dataesr/scientific-paragraphs-categorization huggingface dataset:
A frequency change confirmed the biotin-avidin recognition event (Figure  5 )  [60, 61]. The avidin immobilization showed high reproducibility: ∆F values ≈ 115 Hz were measured in several experiments carried out on different crystals (data not shown). After avidin immobilization, the crystals were rinsed with the washing solution, and subsequently 20 µL of 25 µg/mL BSA solution were added. The addition of BSA solution did not determine any frequency shift, thus indicating the complete surface coverage and the presence of a homogeneous layer of avidin. Biotinylated anti-CD34 antibodies were then added to the flow cell and their immobilization was ascertained by a frequency shift of 70 Hz; saturation was reached after 20 min. After antibody immobilization, a solution of His-tagged-CD34 protein (20 µL, 25 µg/mL) was added and a frequency shift of 60 Hz was observed.
Acknowledgments: We wish to thank  Gianpaolo Papaccio  and  Virginia Tirino , from providing us with dental pulp stem cells, and osteogenic differentiation experiments. Carla Manfredi  is kindly acknowledged for her support with ATR-FTIR data collection, as are  Fabio Pirro  for critically reading the manuscript and  Fabrizia Sibillo  for technical assistance. This work was financed by  EURO TRANS-BIO Programme  ( MSE  Project number  E01/0785/02/X15 - BIOSTECELL-Biosensor for Stem Cells detection and extraction ) and by  PRIN Programme 2015  (MIUR, Project number  2015RNWJAM-Nanoplatforms  for enhanced immune responses).
Phantom experiment The motion estimates provided by the proposed framework were initially validated on a dataset acquired during a phantom study. Motion estimation algorithms typically rely on structural information in order to estimate displacements, having to interpolate/extrapolate/infer motion in more homogenous regions. For this reason, the phantom consisted of a set of two grid-like structures (positioned in the coronal and the sagittal plane) placed inside a cylindrical plastic casing. The casing itself was then filled with a polymer gel and one of the bases was provided with an acoustic-transparent mylar membrane. Figure  3 (a) and (b) illustrate a coronal and a sagittal slice of a T1-weighted 3D MR image acquired on the phantom, showcasing the two integrated structures surrounded by the polymer gel.
Hardware and implementation A multi-threaded (8 threads) C++ implementation was performed for all registration algorithms included by the proposed motion correction framework. The data resulting from the healthy volunteer study and the phantom experiment was processed in retrospect, with motion estimation and analysis being conducted on an Intel 3. 2 GHz i7 workstation (8 cores) with 16 GB of RAM. During the animal experiments, the calculations associated to the proposed framework were offloaded on a dedicated custom-build node with 32 cores and 64 GB of RAM. The implementation was performed as an additional module directly into the clinical software dedicated to the interventional radiologist delivering the therapy. The node together with access to the clinical software code was provided by Philips Healthcare, Vantaa, Finland.
Validation of the multi-modal registration algorithm A validation of the motion estimates provided by the MIND multi-modal registration algorithm was also performed on the phantom dataset. However, instead of registering the non-distorted EPI image to its preceding 3D anatomical anchor, it was registered directly to the 3D reference anchor (see Fig. 2  for reference). The estimated motion vectors were then compared in terms of the EE to the drifts induced in the motion pattern. The spatial distribution of the temporally averaged EE maps is displayed in Fig. 5 (a) and (b) for a coronal and a sagittal slice respectively. It can be observed that the EE remain below the in-plane voxel size (<2. 5 mm). This is further confirmed by analyzing their statistical distribution illustrated in Fig. 5(c ).
Acknowledgements The authors would like to thank  Johanna M. M. van Breugel  ( Dept. of Radiology, UMC Utrecht, Netherlands ),  Gerald Schubert  ( Philips Healthcare, Vantaa, Finland ) and  Martijn de Greef  ( TomTom, Eindhoven, Netherlands ) for their support during the animal experiments. The authors also gratefully acknowledge the two anonymous referees whose remarks and suggestions have helped us greatly in improving the quality of the manuscript.
In this paper, GMNS considers both minor and principal subspace estimation, whereas MNS only considers minor subspace estimation. The potential of GMNS are two-fold. First, GMNS provides a framework that allows the estimation of both minor and principal subspaces in parallel. Second, it can be used to fuse data from several data sources. In the practical application on RFI mitigation studied in Section VII, it will be shown that it is difficult or expensive to directly estimate the "global" principal subspace from the available data covariances of several stations. On another note, GMNS has recently been applied to parallelizable tensor decomposition  [21]. The rest of the paper is organized as follows. The MNS concept and its implementation are briefly reviewed in Section II. The GMNS concept and the GMNS-based batch algorithms for MSA and PSA/PCA are proposed in Sections III and IV, respectively.
C. Minor Subspace Tracking For MST, we choose two low-cost algorithms, FOOja  [24]  and FDPM  [11] , and one moderate-cost one, YAST (yet another subspace tracking)  [26] , and compare them with the corresponding GMNS-based algorithms: GMNS-MST-FOOja, GMNS-MST-FDPM and GMNS-MST-YAST. All algorithms run in a noisy environment with SNR = 15 dB. The performance results with respect to p = 2 and p = 6 are shown in Figure  8 , respectively. Interestingly, the performance of GMNS-MST-FOOja is better than FOOja even though its convergence rate is slower. The reason is that dividing data into small subsystems reduces the search space which then mitigates the local minima convergence problem and enhances the overall performance. An analogous observation can be seen for GMNS-MST-FDPM and FDPM. Better convergence rate and estimation accuracy are obtained by both YAST  7  and GMNS-MST-YAST but at the expense of higher computational complexity.
VII. APPLICATION TO RFI MITIGATION IN RADIO ASTRONOMY Now, we consider RFI mitigation which is a challenging problem in radio astronomy  [27]. In general, RFI is difficult to void even with the spectrum being protected and the deployed area being relatively remote. The interference sources in radio astronomy can stem from various man-made wireless services such as mobile cellular telephone, global positioning system satellites, digital audio and video broadcasting, and so on. The effect of RFI can be observed in Figure  11 (b). In this example, the dataset comes from a single LOFAR station with n = 48 antennas, as shown in Figure  11(a). Usually, those 48 antenna outputs are beamformed in real time and the corresponding signal is sent to a central correlator for further radio astronomical processing with other remote stations.
In our experiment, a 5. 2 ms signal of those 48 antennas has been stored on disk, and a 2 MHz band around a specific terrestrial RFI (a land mobile at 55 MHz) has been selected to produce a 48×48 covariance matrix. The different sky images presented in this section are derived from this matrix. In Figure  11 (b), while signals-of-interest (SOIs) are not really well-defined, a "strong" RFI appears at the horizon. This image with distortions and artifacts is referred to as "dirty image". RFI can lead to distorted data and unwanted artifacts, causing difficulty in astronomical observation. To mitigate RFI, an efficient method is based on projection in which a key step is subspace estimation. However, as stated in the introduction, SVD-or EVD-based subspace estimation methods are quite expensive in terms of computational complexity.
Here we will illustrate how we can apply the proposed GMNS-based methods to tackle this problem while still preserving the imaging quality. The model under consideration can be presented as  [28]  x (t) = A c c(t) + A r r(t) + n(t), (49) where A c ∈ C n×m is the cosmic source spatial signature matrix, c ∈ C m is the cosmic source signal vector, A r ∈ C n×p is the interference spatial signature matrix, r(t) ∈ C p is the RFI signal vector, and n(t) ∈ C n is the additive white noise vector with unknown variance σ 2. Hence, we can estimate the data covariance matrix as R xx = A c R cc A H c + A r R rr A H r + σ 2 I, (50) where R cc and R rr are cosmic and RFI covariance matrices, respectively. Here we have assumed that the cosmic sources, the RFIs and the system noise are uncorrelated. The cosmic sources are point sources because of relative distance between the source and the instrument.
A subspace of dimension 48 × 2, corresponding to two principal eigenvalues, is selected. The Milky Way as well as Cassiopeia A and Cygnus A are now strongly visible and the land mobile signal has been mitigated. 2) Qualitative comparison: In Section VI, we have compared the subspace estimation accuracy between the proposed algorithms and the corresponding SVD-based algorithms quantitatively via numerical simulation. Now we conduct a qualitative experiment using real-life data for further evaluation. We replace the principal subspace in (51), estimated by the standard SVD-PSA method, with the principal subspace estimated by our GMNS-PSA method and build skymaps after RFI mitigation. Again, the subspace corresponding to two principal eigenvalues defines the RFI subspace (p = 2). As can be seen from Figures  11(d )-(f), SOIs are enhanced while RFIs are removed (only the strongest RFI is circled in these figures).
From those figures, we can see that the imaging quality based on GMNS-PSA is comparable with that of SVD-PSA. Again, our main advantage is the fact that the cost is reduced by a factor of K 2 compared to SVD-PSA. While this experiment is based on a significant but still limited number of antennas, our method is potential when massive data like in the SKA project  [7]  need to be processed or when data from some stations need to be fused.
VIII. CONCLUSIONS In this paper, we have proposed a simple but efficient approach for estimation and tracking of the signal and noise subspaces. The different problems considered in this paper are quite common in many array processing applications and are known as the most expensive tasks in source localization and separation to the extent that many efficient spatial filtering methods have been disregarded in real-life applications which use large antenna arrays, e. g. , RFI mitigation in radio astronomy  [27] ,  [28]   9. Our GMNS solution exploits the specific array processing model together with a parallel computing architecture to reduce the overall cost by a factor close to K 2 , where K is the number of parallel computing units, for large dimensional systems. At the same time, it can be used to fuse data from a number of data sources.
elegans  17, 18  , Drosophila  [19] [20] [21] [22]  and mice  23. The molecular functions of NPRL2, NPRL3, and their orthologues remain under explored. Both proteins exhibit similar structures and may be paralogues. At their N-termini they contain a longin domain, which is often found in guanine nucleotide exchange factors (GEFs); however GEF activity has not yet been demonstrated for these proteins  24, 25. At their C-terminus NPRL2 and NPRL3 contain two or more winged helix-turn-helix (HTH) domains, which facilitate interactions with DNA. Notably, NPRL2 and NPRL3 are typically maintained at the very low levels within cells. For example, protein expression level of NPRL2 is between 100 and 10000 times lower than GAPDH (depending on a tissue), as annotated by Human Integrated Protein Expression Database (HIPED)  26  (http://www.
Cycloheximide treatment induces mTORC1 activation and inhibits autophagy during nitrogen starvation  33, 34. The canonical model suggests that CHX causes mTORC1 activation through the accumulation of intracellular amino acids, resulting from inhibition of the protein synthesis. However, this might not be the only explanation. Our data show that CHX treatment greatly reduces the amount of the mTORC1 inhibitor NPRL2, resulting in mTORC1 reactivation. Indeed, when we checked mTORC1 activity by monitoring the level of p70 phosphorylation, we observed that declining of the NPRL2 level caused by CHX treatment was correlated with the increase of mTORC1 activity (Supplementary Fig. S1a ). Inversely, overexpression of NPRL2 reduces mTORC1 activity (Supplementary Fig. S1b ). We further verified whether NPRL2 degradation was mediated by proteasome, by treating the cells with a proteasome inhibitor, MG132 (Fig.
Components of    (c, d ) Classification of proteins co-purified with NPRL2 according to their localization (c) and biological process (d) was done by Gene Ontology Database. (d) Protein interaction networks of GATOR1 complex. The interaction networks between the GATOR1 complex and selected proteins from NPRL2 interactome known to be involved in the processes of DNA damage response (Supplementary Table  S3 ) was done using protein interaction information from the STRING database (https://string-db. org/). GATOR2 complex were also present in the IP (Supplementary Table  S3 ), but in smaller amounts. This is consistent with previous observations that GATOR1 and GATOR2 associate weakly with one another in mammalian cells, in contrast to their yeast counterparts from the SEA complex  3, 13. We did not detect any components of the mTORC1 complex and only very few lysosomal proteins.
The raw files were processed with MaxQuant (version 1. 5. 8. 3) and searched against the Uniprot Human protein database (158,090 entries in total). The following parameters were used for database searches: cysteine carbamido methylation was selected as a fixed modification; methionine oxidation, protein N-terminal acetylation, and phosphorylation on serine, threonine and tyrosine were selected as variable modifications. Up to two missing cleavage points were allowed. The precursor ion mass tolerances were 7 ppm, and fragment ion mass tolerance was 0. 5 Da for MS/MS spectra. The false discovery rate (FDR) was set to < 1. 0% for both peptide and protein identifications, the minimum peptide length was set to 6. The value for the label-free quantification (LFQ) intensity minimum ratio was set to 2.
To retain high confidence interacting partners of NPRL2 and NPRL3 we excluded proteins, which were identified only in one replica and proteins, which were detected with 1-2 peptides. For the generation of the heat map by Perseus (version 1. 5. 8. 5) (http://www. perseus-framework. org/), LFQ intensities were log 2 transformed and z-score normalized before hierarchical clustering (Fig. 2b ). Proteins were analyzed by Gene Ontology Database to get the information about cellular localization and function of GATOR1 interactors. Transfection. 200,000 cells/well were plated in 12-well plate in complete DMEM medium. One day later when cells reached 50-70% confluence they were transfected with appropriate plasmid using the jetPEI reagent following manufacturer's protocol. Transfection was carried out for 24 h.
For the analysis of ROS production by DHE staining cells were treated as above, and after PFA was removed, cells were washed with PBS, and 1 ml of 20 μM DHE was added to on a slide. Cells were incubated at the room temperature with rotation during 5 min in the dark. DHE was removed, cells rinsed once with PBS. Coverslips were mounted on a glass slides after Fluoroshield Mounting Medium with DAPI was added to the cells. For DHE detection images were observed at wavelengths range between 520 and 610 nm. For DAPI, GFP and Alexia Flour detection, images were observed in DAPI, 488 and 532 channels respectively. Images were acquired on a Confocal Leica SPE DM4000B microscope with 63x oil-immersion objective. Image intensities were adjusted and quantifications were carried out with LAS X software.
Acknowledgements Dr. Vassetzky  for critical reading of manuscript;  Professors M. P. Rout  and  B. T. Chait  for their generous collaborative support;  Mrs. H. Jiang  and  Ms. K. R. Molly  for excellent technical support in protein affinity purification and mass-spectrometry analyses;  Dr. Fujita ,  Dr. Collavin  and  Dr. Dupuy  for sharing plasmids and oligos. Y. M. is grateful to the support from  Chinese Scholarship Council (CSC)  fellowship. L. S. is grateful for the support from  Fondation pour la Recherche Médicale  and  Fondation Gustave Roussy  fellowships. This work was supported by grants from  La Ligue Nationale contre le Cancer (Comité de Paris) ,  Fondation de France ,  Fondation pour la Recherche Medicale, PICS CNRS  to S. D. ;  NIH  grants  P41 GM109824 ,  P41 GM103314 , and  P50 GM107632.
SANS SANS measurements were performed on the D22 SANS instrument at the Institut Laue Langevin (ILL) in Grenoble, France, and on the NG7 30 m SANS instrument at the National Institute of Standards and Technology Center for Neutron Research (NIST-CNR) in Gaithersburg, MD. Vesicle characterization was obtained by taking data over a broad Q-range: 0. 003 Å 1 < Q < 0. 6 Å.
1. For kinetic measurements, a single instrument configuration covering only one order in magnitude in Q (range between 0. 005 Å 1 < Q < 0. 05 Å 1 ) is used. Here Q is the magnitude of the momentum transfer vector, given by Q = 4 sin(/2)/, where  is the scattering angle and  is the neutron wavelength. The wavelength used was 6 Å. To increase the neutron flux, the wavelength spread was set to / = 0. 22 and the collimation length was increased to 10 m to reach 0. 005 Å 1 with a higher beam intensity on the NG7 SANS instrument. Data was collected on a 2D detector and the data was reduced using the reduction packages provided by NIST-CNR and ILL.
Objectives: General health examinations (GHE) have become an increasingly common measure for preventive medicine in Vietnam. However, little is known about the factors among Vietnamese people who attend or miss GHE. Budget or time constraints remain to be evaluated for better-informed policy making. This study investigates factors affecting behaviors in attending periodic GHE. The main objectives are as follows: (1) to explore empirical relationships between influencing factors and periodic GHE frequencies, and (2) to predict the probabilities of attending GHE under associated conditions. Methods: The study used a 2,068-observational dataset, obtained from a Vietnamese survey in 2016. The analysis was then performed using the methods of baseline-category logits for establishing relationships between predictor and response variables.
MATERIALS AND METHODS The data, consisting of 2,068 observations, have been collected from surveys at clinics, schools, companies, and households in Hanoi and nearby provinces, which were conducted during September to October 2016. The investigation was a random selection of individuals (i. e. , not chosen based on any criteria). Vuong & Associates (V&A) is the main responsible of data collecting, with ethical standards stated in the surveying institution's Decision V&A/07/2016 dated September 15, 2016. The data team directly gathers signed questionnaires with consent and cooperation from participants. The questionnaires were then checked and signed off by a team member, supervising person, head of V&A and principal researcher.
ACKNOWLEDGMENTS The authors would like to thank several people at  Vuong & Associates and Quang-Vu-Hung Hoi (Foundation)  for assistance in collecting and preparing the dataset for this research, particularly  Dam Thu Ha ,  Do Thu Hang ,  Nguyen Thi Phuong ,  Nghiem Phu Kien Cuong , and  Tran Thanh Tung. Special thanks are extended to the thousands of respondents participating in this survey, and especially  Dang Tran Dung ,  CEO of Hospital 125 Thai Thinh (Hanoi) , for his heartfelt support during the research process.
Essential Biodiversity Variables (EBVs) consolidate information from varied biodiversity observation sources. Here we demonstrate the links between data sources, EBVs and indicators and discuss how different sources of biodiversity observations can be harnessed to inform EBVs. We classify sources of primary observations into four types: extensive and intensive monitoring schemes, ecological field studies and satellite remote sensing. We characterize their geographic, taxonomic and temporal coverage. Ecological field studies and intensive monitoring schemes inform a wide range of EBVs, but the former tend to deliver short-term data, while the geographic coverage of the latter is limited. In contrast, extensive monitoring schemes mostly inform the population abundance EBV, but deliver long-term data across an extensive network of sites.
Satellite remote sensing is particularly suited to providing information on ecosystem function and structure EBVs. Biases behind data sources may affect the representativeness of global biodiversity datasets. To improve them, researchers must assess data sources and then develop strategies to compensate for identified gaps. We draw on the population abundance dataset informing the Living Planet Index (LPI) to illustrate the effects of data sources on EBV representativeness. We find that long-term monitoring schemes informing the LPI are still scarce outside of Europe and North America and that ecological field studies play a key role in covering that gap. Achieving representative EBV datasets will depend both on the ability to integrate available data, through data harmonization and modeling efforts, and on the establishment of new monitoring programs to address critical data gaps.
EBVs are state variables that stand between primary observations (i. e. , raw data) and high level indicators (e. g. , the Living Planet Index  (Collen et al. , 2009) ), and may represent essential aspects of biodiversity (from genetic composition to ecosystem functioning) or may be integrated with other EBVs or with other types of data, such as data on drivers and pressures, to deliver high-level indicators  (Pereira et al. , 2013; GEO BON 2015a). The aim of the EBV framework is to identify a minimum set of variables that can be used to inform scientists, managers and the public on global biodiversity change. In a first attempt to identify a minimum set of EBVs, GEO BON aggregated candidate variables into six classes: "genetic composition," "species populations," "species traits," "community composition," "ecosystem structure," and "ecosystem function"  (Pereira et al.
, 2013). Recently,  Geijzendorffer et al. (2015)  compared the EBV framework with indicators currently used for reporting biodiversity information by seven biodiversity policy instruments. They found that the current suite of biodiversity indicators does not incorporate EBV classes equally. For instance, some EBV classes, like "species populations," were well represented in current indicators, while others, like "genetic composition," were not. This asymmetry in EBV coverage is related to biases in indicator selection, and ultimately to biases in extant biodiversity monitoring data, as indicator selection is often driven by data availability for reasons of feasibility  (Geijzendorffer et al. , 2015). Hence, the current set of indicators misses important biodiversity facets, due to gaps in primary data.
Instead, monitoring efforts should be driven by the information needs of selected indicators. The EBV framework could become an important tool towards that end, by promoting costefficient approaches  (Pereira et al. 2013, Fig. 1). This article aims to discuss how primary data sources affect the representativeness of current EBV datasets. That is, if available, are primary data well distributed across spatial and temporal scales of interest to provide meaningful measures on biodiversity change? Do data cover a diverse range of species groups? Previous studies have identified the existence of geographic and taxonomic biases in data availability (e. g. ,  Boakes et al. , 2010 , Pereira et al. , 2010; Martin et al. , 2012; Hudson et al. , 2014; Velasco et al. , 2015). Data asymmetries will be a barrier to effective policy responses  (Pereira et al.