeXchange Consortium (28) (http://proteomecentral.
proteomexchange. org) via the PRIDE partner repository  (29)  with the data set identifier PXD001984 for cellular extracts (For reviewer access only Username: reviewer62003@ebi. ac. uk; Password: zokVruKN) and PXD001986 for the secretome analyses (For reviewer access only Username: reviewer19925@ebi. ac. uk; Password: PUUJeVfO). Analysis of the identified proteins was performed using Perseus software (http:// www. perseus-framework. org/) (version 1. 5. 0. 31). The file containing the information from the identification and hits from the reverse database were used, and proteins with modified peptides and potential contaminants were removed. The LFQ intensity was logarithmized (log2(x)). Categorical annotation of the rows was used to define the different group depending on the following: (1) the cell line (NT or K D ); (2) the treatment (Ctrl/LPS); and 3) the kinetics of the secretomes (1 h, 16 h, 24 h, 48 h, or 72 h).
Specifically, to compare the relative protein abundances between and within samples, the protein length normalized to the log 2 protein intensities (termed the "iBAQ" value in MaxQuant) was included in the MaxLFQ differences. Functional annotation and characterization of the identified proteins were performed using PANTHER software (version 9. 0, http://www. pantherdb. org) and STRING (version 9. 1, http://stringdb. org). The GeneMANIA Cytoscape plugin  (30)  was used to generate co-expression networks from cell extracts proteomics data. A "basal" network composed of 95,886 recognized interactions was generated from the data obtained by the analysis of K D or NT cells under basal conditions. A supervised clustering was then performed to identify the top 100 molecules that coregulated with Anxa6 in this "basal network.
" The list of 100 genes which encoded molecules were identified was then assessed for gene set enrichment using EnrichR  (31)  and the GO classification. Following the same approach, two "LPS-stimulated" networks were then generated from the data obtained by the analysis of: (1) unstimulated or LPS-stimulated K D cells (89,017 recognized interactions) and (2) unstimulated or LPS-stimulated NT cells (96 563 recognized interactions). A supervised clustering was then performed to identify the top 100 molecules that coregulated with NFKB1 in each of these "LPS-stimulated" networks. Both lists of 100 genes that encoded molecules were identified and then assessed for gene set enrichment using EnrichR and the GO classification. Finally, subnetworks of genes presenting significant enrichments for specific GO terms were selected and visualized on Cytoscape.
For presentation purposes, nodes were assigned equal weights and subnetworks were slightly distorted to avoid nodes superimposition. Systems Biology Analysis-The altered pathways relevant to overexpressed proteins in K D PC1/3 cells were analyzed using Pathway Studio software v. 10 (Ariadne Genomics, Rockville, MD). This soft-ware helps to interpret biological meaning based on gene (protein) expression, to build and analyze pathways, and to identify relationships among genes, proteins, cell processes, and diseases. This software contains a built-in resource named ResNet, which is a database of molecular interactions based on natural language processing of scientific abstracts in PubMed. Using ResNet, a researcher can analyze the gene product/protein list and build a pathway using well-known interactions that are discussed in the existing literature.
The program searches the current pathway database and ResNet for interactions with the selected entities and then adds them to the pathway. After the new pathway was constructed, we were able to obtain more detailed information regarding the putative pathways that were altered in response to LPS treatment. Data Analysis of Mouse Peritoneal Macrophages-An analysis of previously publish data was done to obtain information concerning modifications of proteins induced in peritoneal macrophages from wild type (WT) or PC1/3 KO (KO) mice challenged by LPS (8 h) or not  (10). Briefly, peritoneal macrophage from wild type (WT) and PC1/3 knocked out (KO) mice were collected as described previously  (10). Cytosolic and membrane fractions were then prepared according to  (16)  and processed for Gel-LC-MSMS as described in 10.
The relative protein expression was calculated based on the protein score, which was shown to be an adequate relative indicator of the relative differential expression  (20). Ratio between WT and KO was calculated using the sum of corresponding fractions. Gene ontology analysis was performed using Blast2go  (21). The network analysis was performed as follows: The gene names of identified proteins were used as input to retrieve a network from STRING  (22) , and this network was then loaded into Cytoscape 3. 2  (23, 24). with relative expression data using Id mapper. The Reactome FI pluggin was used to select a subnetwork of gene ontology terms and NCI database-associated specific proteins Calcium Imaging-Cells were plated onto glass coverslips and loaded with 4 M Fura-2 AM at room temperature for 45 min in growth medium.
PC1/3-knockdown NR8383 Cells Express an Inflammatory Profile-A stable PC1/3-K D cell line has been developed by lentiviral delivery of shRNAs  (11). To better understand the interaction and regulation of TLR4 by PC1/3, we used these PC1/3-K D NR8383 cells in the present study. A proteomics analysis was performed to identify secreted proteins subsequent to PC1/3 silencing in NR8383 cells, before and after LPS challenge. Shotgun proteomics of secreted proteins (from 1 h to 72 h after stimulation) was performed for nontarget (NT) control cells and K D cells (Fig. 1 ). More than 1400 proteins were identified in all analyses of the NR8383 secretomes (supplemental Data S1). Comparison of the identified proteins between resting K D or NT cells and cells challenged with LPS allowed the identification of 28 specific proteins that were directly related to PC1/3 (Fig.
1A ). These proteins were only secreted by K D cells, both resting and those stimulated with LPS. The proteins are involved in different functions, e. g. responses to stimuli, RNA processing, endocytosis, regulation of transcription, catabolism, protein binding, regulation of axogenesis and immune responses (Table  I ). This study was then further coupled to a kinetic study at 1 h, 16 h, 24 h, 48 h, and 72 h after LPS challenge. Proteins with an abundance that was significantly different among the conditions were determined according to the MaxQuant and Perseus software. As a criterion of significance, we applied an ANOVA significance threshold of p Ͻ 0. 05, and heat maps were created. A total of 125 proteins in the cell culture super-natants of the macrophages stimulated with LPS versus nonstimulated macrophages were considered reliable based on the statistical analysis (Fig.
3 , supplemental Data S3). For the ANOVA test, the samples were classified between NT and K D. Two clear clusters were highlighted. Cluster number 1 represents over-expressed proteins in K D cells, whereas cluster number 2 groups over-expressed proteins in NT cells (Fig. 3A , Table  III  and supplemental Data S4). The proteins in each cluster were then analyzed using PANTHER software (http://www. pantherdb. org) to determine the biological functions based on the protein classes. Using this analysis, we demonstrated that over-expressed proteins in K D cells were implicated in cell adhesion, extracellular matrix or cytoskeleton, whereas in NT cells, overexpressed proteins were involved in nucleic acid binding or oxidoreduction functions (Fig. 3B ). For a more detailed analysis, we performed a systems biology analysis for network identification of the over-expressed proteins in PC1/3-K D cells (Fig.
Significant differences were analyzed using Student's t test. *p Ͻ 0. 05. proteins are required for the regulation of cell proliferation in response to extracellular signals from an early endosomal compartment and also play a role in the biogenesis of endo-some carrier vesicle (ECV)/multivesicular body (MVB) transport intermediates from early endosomes  (38). Moreover, these proteins are involved in the late steps of the endosomal FIG. 3. Whole-cell extract analysis strategy. A, NR8383 cells were stimulated with LPS (200 ng/ml) or not (control) and lysed before FASP and LC-MS/MS analysis. MaxQuant and Perseus software were used for the statistical analysis, and a heat map was generated to show proteins that were significantly different between NT and PC1/3-K D NR8383 macrophages in the cell extracts.
Two clusters are highlighted. B, The proteins in each cluster were analyzed using Panther software. The biological functions associated with these proteins are shown. The functions framed in red correspond to those of proteins that were over-expressed in K D cells, whereas the functions framed in black correspond to those of proteins that were over-expressed in NT cells. C, Global pathway analysis of the over-expressed proteins identified in PC1/3-K D cells. Over-expressed proteins in PC1/3-K D cells were involved in global altered molecular pathways. The different colors reflect the degree of expression. Proteins in blue are over-expressed in PC1/3-K D cells, whereas those in orange show no expression differences between NT and K D cells. MVB pathway. MVBs contain intraluminal vesicles that are generated by the invagination and scission from the limiting membrane of the endosomes and are mostly delivered to lysosomes, enabling the degradation of membrane proteins.
This phenomenon indicates that PC1/3 K D remodels the endosomal compartment, which is consistent with our data obtained using macrophages isolated from PC1/3-KO mice. Ontogenic enrichment of proteins issued from our previous publish proteomic analyses of peritoneal macrophages  (10)  from wild type (WT) or PC1/3 KO (KO) mice challenged by LPS (8 h) or not, confirms the over-expression of cytoskeleton proteins as well as GTPase activity (5 Go terms) (Fig. 5A ). STRING analysis of proteins with GTPase activity has been performed and the network subsequently analyzed using the visualization tool Cytoscape followed by the Integrated Reactome FI analysis tool which selects subnets with specific ontogenetic functions. The biological interaction network GTPase proteins included 202 proteins. Among the groups of proteins with specific ontogenic functions two groups are overexpressed in KO samples i.