dataesr/scientific-paragraphs-categorization huggingface dataset:
In terms of storage, EMPIAR relies on the EMBL-EBI object store which in technical terms is well scalable to the exabyte range. The cryo-EM community has benefitted greatly from this hot storage, making it possible to download tens of terabytes of data at the click of a button. However, the storage and IT infrastructure is funded by the UK government and the UK Research and Innovation Strategic Priorities Fund (UKRI SPF) data infrastructure program and sustained future investment relies on our ability to justify that this is money well spent. We are therefore considering adding metadata to EMPIAR entries to track lifecycle events, e. g. , date of last access, number of downloads etc. With this information it will be possible to better manage storage of the data, for instance by allowing data that is older and rarely accessed to be moved onto "colder" but cheaper storage.
The large-scale vEM projects mentioned above tend to have significant resources to host and provide access to their data long term. In a 2021 EMPIAR Volume-EM workshop it was proposed that for such large datasets, rather than duplicating the data in the centralised store, EMPIAR could maintain links to the project site and only store relevant metadata and possibly a final condensed version of the 3D reconstruction that could be used for viewing purposes. In addition to increased storage, an increase in the number of depositions will also pose challenges in terms of the curation of the data. We have a collaboration with PDBj (Osaka, Japan) who maintain a mirror of EMPIAR (https://empiar. pdbj. org/) and who are also data brokers for Japanese depositions, i. e. , they assist Japanese depositors in preparing depositions which are then pushed to EMPIAR.
As EMPIAR continues to grow, it may be necessary to consider further collaborations with regional partners in order to have a sustainable solution for the public archiving of EM data. A FAIR analysis of EMPIAR data   (38)  and it is useful to gauge where EMPIAR stands with respect to these aims. Every EMPIAR entry has a persistent identifier and meta-data indexed in a searchable resource, which is needed to make the data findable. As discussed earlier, work is underway to substantially enhance the richness of the metadata and this should further improve the findability of the data. All EMPIAR data is freely and openly accessible via a range of download options provided at no cost to the user. However, the very size of these datasets may hinder accessibility and technological solutions (such as data compression or the ability to download parts of datasets at the desired sampling) may be required to make the data accessible to a wider range of users.
The formal specification of the EMPIAR metadata model as an XML schema aids interoperability. This is further enhanced by the initiatives in place for sharing and linking information with EMDB and the BioImage Archive, the incorporation of relevant references to these archives and the eventual use of REMBI to underpin the vEM data model. Despite the numerous examples of reuse of EMPIAR data, we know anecdotally that the lack of sufficient metadata describing the data is impeding wider reuse of EMPIAR, e. g. , for automated reprocessing of datasets and as training data for machine learning approaches. In summary, improving the richness of the metadata description of the data is fundamentally important to maintain and increase the relevance and usefulness of the archive and is therefore a key focus of future development.
Illustrative example (https://www. ebi. ac. uk/empiar/volumebrowser/empiar_10087_e64_tomo03) of an entry in the Volume Browser showing images overlaid with annotated segmentations. Textual annotations are provided in the panel on the right, which displays annotations for the currently selected segment (purple border in the main image panel, indicated by the red arrow). 4. A collage of EMPIAR social media highlights.
Weekly tweets focus on particularly interesting entries ranging from biologically important proteins, protein complexes or cells to datasets supporting methods development or obtained with new techniques. Other tweets include announcements of new EMPIAR developments, milestones or job opportunities, and upcoming conferences, courses and workshops. EMDB and EMPIAR share a YouTube channel (https://bit. ly/emchan), which is mostly dedicated to tutorials on how to use the archive, recorded public talks about the resource, featured entries, and highlights of new features.
Acknowledgements We thank  José Salavert-Torres  and  Carlos Lugo Vélez  for their contributions to the early stages of development of EMPIAR. We thank  Matthew Hartley  ( BioImage Archive Team Leader ;  EMBL-EBI ),  Mary Barlow  ( Head of Campus Ops & Capital Projects ;  EMBL-EBI ),  Andy Cafferkey  ( Technical Services Team Leader ;  EMBL-EBI ) and their respective teams for their help and collaboration in building up and maintaining EMPIAR, the  BioImage Archive  and the underpinning IT infrastructure. We thank  Prof. Genji Kurisu  and the  PDBj team  for the collaboration in developing EMPIAR-PDBj. Finally, we would like to thank the very many members of the  EM community  who have enthusiastically deposited data, participated in workshops and provided help, advice, and feedback.
Funding Work on EMPIAR was funded from 2014 to 2021 by two project grants awarded to EMBL-EBI by the  Medical Research Council (MRC)  and the  Biotechnology and Biological Sciences Research Council (BBSRC)  [ MR/L007835/1 ,  MR/P019544/1 ]. From 2021, EMPIAR benefits from funding from the  Wellcome Trust  [ 221371/Z/20/Z ]. EMPIAR is further supported by  EMBL  with funding from its member states.
3. 3 Illustrative example (https://www. ebi. ac. uk/empiar/volumebrowser/empiar_10087_e64_tomo03) of an entry in the Volume Browser showing images overlaid with annotated segmentations. Textual annotations are provided in the panel on the right, which displays annotations for the currently selected segment (purple border in the main image panel, indicated by the red arrow). 4. A collage of EMPIAR social media highlights.
Weekly tweets focus on particularly interesting entries ranging from biologically important proteins, protein complexes or cells to datasets supporting methods development or obtained with new techniques. Other tweets include announcements of new EMPIAR developments, milestones or job opportunities, and upcoming conferences, courses and workshops. EMDB and EMPIAR share a YouTube channel (https://bit. ly/emchan), which is mostly dedicated to tutorials on how to use the archive, recorded public talks about the resource, featured entries, and highlights of new features. An excellent introduction to EMPIAR and the resources available is provided by the EMPIAR Quick Tour (https://www. ebi. ac. uk/training/online/courses/empiar-quick-tour/). For those wishing to delve deeper, especially about depositing data, we recommend the EMPIAR Policies and Processing Procedures document (https://www.
ebi. ac. uk/empiar/policies/) and the deposition manual (https://www. ebi. ac. uk/empiar/deposition/manual). The EMPIAR team regularly provide training at courses and workshops and if possible recordings from these are made available on our tutorials page (https://www. ebi. ac. uk/empiar/tutorials/). We use Twitter (https://twitter. com/EMDB_EMPIAR) to reach out to (potential) users of EMPIAR, Figure4.
The proposed framework extends the method described in  [32]  in several ways. It first adds spatial scalability based on a single image super-resolution approach which is shown to give a very high rate-distortion performance for each target spatial resolution. The flexibility of the encoding structure has been increased by adding spatial scalability in addition to the viewport and quality scalabilities. This increased flexibility allows us to better address the various trade-offs between encoding efficiency, random access, and the different forms of scalability. A comprehensive analysis is carried out using a light field dataset with a large parallax which is more challenging in terms of encoding efficiency as well as low parallax light fields. In a nutshell, we first downscale light field views to a lower resolution to make two spatial layers: (i) Spatial Layer 1 (SL 1 ) and (ii) Spatial Layer 2 (SL 2 ).
A. Test Condition To evaluate the performance of the proposed method, we have selected six light fields from the Stanford 1 dataset  [39]  and three light fields from the JPEG Pleno 2 dataset  [38]  to cover light fields from large to narrow parallaxes. The characteristics of these images are summarized in Table. I. The Stanford light field views were converted to 8-bits YUV420 format and the JPEG Pleno light field views were converted to 10-bits YUV444 format to match the coding conditions of the baseline codecs selected for comparison. VTM Encoder Version 10. 2 3 , was used as the standard encoding software for VVC. We encode light fields at four quality levels. The base QPs used to encode each light field test image at four quality levels are also summarized in Table. I. QPoffsets for each viewport layer are selected in a way that the quality of encoded views remains similar to each other.
B. Compression Efficiency and Quality Distribution To evaluate the compression efficiency of the proposed method, we consider three points in its workflow: (i) SL 1 : the compression efficiency of the first spatial resolution after applying the bicubic upsampling, (ii) SL 1 + SR: the compression efficiency of the first spatial resolution after applying super-resolution, and (iii) SL 2 : the compression efficiency of the overall proposed method. We compare the encoding efficiency of these three points with the JPEG Pleno anchor (x265)  [38] , MV-HEVC  [51] , and Shearlet Transform Based Prediction (STBP) approach  [21]  for Stanford light fields, and with the JPEG Pleno Verification model 2. 1 (4D Prediction) (VM2. 1) [38]  for JPEG Pleno light fields. Note that different baseline codecs have been selected for each dataset since they perform differently on each of them.
VM2. 1 performs well on the JPEG Pleno dataset, which mainly includes light fields with a narrow disparity. However, it does not perform well for large disparity light fields such as those of the Stanford dataset. On the other hand, STBP, which is based on MV-HEVC, provides limited compression efficiency for narrow disparity light fields  [21]. Fig. 8  shows the RD curves using the mean PSNR of the Y component of all the views as the objective metric. For the Eucalyptus F lower light field, which has lots of fine geometry, the proposed method fails to outperform the state-of-the-art scheme. This might happen because of the inefficiency of video frame interpolation or super-resolution DNNs for these images or the lack of this type of image in their training dataset. For other light fields the proposed method (SL 2 ) shows superior performance compared to its competitors, particularly at lower bitrates.
C. Scalability In this paper, to support spatial scalability, the light fields are compressed at two spatial resolutions. Therefore, the final bitstream consists of two parts: (i) b SL1 : the bits allocated to compress the lowest resolution, and (i) b SL2 the bits allocated to compress the highest resolution. The allocated bits to each spatial layer are also divided into multiple viewport layers (i. e. , {b V L1 ,. , b V Ln }) to support viewport scalability and uniform quality distribution. Finally, the allocated bits to each viewport layer are used to improve the quality of viewports in that Fig. 8 : RD curves for light field test images of the Stanford dataset  [39]  and the JPEG Pleno dataset  [38]. SL 1 represents the first spatial resolution after applying bicubic upsampling, SL1 + SR represents the first spatial resolution after applying super-resolution, and SL 2 represents the compression efficiency of the overall proposed method.
E. Error Resiliency Compressed data is always vulnerable to channel errors and bandwidth constraints. However, our proposed method can synthesize all views even with a small portion of the whole bitstream, i. e. , b SL1V L1 and b SL1V L2. When corner views are available in the first spatial layer, all other views can be synthesized and super resolved to generate the whole image views but at a lower quality. For example, as shown in Fig. 10 , at bpp3, with only b SL1V L1 + b SL1V L2 = 1. 7% + 2. 3% = 4% of the whole bitstream, all other views can be synthesized. To show how much quality improvement can be achieved by additionally downloading each layer (and loosing next layers), we plot quality vs. downloaded bits for the Bunny light field in Fig. 14. It is seen that the proposed method is resilient to channel errors and can retrieve image views even when a significant portion of a bitstream is lost.
and encode SL 1 with the proposed method (Section IV-A). For super-resolution, we select EDSR  [56]  as an SISR approach and LFT  [55]  as an LFSR approach from BasicLFSR 7 , an open-source light field super-resolution toolbox. EDSR and LFT have been selected since they have been both trained with the same light fields allowing a fair comparison. We super resolve views using EDSR and encode views in SL 2 using the proposed method (Section IV-B). When EDSR is replaced with LFT, all views in SL1 are used as inputs of LFT and the output of LFT will be all views that have been super resolved. Therefore, SL 2 comprises only one viewport layer with LFT approach. We show the compression efficiency and random access performance of both methods in Fig. 17. It is seen that utilizing the LFSR approach for super-resolution improves the compression efficiency at the cost of reduced random access 7 https://github.
The financial support of the  Austrian Federal Ministry for Digital and Economic Affairs , the  National Foundation for Research, Technology and Development , and the  Christian Doppler Research Association  are gratefully acknowledged. Christian Doppler Laboratory ATHENA: https://athena. itec. aau. at/. This work has also been in part supported by the  EU H2020 Research and Innovation Programme  under grant agreement No  694122  ( ERC  advanced grant CLIM, and in part by the french  ANR  research agency in the context of the project DeepCIM. ) v u.
Application to SORCE SSI observations In this second and more realistic example, we consider sixteen years of daily SSI observations made between May 2003 and April 2019 by the SORCE/SOLSTICE and SORCE/SIM instruments. Their spectral ranges are respectively 115. 5-309. 5 nm and 240. 0-2412 nm. Here we restrict ourselves to the UV and visible range up to 700 nm. Our time interval is constrained by the launch of SORCE in 2003 and the time span of one of the proxies. The SORCE/SOLSTICE (level 3, version 17) and SORCE/SIM (level 3, version 27) data are available at http://lasp. colorado. edu/home/sorce/data/. Because of battery degradation, after 2013, SORCE could be operated only during the sunlit portion of the orbit. This led to larger thermal amplitudes that affected the performance of its instruments.
Comparisons with independent observations were made  Marchenko et al. (2016)  using AURA/OMI data after July 2004 in specific bands between 264 and 504 nm, by  Lockwood (2011)  using UARS/SUSIM between 115 and 411 nm until August 2005, and by  Mauceri et al. (2020)  for TSIS/SIM after 2018. These studies confirmed an unusual variability for observations made by SORCE but also suffered from a limited overlap in time.
The solar radio flux a 30. 0 cm (or F30) from Nobeyama Radio Observatory. The flux at that wavelength receives a stronger contribution from thermal Bremsstrahlung and has been shown to be a better proxy than F10. 7 for the Far-UV (120-200 nm) and Medium-UV (200-300 nm) (Dudok de  Wit and Bruinsma, 2017). The F30 index is available at https://spaceweather. cls. fr/services/radioflux/. T. Dudok de Wit: J. Space Weather Space  Clim. 2022, 12, 10  The Mg index from Section 4. This is a proxy for the Far and Medium UV. We consider the Bremen composite, which is the only one that covers the full-time interval. The proxy can be retrieved from https://www. iup. unibremen. de/UVSAT/Datasets/mgii. The total solar irradiance (TSI), which is not a proxy but the integral of the SSI, is measured by an independent instrument.
The good agreement obtained between corrections from different proxies (or, conversely, the small dispersion in Fig. 7b ) strongly supports the idea that the degradation of instrumental sensitivity may not be fully accounted for by the current calibration. Secondly, while the corrections may seem small in absolute terms they are actually comparable to or larger than the solar cycle magnitude. Therefore, great care should be taken in interpreting variations reported by SORCE/SOLSTICE and SORCE/SIM. This is particularly true for variations observed T. Dudok de Wit: J. Space Weather Space  Clim. 2022, 12, 10  between 2003 and 2009 when the correction was largest and partly mimicked the shape of the decaying solar cycle. To illustrate the impact of these corrections, we compare in Figure  8  three different versions of the SSI: 1) the original data from SORCE/SOLSTICE or SORCE/SIM, 2) the original data with our correction, and 3) the original data with the MuSIL correction  (Woods et al.
The latter has been used for correcting the SSI in plot a). The uncertainties of the corrections (not shown) are comparable to or smaller than the dispersion. If we apply our correction to the MuSIL dataset and compare it to the reconstruction from original data, we find a good agreement (not shown), given their uncertainty. This important result means that our method is robust with respect to degradation corrections applied by the instrument teams as long as these can be approximated by our low order model. As a final sanity check, let us compare the reconstruction at 249. 5 nm from two independent measurements from SORCE/ SOLSTICE and SORCE/SIM, see Figure  8a. While the original records show substantially different trends (not shown), their reconstructions agree remarkably well apart for an offset.
The reconstruction, according to MuSIL (available from https://lasp. colorado. edu/lisird/), is based on an earlier release of SORCE data and therefore is not strictly comparable to our reconstruction. Short dropouts observed at 500. 1 nm are primarily due to sunspot darkening. The shaded area around our reconstruction represents a ±2r confidence interval that has been estimated by bootstrapping. T. Dudok de Wit: J. Space Weather Space Clim. 2022, 12, 10 evidence in favour of it. The estimated error on our reconstruction (not shown on the plot) is comparable to the difference between our corrected SORCE data and the two models. From this, we conclude that there is no significant difference between the solar cycle variability of the SSI derived the SORCE dataafter applying our correctionand the SATIRE-S and NRLSSI2 models.
Gaussian processes  (Rasmussen & Williams, 2006)  may provide an interesting alternative to our parametric Fourier model, although they come with a much higher computational cost. Finally, our method provides confidence intervals that reflect both the uncertainties in the data and the errors that result from the modelling of the trend. The latter errors are often neglected while the robustness of the model is vital for allowing the reconstruction to be tested for significance. In an open science approach, we make the Matlab routine and some test data available at https://github. com/tddwit/solar_trend_ detection. The observation of the same trend in SORCE data regardless of the type of proxy lends strong support to the presence of undocumented instrumental effects. Let us stress again, however, that our method should be regarded as an independent and complementary tool for detecting uncorrected variations.
Wayfinding levels present players with a map indicating check-points to reach and the layout of the environment. After this, players tap left or right of the boat to steer to these checkpoints with their trajectory recorded and transmitted from the mobile device to a remote server for later analysis. Using this data we have revealed that navigation ability linearly declines over the life-span, population-level performance for a country can be predicted by its GDP and the extent of the male advantage in navigation skill can be predicted from economic disparities between countries. 3 Navigation performance in the game is also predictive of realworld navigation performance  5  , shows good test-retest reliability  6  and it can better distinguish those at genetic-risk of AD than current 'gold-standard' tasks.
The results were normalized on untreated control (PBS) and expressed as the mean of percentage ± standard deviation of two independent experiments (n = 3-6). The whole bioactive extracts and derived fractions at 10 µg/mL were also tested using MTT cell viability test and the Lactate Dehydrogenase (LDH) assay to validate cell death (%) and cytotoxicity. The additional data is available in the supplementary material. For the LDH assay, the RAW 264. 7 cells were inoculated into a complete culture medium in 96-well plates, at a rate of 10 4 cells per well. After incubation at 37 • C overnight, the medium was renewed with DMEM 4. 5 g/L culture medium without pyruvate with 0. 1% Fetal Bovine Serum (FBS) containing penicillin/streptomycin and glutamine, incubated at 37 • C overnight. The medium was aspirated again, and the cells were treated with 10 µg/mL of extracts/fractions, which were diluted in the medium used previously.
The NO content of the culture medium was analyzed by the Griess reagent method. Data represent mean ± standard deviation (n = 7-11) and the results normalized by the effect of LPS (1 ng/mL) on untreated and stimulated cells. The tested extracts revealed a significant difference with p-values < 0. 05, calculated with ANOVA. ** p < 0. 01, **** p < 0. 0001 indicate significant differences compared to the control. The dashed line represents the level of NO in cells stimulated by LPS alone. The amount of nitrite in the media was calculated from sodium nitrite standard curve.
7 cells. Cells pre-treated with 100 µg/mL of extracts for 1 h were stimulated with LPS (1 ng/mL) for 18h, which is enough to induce a significant signal. The NO content of the culture medium was analyzed by the Griess reagent method. Data represent mean ± standard deviation (n = 7-11) and the results normalized by the effect of LPS (1 ng/mL) on untreated and stimulated cells. The tested extracts revealed a significant difference with p-values < 0. 05, calculated with ANOVA. ** p < 0. 01, **** p < 0. 0001 indicate significant differences compared to the control. The dashed line represents the level of NO in cells stimulated by LPS alone. The amount of nitrite in the media was calculated from sodium nitrite standard curve.
Daily physical activity and sedentary time. From the day after the experiment, every subject was asked to wear triaxial accelerometers (ActiGraph wGT3X-BT, ActiGraph, Inc. , Pensacola, FL) during 7 days with at least one weekend day. Participants wore the device on the right hip  25  on an elastic belt. Data were collected at a frequency of 60 Hz and converted to counts per 1 s epoch using the manufacturer's software (ActiLife version 6. 13. 4). Non wear time was defined as 90 min of 0 count per minute (cpm) with an allowance of 2 min of activity when it is placed between two 30-min windows of 0 cpm  26. To be accepted in the analysis, accelerometer data had to be at least 4 days (including 1 weekend day) of wear with a monitor wear time of ≥ 10 h/day (600 min/ day)  27. SB was calculated with the vertical axis and PA with vector magnitude.