dataesr/scientific-paragraphs-categorization huggingface dataset:
TOWARDS FULLY AND VERSATILE AUTOMATED CO-REGISTRATION Our co-registration method is based on incremental processing as image sets had to be merged among several iterations, with multiple users and/or at different time. As tie-points extraction and matching is the core and the main issue of photogrammetric based 2D/3D registration, our pipeline is initialized by an enhancement of 2D matching based on best neighbours pre-selection (see  Figure 15)  aiming to optimize the numbers and the quality features extracted. It is followed by a self-adaptive method for internal and external calibration fitting to the complexity of the dataset. A complete description of the "Totally Automated Co-Registration and Orientation" photogrammetric based workflow, called TACO is given in a complementary publication  (Pamart et al.
, 2019). The global co-registration processing dedicated to RTI is structured as follow (see Figure  1 ), a first iteration (Iter0) aims to achieve a first photogrammetric result. At the current stage of development, this initial 3D scene composed of calibrated/oriented pictures and a 3D point cloud is set as a master acquisition and stay frozen. Hence, it implies that the RTI must overlap with the area covered by CRP dataset initially processed (completion and/or densification of the scene is a planned future work). On next iterations (Iter1 and next), RTI master views are extracted, matched and spatialized, sequentially trying two methods. Matching is performed on the albedo image, produced by exploiting the inverse lighting capacities of RTI. It has been chosen, among other master images derived from RTI such as Mean or Median composites, since our tests produced better results in feature extraction and matching, even if we noted a potential issue with some radiometric deviation.
These two operations can easily integrated into a weighted linear summation, making the visualization client simple and efficient to implement. The quality/size ratio obtained in this way is always higher than the one of standard RTI techniques, such as PTM or HSH. Additionally, the appearance of highly specular material is well preserved at a cost of moderate file size (300-400 Kbytes). All the above presented results have been implemented in the Relight library (http://vcg. isti. cnr. it/relight/), that includes not only tools for creating a RTI image in any format, but also a Javascript library for web visualization. This initial code was modified to be integrated within the AIOLI framework, so that images are encoded in a tiled format, and refined progressively as soon as bandwidth is available.
CURRENT STATUS AND RESULTS OF THE IMPLEMENTATION IN THE AIOLI PLATFORM In addition to the methodological explanations above, this section intends to describe and clarify the technical aspects of SO-RTI stages and framework currently implemented in AIOLI. Thanks to its flexible architecture, we have been able to easily implement this RTI dedicated pipeline (presented in Figure  5 ). All of the processes are containerized thanks to Docker  (Merkel, 2014)  and are triggered by the server as every other AIOLI process pipeline. This allows us to benefit from the scalability, while using regular AIOLI communications to speak with the database and the project storing unit, thus not requiring to build a specific communication layer. The framework currently requires the upload of raw RTI images and the corresponding LP files (which include calibrated light direction data), to allow to control and automatize the entire process.
This includes the RTI encoding, which is implemented by avoiding a user-driven or explicit ball and highlight detection. To simplify the first implementation of the workflow, we focused our experiments on this kind of scenario. In the future, a metadata management system (i. e. group by EXIF) will help to define the best strategy for incremental processing using the optimum or combined BBA/SR approach. The main difficulty is the integration of unprocessed Highlight-Based RTI but thanks to the flexibility of the Relight library in term of supported fitters  (Ponchio et al. , 2018a) , importing processed data in RTI or PTM format will be an easy task. For further information on updates, next features releases or conditions to join AIOLI's the beta-testing program visit the website (http:www.
aioli. cloud/) The first results show a robust and versatile co-registration process but remains perfectible and still has to be tested on: i) reproducibility, complex dataset (scaling, spectral or temporal gaps and numbers of iterations); and ii) accuracy, checking by a necessary shift from relative to absolute coordinates with known or measured positions. Not surprisingly, the best results are obtained with ad-hoc combined acquisition or at least with fixed set-up contexts, diminishing global overlapping and matching issues. The 2D matching approach seems robust enough even considering some spatial or resolution gaps (e. g. distance, focal or sensor) solved by an automatic increase of image resolution to obtain optimize tie-points extraction used for both methods of spatial registration.
The integration has been tested on some real cases, showing how RTI can be easily integrated and help the analysis and annotation work by enabling the transfer of quantitative and qualitative properties. From this first outcome, there are two main research directions toward a so called high-level data fusion  (Ramos and Remondino, 2015)  that could start from the promising results presented in this paper: • The seamless integration of other multimodal data. In principle, any advanced image that could be encoded with an interpolation (i. e. time lapse images) could be directly included in the Relight library. Another challenge is represented by hyper-spectral images, which are becoming more and more important in multimodal acquisitions. In this case, similar issues to RTI have to be taken into account: coregistration with RGB images, data compression, interaction in the context of Aioli, and seamless remote navigation.
Nonetheless, the merging of multimodal CH dataset composed of photogrammetric and RTI acquisitions can be achieved through variable scenarios according to the type of data acquisition and set-up used for the respective technique. The first and optimal scenario consist of an ad-hoc combined data acquisition where the RTI have been acquired with an automated dome, thus using the same equipment (sensor and focal) in a fixed context (see data sets, results and illustrations of Tegulae, Manuscript and Vasarely). However, the RTI-to-CRP registration produced accurate results, even when slight changes in illumination occurred. As shown in Figure2related to the Skull dataset where a global lighting picture taken within the dome used as CRP-based reference and the RTI albedo added (from a fixed position) have a perfect spatial overlapping.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W9, 2019 8th Intl. Workshop 3D-ARCH "3D Virtual Reconstruction and Visualization of ComplexArchitectures", 6-8 February 2019, Bergamo, Italy Dataset CRP RTI Focal length Contexts Nb of pics Views and type range (mm) Set-up Venasque - 3 H-RT I 60 Diff. Tegulae 5 1 LP 24 Fixed Manuscript 5 1 LP 60 Fixed Vasarely 5 1 LP 35 Fixed Skull 6 2 LP 60 Fixed iSkull 38 2 LP 28 ||60 Diff. Bramante 23 + 33 2 LP 35 and 60 ||24 and 60 Diff. Melchior 50 17 H-RT I 105 ||85 Diff. Brea 29 + 39 5 LP 35 and 60 ||24 to 105 Fixed Hermes 43 + 185 3 H-RT I 35 and 60 ||60 Diff. Table 1. Composition of multimodal data sets tested respectively varying ; numbers of pictures, RTI views and techniques, range of focal length and acquisition contexts.
Statistical methods SAT and VAT on 30 subjects were obtained by two observers, (coded here as K and C). Each measurement was replicated 3 times, (with the exception of one occasion where observer K made just two replicate measurements of subcutaneous fat on a particular subject). Intra-observer precision was assessed using Coefficientof-Variation (Table  1 ). Inter-observer reliability was assessed using Coefficient-of-variation, and precision expressed as repeatability standard deviations (SD) and their respective coefficients, as well as Limits-of-Agreement. Graphical comparisons of the measurements obtained by the two observers were made using Bland-Altman plots showing unlinked replicates  [28]. These were generated using the method comparison studies R package MethComp  [29]  which determines the LoA by fitting a variance component model that assumes unlinked replicates  [30].
Results Intra-observer precision was assessed by co-efficient of variation. The data are presented in Table  1. Intraobserver precision was to an acceptable degree according to anthropometric criterion. Measurements of SAT Fig. 1  Example ultrasound screenshot image at correct position for measurement of SAT (1) and VAT  (2)  and total adipose for both observers was < 5% CV and level for VAT < 10% CV for both observers. Inter-observer precision and repeatability was also assessed by co-efficient of variation, with %CV of 3. 05, 7. 15 and 2. 95% for SAT, VAT and total adipose respectively. Inter-observer repeatability standard deviations and coefficients are presented in Table  2. Inter-observer reliability was also assessed by LoA. Determinations by each of the two observers was made in triplicate.
Acknowledgements We would like to thank the pregnant women at the antenatal clinic at UMHL for their active participation in the study and all the staff of the  Department of Ultrasonography  for their patience whilst data was collected. This research project was supported by the  University of Limerick  through an  Education & Health Sciences faculty Postgraduate Deans Scholarship ,  Graduate Entry Medical School Strategic Research Fund and Health Research Institute (HRI)  Seed Funding from the  University of Limerick.
Data collection, management and analysis The establishment of computer database and the necessary manipulations such as variable coding was performed using MS Excel (Microsoft® Excel® 2010, Microsoft Corporation; Santa Rosa, California, USA). The database was transferred to SPSS version 11. 5 (IBM Corporation, New York, USA)  [31]  for analysis. Descriptive statistics such as proportions, standard deviations and 95% confidence intervals were performed. Over all and sample-specific prevalence were determined by dividing the number of positive samples to the total number of samples examined. Difference among and between proportions of the groups with certain determinant factors was determined by chisquare (X 2 ) test. ORs were calculated using univariable logistic regression to determine the degree of associations of carcass contamination with fecal, skin and carcass in contact surfaces' status.
Acknowledgements We are extremely indebted to  Professor Dr. Bruno Goddeeris , from the  Catholic University of Leuven  who led the  VLI R-UOS  project, for facilitation of the overall research grant, continuous support and follow up. We thank the  Microbiology Laboratory, College of Veterinary Medicine and Agriculture, Addis Ababa University , for availing their laboratory facility and equipment. The authors also would like to extend their gratitude to the staff of the export of abattoir in Modjo for their collaboration during sampling.
Obtaining bird genetic data and ecological attributes We conducted a survey of studies on avian population genetic based on microsatellite data. We used the Web of Science (http://www. isiknowledge. com) database with the following combinations of keywords: From the publications, we retrieved the following data: (i) title; (ii) year of publication; (iii) journal; (iv) study species; (v) number of individuals; (vi) number of loci; and (vii) mean values of genetic diversity; H E , H O and f. In several studies, f was not estimated. Thus, we estimate it using the equation: f = 1 -(H O / H E ). We used the database of the IUCN Red List of Threatened Species (http://www. iucnredlist. org) to extract information about the conservation status of target species. We considered the following categories: Least Concern (LC), Near Threatened (NT), Vulnerable (VU), Endangered (EN), Critically Endangered (CR), Extinct in the Wild (EW) and Extinct (EX) as of December 2015 to July 2016.
The Birdlife International database (http://www. birdlife. org/datazone/species) contains data on avian species worldwide, and we used it to obtain the following information for each bird species in the selected studies: (i) migratory habit (migratory or or low dependence or not occurring in forests) and (iii) habitat type (terrestrial or other). This information, as well as the conservation status of the species (IUCN Category), was considered here as the ecological attributes.
Analyses were performed using MCMCglmm package  (Hadfield, 2010)  implemented in R version 3. 4. 4 (R Core Team, 2018). To account for phylogenetic non-independence of the effects of ecological attributes on genetic diversity, we first obtained the reference phylogenetic hypothesis of the species included in each analysis. We gathered ten thousand phylogenies sampled from a pseudo-posterior distribution  (Jetz et al. , 2012)  deposited in BirdTree. org website (https://birdtree. org/). We made a consensus tree using Tree annotator 1. 8. 2  (Drummond et al. , 2012)  and dropped all species without data using the 'drop. tip' function in the package 'ape'  (Paradis, 2004)  implemented in R version 3. 4. 4. We then tested whether the studied ecological attributes showed a phylogenetic signal to account for phylogenetic relationships.
We performed Abouheif's proximity test of serial independence  (Abouheif, 1999; Pavoinea et al. , 2008)  using the function 'abouheif. moran' from the R-package 'adephylo'  (Jombart et al. , 2010). We then fitted Phylogenetic Generalized Least Square Models (pGLS;  Martins and Hansen, 1997)  to the genetic parameters to verify whether GLMM models had resulted in robust inferences and hence the pattern persisted when accounting for phylogenetic relationships. We tested the solitary effect of each ecological attribute, as well as the effect of combining all of these in a complete model, on the genetic diversity parameters. When the same molecular marker was applied more than once to study a species the mean of the genetic parameters was used. The analyses were carried out using the package 'caper'  (Orme, 2013)  of the R.
Scientometrics The search revealed 359 published papers that met the criteria entered. They were published across 98 different journals between 1998 and 2015. Five of these journals (i. e. , Molecular Ecology Resources, Conservation Genetics Resources, Conservation Genetics, Molecular Ecology and PLOS One) hosted 51. 81 % of the articles; 35 journals published between four and eight articles, and the remaining 58 journals published only one article. The analysis of annual number of publications per journal revealed that 2014 was the year with the highest number of publications; 13 of the analysed journals published studies containing genetic diversity data of bird species. The second highest mean annual publication rate occurred in 2013 and in 2015; in each of these years, nine different journals published articles that analysed the genetic diversity of birds using microsatellite markers.
Cell spreading assay. CD cells were plated onto slides coated with LM-511 (0. 5 μg/ml) for 1h and treated with growth factors for 30 minutes after which they were fixed, permeabilized, exposed to rhodamine-phalloidin (1:5000) and visualized under a fluorescence microscope. Fifteen to 30 HPFs were analyzed using ImageJ software. Cell proliferation was assessed by measuring incorporation of 5-bromodeoxyuridine (BrdU) in an enzyme-linked immunosorbent assay-based 5-Bromo-2′-deoxy-uridine Labeling and Detection Kit III (Roche Applied Science, Indianapolis, IN) as previously described  [30]. BrdU incorporation was quantified by a change of absorbance (optical density) at 405 nm. Various cellular treatments were as described in Cell replating assay.
Statistical analyses. The mean and SEM of each treatment group were calculated for all experiments. At least 4 independent experiments (some in triplicates each) were performed. Student's t test was used to compare two groups. All statistical tests were two-sided and statistical analysis was done with the use of SigmaStat software (Systat Software Inc. , San Jose, CA). Statistical significance was defined as p less than or equal to 0. 05. kinase activity increases sensitivity to cisplatin, Clinical cancer research : an official journal of the American Association for Cancer Research 5(5) (1999) 1007-14. Itgα3 -/-α6 -/-CD cells do not express integrin subunits α3 and α6 and have less β1 and α2 than Itgα3 f/f α6 f/f cells as assessed by immunoblot (A) and FACS analyses (B). Adhesion (C), migration (D) and proliferation (E) of Itgα3 f/f α6 f/f and Itgα3 -/-α6 -/-CD cells on LM-332, LM-511 and Col I were evaluated at 1 (C), 4 (D) and 24 (E) h after plating.
Acknowledgements: This work was in part supported by  Veterans Affairs Merit Reviews   1I01BX002025-01  (AP) and  1I01BX002196-01  (RZ);  National Institutes of Health  grants  R01-CA162433  (AP),  R01-DK095761  (AP),  R01-DK069221  (RZ),  R01-DK083187  (R. Z).
Evaluation of tumour angiogenesis To evaluate the influence of calcitriol and its analogues on tumour angiogenesis, tumour perfusion was evaluated on day 24 by ultrasound imaging, as previously described  [27]. Briefly, ultrasound imaging was performed after MicroMarker TM Contrast Agent (VisualSonics, Canada) intravenous injection in anaesthetized animals placed on the handling station. The central section of the tumour was visualized. Signal of the contrast marker accumulating in the tumour was recorded using the probe at 13-24 MHz frequency (MS250, VisualSonics, Canada). Next, the peak-enhancement [Contrast Mean Power as l. a. u. ] parameter was calculated using Vevo LAB 1. 7. 1 Software (VisualSonics, Ontario, Canada).
Bone homeostasis Calcium level was measured in each sample of plasma using the Cobas c 111 z ISE device (Roche Diagnostics Ltd. , Rotkreuz, Switzerland). Radiographs of mouse bones were taken on day 28 after the administration of anaesthesia on a Kodak/ Carestream FX PRO Imaging System for a total of 3-6 young and old mice (for each group). Images were acquired using the X-ray source energy at 35 kVp with an X-ray calibrated energy of ~13 keV. Binning was set at 2 X & 2 Y with a 0. 4 mm aluminium filter, an ƒ-stop of 5. 57, an exposure time of 2 min, and FOV = 200, which provided sufficient grey scale values for acquiring an image quality suitable for bone mineral density measurements. Images were processed using Bruker Molecular Imaging software v. 7. 1. 3. 20550 with the Bruker Bone Density Software Module by quantifying pixels after selecting the femur regions of interest  [40, 41].
Media were changed every other day, and cultures were maintained for 7 days. After this, the stain for active osteoclasts was prepared using an Acid Phosphatase Leukocyte (TRAP) Kit according to manufacturer's instructions. Microscopic examination and photographs were captured using a bright field microscope (Olympus IX81) connected to a camera equipped with Olympus Stream Image Analysis software (Olympus Europe Holding GmBH, Hamburg, Germany). Blinded microscopic examination was performed at 10× magnification, and photographs were taken at 4× magnification. Characterized by stained acid phosphatase (dark purple colour visible in the cytoplasm of cells) and numerous cell nuclei (>5), active osteoclasts were counted over the entire surface of the well. The percentage of active osteoclasts in the study groups was compared to the values obtained for the control group.
Acknowledgements This project was funded by the National Science Center granted on the basis of decision number  DEC-2013/11/B/NZ5/00162. Publication was supported by the  Wroclaw Center of Biotechnology  within a Program-The Leading National Research Center (KNOW) for years 2014-2018. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Paired-end sequences were stitched, and the resulting ∼300 bp fragments were filtered using QIIME 1. 9. 1  (Caporaso et al. , 2010) , with chimeras removed using UCHIME. The cleaned sequences were clustered into OTUs at a distance of 0. 03, assigned using the Greengenes database  (McDonald et al. , 2012) , and filtered to remove Archaea, mitochondria, and chloroplast OTUs. To enable comparison between samples, sequences were randomly resampled  (Lozupone et al. , 2011)  from the filtered OTU table to the same depth as the sample with the fewest bacterial sequences (6,751 reads) using Mersenne twister PRNG (QIIME 1. 9. 1). All further analyses were performed on the resulting rarefied OTU table. For multivariate statistical analyses, OTU abundance was averaged between triplicates.
Bacterial Community Analyses Non-parametric species richness estimator Chao1 and Shannon diversity index were calculated using the R package phyloseq  (McMurdie and Holmes, 2013). Both indices were used because they capture different aspects of community composition. Chao1 is based on relative abundance, thus it primarily measures richness, while the Shannon index considers both richness and evenness. Principal Coordinate analyses (PCoAs; R package phyloseq) based on Bray-Curtis similarity were generated for the bacterial communities of each phytoplankton species, and for all species together. Significant bacterial clusters were identified in the multi-species PCoA via an analysis of similarities (ANOSIM; R package vegan). Similarity percentage (SIMPER, 999 iterations, R package vegan) was then used to determine which individual OTUs contributed most to the dissimilarity between these significant bacterial clusters.
Indicator Species analysis (R package indicspecies;  Cáceres et al. , 2010)  was used to identify the OTUs significantly correlated with significant clusters identified in the single-species PCoA via ANOSIM. The results were visualized as networks with the igraph R package  (Csárdi and Nepusz, 2006). Canonical Correspondence Analysis (CCA) was performed with the R package vegan to investigate the parameters influencing the bacterial community structures: phytoplankton growth rate and photophysiology, phytoplankton exudates (aminopeptidase and glucosidase activities), or the different treatments (oil and/or dispersant and lipase activity). These parameters were transformed according to their pairwise distributions. Prior to CCA, Spearman's rank pairwise correlations were calculated between all transformed variables for each culture separately and all together.
As the presence of correlated variables in the same CCA would lead to superimposed arrows, only one variable was selected and the covariables were removed from the analyses to get a clearer picture  (Ter Braak and Smilauer, 2002). A summary of the correlated variables and the corresponding selected one for the CCAs is in Table  S4. A forward selection was then conducted (R package vegan) to select the significant non-correlated variables (p-values < 0. 05), while maximizing the explained variation.
Statistical Analysis of Physiological Parameters ANOVA (R package stats) were performed on X and AX phytoplankton growth rates, the averaged X and AX photosynthetic parameters, and on the averaged FL and PA extracellular enzyme activities to determine the significance of each treatment compared to the control. Dunnett's post-hoc tests were then conducted between the control and each treatment. In each treatment, differences between X and AX phytoplankton   S1. parameters (growth rate and photophysiology), and between FL and PA bacterial extracellular enzyme activities were analyzed using student tests. The photosynthetic parameters Fv/Fm, SigmaPSII, and potential rETRmax were averaged over the time to simplify data visualization and because the time was not a significant variable (p-values > 0.