dataesr/scientific-paragraphs-categorization huggingface dataset:
D) was presented to the visitors as an app on a portable device. The Dr. D iPad app contains a question asking tool that allows visitors to ask questions through voice or typing. The question is matched to a database consisting of approximately 13. 000 verified question answer pairs related to the museum content. When there is a close match the app provides an answer; in case of a weaker match the app asks the visitor to provide additional information, if there is no match, Dr. D will inform the user that he does not know the answer to their question yet, and offers to tell them something related to the exhibit. In this way the app stimulates the visitor to further explore the museum. To encourage visitors to focus on the content of the exhibition, gamification is introduced. Visitors are stimulated to ask questions in order to get Dr.
Accessible Cultural Heritage through Explainable Artificial Intelligence AI as a tool is ubiquitously being applied to many domains, art being one of the latest ones being touched. As one of the many applications of AI, art can benefit from AI latest advances because the latest impressive results in AI have mainly focused on images (followed by text), as data source types. However, assessing beyond natural images can serve AI to bullet proof the latest methods on a more challenging domain as it is artistic images or paintings. In this section, we take cultural heritage as a use case to present a conceptual framework for accessible museum experiences. In particular, we advocate for including minorities as special user and evaluator of the latest AI techniques, with an emphasis on some of the drivers to accessibility, concretely involving AI, eXplainable AI techniques and principles for accessible and responsible AI  [93].
For instance, people with visual impairments take and share photographs for the same reasons that sighted people do, but as they find many more difficulties, methods have been developed to assist blind photography (including audio feedback that facilitates aiming the camera)  [99]. Automatically generating descriptions helps visually impaired people better browse and select photos based on human-powered photo descriptions and computer-generated photo descriptions. Could such human computation-generated visual explanations also help completely blind users, e. g. , navigate a museum? Could these help any user that wants to learn from first-hand experts how a given artwork is interpreted, or what it conveys, providing the right context of its time? If the answer is positive, perhaps a model could be trained with all generated data to avoid the arduous task of labelling data so that eventually, the blind would not require human assistance.
Image Captioning Models Image captioning models  [110]  produce a text describing the scene given an input image. With the aim of producing clarifying explanations on why a particular image caption model fails or succeeds, recent strategies make sure that the objects the captions talk about are indeed detected in the images  [111, 112]. Textual explanations  [15, [113] [114] [115] [116] [117]  can also contribute to make vision and language models more robust, in the sense of being more semantically grounded  [118] [119] [120]. Since image captioning models pretrained on datasets outside the art domain fail completely at describing out of distribution inputs (e. g. , pictorial compositions not found in natural images), some metrics evaluating the semantic fidelity of the model have been devised  [111].
While museum staff might also interact with visitors and try to make meaningful experiences for each visitor, this is more transparent and often is shaped in interaction with the visitor. In contrast to this, the algorithmic solution often remains a black box for the visitor and the museum staff. Therefore, it is important that the software is as transparent as possible and that the personal has sufficient understanding of the algorithms to make informed decisions. A concern is that responses that are created, for example by machine learning algorithms, might be very robust, but also are more likely to lack the human touch. Another challenge is related to AI solutions and to the use of personal data. In AI automated decisions are made based on a variety of data sources including personal data, this might lead to issues related to GDPR rules and regulations.
Cardiovascular MRI evaluation MRI studies performed at 3±15 months around Doppler echocardiography used a 1. 5-Tesla magnetic resonance scanner (Symphony™ TIM™, Siemens, Erlangen, Germany). All examinations were transferred to a dedicated workstation, and flow was quantified using Argus TM Flow software (Siemens, Erlangen, Germany). Assessment of cardiac function and measurements were guided by recommendations  [18]. Gadolinium administration and delayed enhancement were used according to standard protocol. The final decision to process gadolinium injection was left to the radiologist in charge of the patient.
Statistical analysis Categorical variables are presented as numbers (percentages) and quantitative variables as means±standard deviations. An overall description of the study population, including imaging measurements, was performed. Quantitative data were compared using Student's t test when appropriate (or the non-parametric Mann-Whitney U test otherwise), and categorical data were compared using the χ 2 test (or the non-parametric Fisher's exact test, accordingly). Characteristics associated with the main independent variable of interest defined by PALS as a continuous variable or categories (< 17%, 17% to < 30% and ≥ 30%, according to cohort tertiles), were assessed by logistic regression using patient and HCM characteristics: age, AF, B-type natriuretic peptide (BNP), LVEF and LV thickness.
Methods A search in the protein database (PDB) for homologous proteins to the FPPMT revealed only three proteins ((pdb-code: 5KOK: the pavine N-methyltransferase  45  ; 5DOO: a protein-lysine methyltransferase from Rickettsia  46  , and 3MGG: a methyltransferase from Methanosarcina mazei  47  ), however, with very low sequence identity (5KOK 16. 6%, 5DOO 14. 8%, 3MGG 12. 7%). This very low sequence identity was not sufficient for standard protein homology modeling. Therefore, the sequence was submitted to the Robetta Web Server  23  for ab initio modeling or/and threading. Five models were returned. Their quality for putative native fold was checked with PROSA II and stereochemical quality with PROCHECK. The one with the lowest z-score value from the PROSA II analysis (Fig. S3 ) was used for further studies.
Grids and scans were analyzed in MOE 2016. 08 using a set of svl-scripts implemented by Richard Bartelt  65. All images of the 3D-structures were rendered with PyMOL  66. All the intermediates as well as the corresponding transition state structures (Fig. 5 ) were directly taken from the PM7 optimized structures for single point energy calculations by more advanced energy optimizations using the density functional theory (DFT) implemented in the ab initio ORCA 4. 0. 0. 2 program package  67. The optimizations were done with the B3LYP functional with the def2-TZVP(-f) basis set and TightSCF optimization  [68] [69] [70] [71]. To save computational time, the diphosphate binding site was removed and a truncated model of the active site was used (F86, Y39, H191, L239, see Fig. S6 ). Therefore, instead of FPP, farnesyl alcohol was used to form presodorifen in the last step of the reaction.
5 min, followed by 15 min at 280 °C. Electron ionization at 70 eV was used. Mass spectra were obtained using the scan mode (with m/z 40-280). Data were analyzed using the Lab Solution software (Shimadzu, Duisburg, Germany). Compound identity was confirmed by comparison of the mass spectra and GC retention times with those of sodorifen and pre-sodorifen. LC-MS analysis. LC-MS analysis was performed using a Nexera X2 liquid chromatograph (Shimadzu Corporation, Kyoto, Japan) coupled to an AB Sciex QTRAP 5500 mass spectrometer (AB Sciex GmbH, Darmstadt Germany). Data were analyzed using the Analyst Instrument and Data Processing Software Version 1. 6. 3. Proteins in the enzyme assay reaction mix were precipitated by the addition of 50% acetonitrile and ultra-filtrated. FPP, PSPP, and SAM were separated by ion-pair chromatography according to Balcke et al.
Study design and participants The ARAMIS trial (NCT02200614) was a phase III, randomised, double-blind, placebo-controlled trial conducted at 409 centres in 36 countries worldwide. Full details of the study have been previously reported  [6]. Briefly, eligible patients aged 18 years or older were diagnosed with histologically or cytologically confirmed adenocarcinoma of the prostate. Patients were required to have a confirmed diagnosis of nmCRPC, a baseline PSA level of at least 2 ng/ml, a PSA doubling time (PSADT) of 10 months or less and an Eastern Cooperative Oncology Group performance status of 0 or 1. Patients were excluded if they had a history of metastatic disease or distant metastases detected by whole-body radionuclide bone scan and computed tomography or magnetic resonance imaging of the pelvis, abdomen and chest; the presence of pelvic lymph nodes less than 2 cm in the short axis below the aortic bifurcation was allowed.
Acknowledgements The authors would like to thank the patients, their families and all investigators involved in this study. Medical writing support, including assisting authors with the development of the outline and initial draft and incorporation of comments, was provided by  Charlotte Simpson, PhD , editorial support was provided by  Annabel Ola, MSc  (both of  Scion, London, UK ) and submission preparation was provided by  Michelle McDermott, PharmD  and  Maria Nicholas, MSc  (both of  OPEN Health Communications, London, UK ), supported by  Bayer AG  in accordance with Good Publication Practice guidelines (Link). The Sponsor was involved in the study design, collection, analysis and interpretation of data, as well as data checking of information provided in the manuscript. However, ultimate responsibility for opinions, conclusions and data interpretation lies with the authors.
Three systematic reviews included RCTs of mucositis prevention examining cryotherapy, KGF and photobiomodulation therapy in pediatric and adult populations, with a focus on evaluating intervention efficacy overall and among pediatric patients where possible. Three systematic reviews included studies of any design examining these same three interventions but restricted to pediatric patients, with a focus on describing adverse effects and feasibility of the intervention. The final systematic review included all RCTs of mucositis prevention conducted in pediatric populations. The literature searches were facilitated by a library scientist in the following databases: MEDLINE, MED-LINE in-process, MEDLINE epubs ahead of print and Embase. Appendix 4 consists of the full search strategies for all seven systematic reviews.
Statistical analysis Synthesis of quantitative data was performed when there were at least three studies with available outcome data. All syntheses used the risk ratio (RR) with the 95% confidence interval (CI) as the effect measure, where an RR less than 1 suggests that the intervention is better than control. As we anticipated heterogeneity, the Mantel-Haenszel random effects model in Review Manager 5$3 20 was used to estimate treatment effects. Stratified analysis focused only on the primary outcome, severe mucositis, and evaluated whether heterogeneity in effects could be explained by age and risk of bias. For risk of bias, adequate sequence generation and adequate allocation concealment were examined as they have the largest association with exaggerated treatment effects  [21]. Stratified analysis was performed when there were at least two studies with outcome data in each stratum.
One study included infants younger than one year of age. Its authors noted that intraoral photobiomodulation therapy could be executed in all patients although for infants and young children, there could be the need to wait for the child to fall asleep or to distract the child with entertainment, increasing the time required  [32]. In stratified analysis, heterogeneity in the effect of photobiomodulation therapy on severe mucositis was not explained by age group, adequate sequence generation or adequate allocation concealment (Appendix 14). The funnel plot of severe mucositis suggested the potential for publication bias (data not shown) although sensitivity analysis using the trim and fill approach did not show substantially different effects compared with the base analysis (Appendix 16). The strong recommendation for photobiomodulation therapy was influenced by consistent benefits and data regarding feasibility in pediatric patients.
However, the indirectness of the data lowered the quality of evidence to moderate and resulted in a conditional recommendation. Recommendation 6: Do not administer GCSFs to pediatric patients receiving treatment for cancer or undergoing HSCT for the purpose of mucositis prevention (strong recommendation, high-quality evidence). Explanation: There were 5 RCTs evaluating GCSFs versus no GCSF (1 added to the 2021 update; Appendix 15). Products evaluated were lenograstim, filgrastim and pegfilgrastim. GCSFs did not significantly reduce severe mucositis (RR 0. 95, 95% CI 0. 75e1. 21) compared with no prophylaxis. The strong recommendation against the use of GCSFs for the purpose of mucositis prevention was influenced by the absence of benefit, adverse effects and costs.
The qPCR reactions were performed in technical triplicates using independent cDNA reactions for each biological replicate and 300 nM of genespecific primer pairs. Specific primers for all candidate genes were designed using Primer3 software (version 0. 4. 0) and are listed in Supplementary Table  1. The thermal cycler protocol included 98 • C for 3 min, 40 cycles of 98 • C for 15 s, 60 • C for 30 s, 72 • C for 15 s, and a final 3-min extension at 72 • C. The expression of all the candidate genes was normalized against four rapeseed reference genes, namely BnaEf1alpha, BnaACT7, BnaTIP41, and BnaPP2A. The selection of four reference genes was based on the different published literatures comparing the reference gene stability in stress conditions in Brassica species  (Wang et al. , 2014; Ma et al.
, 2020). We analyzed the four reference genes in both the roots and the leaves of the experimental samples and used the inbuilt Reference Gene Selection tool provided with CFX Maestro software version 1. 0 (Bio-Rad, Hercules, CA, United States) to evaluate the stability of these reference genes in all the tested samples. Based on the stability values of these reference genes in experimental tissue samples, we chose only the ideal reference genes in each tissue for normalization of expression. Therefore, the expression in the shoots were normalized to the stable BnaACT7 and BnaEf1alpha reference genes, while in the roots, the expression values were normalized in all four reference genes (BnaEf1alpha, BnaACT7, BnaTIP41, and BnaPP2A). Normalized expression of each sample for selected candidate genes were calculated using CFX Maestro Software Version 1.
Statistical Analysis Data are represented as boxplots representing all datapoints, or bar plots indicating mean ± standard error of the mean (SEM) for n = 6. T-test (R software) was employed to analyze the data at each time point independently and graphs were marked by asterisks when significantly different ( * p < 0. 05; * * p < 0. 01, and * * * p < 0. 001, n = 6). Boxplot graphs were created using GraphPad Prism 9. 0. 1 software (GraphPad Software, San Diego, CA, United States).
Participants Frail older adults were included at the Geriatric Medicine and rehabilitation service of Dijon University Hospital, France. All the participants were right handed, as assessed by the Edinburgh Handedness Inventory  [25] , and gave written informed consent, in accordance with the local ethical committee clearance ID-RCB number 2015-A01583-46, Clinical Trial Reference number NCT02855853. This work was carried out in agreement with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. The privacy rights of human participants were observed at all times. Inclusion criteria were (1) recognized cognitive deficit with ability to understand simple orders according to a Mini Mental State Examination  [26]  (MMSE) scored between 15 and 28; (2) recognized motor deficit highlighted by a gait velocity lower than 0.
This complex reaction time task with a relative location uncertainty generates greater APA than a simple reaction time task  [18]. The arm-raising protocol in the pre-and post-test sessions were composed of 20 pointing trials while movement kinematic and surface EMG activities from 11 muscles were recorded. Hand kinematics were recorded using the VICON system (Oxford metrics group, UK) at a sample frequency of 100 Hz. Reflective passive kinematic markers were placed on the right upper arm on the following anatomical loci: nail of the index finger, dorsal aspect of the scaphoid bone, lateral aspect of the elbow, and anterior aspect of the acromion. The three-dimensional path coordinates of the patients' arm markers were captured, reconstructed, and labelled by the Vicon software, following which smoothing was performed using cross-validation splines  [32].
Trajectory data were filtered using a fourth-order Butterworth filter with zero lag and a cut-off frequency of 30 Hz. From these kinematic data, the finger movement onsets were defined when the linear tangential velocity of the index fingertip was equal to 5% of the maximal velocity before the peak value was reached for every trial in every patient. Such a 5% threshold allowed for the detection of the finger movement onset without false-positive detection using an automatic procedure with MATLAB scripts (MATLAB R2014b, Mathworks). The finger reaction time was then calculated as the time-lag between the light-on of the visual target and the finger movement onset. Surface EMG activities (ZERO WIRE EMG system, Aurion, Italia) of 11 muscles were also recorded at a sampling frequency of 2000 Hz and synchronized with the kinematic recordings.
In every movement, the timing of each muscle activation was calculated in each patient for each muscle recording prior to finger movement onset. This parameter was named the activation timing. Consequently, the activation timing of the muscles involved prior to the finger movement onset had negative values (Figure  3B , right column), whereas the activation timing of the muscles with positive values refer to post finger movement onset muscular activations (Figure  3B , left column). In each graph, the 20 recording trials are superimposed. The left column reports the pre-test measures with grey lines whereas the right column reports the post-test measures with black lines. All the recording data were synchronized to the hand movement onset and represented here from 500 ms before the hand movement onset to 500 ms after.
Statistical Analyses First of all, the data coming from the clinical tests (Gait Speed, TUG, and FRT), the finger reaction times, finger velocity peaks, and all the activation timings coming from the EMG data were checked for a normal distribution using Shapiro-Wilk W tests (all the p-values were >0. 12). The variances in each variable were also assessed for equality using Levene's test (all the p-values were >0. 4). The effect of the rehabilitation protocol on clinical tests, motor control, and more precisely management of balance abilities was assessed by comparing the data coming from the pre-test session with those obtained in the post-test session phase. The clinical test results (Gait Speed, TUG, and FRT), finger reaction times, and finger velocity peaks were analysed with a mixed ANOVA.
Secondly, our previous work highlighted significant modulations of the activation timings in these muscles when patients suffering from mild cognitive impairments performed arm-raising movements  [30]. For this reason, the activation timings of the lESl3 and rRF were assessed using a similar mixed ANOVA as described above. From a statistical point of view, such analyses could be interpreted as multiple tests of significance on the same EMG dataset. For this reason, a Bonferroni correction with a level 2 (left ESl3 and right RF) was applied. For the sake of clarity throughout this article, the corrected p-values were presented when dealing with the results of the ANOVA performed on the activation timings of the left ESl3 and the right RF. Once again, Tukey HSD post-hoc tests were carried out where appropriate.
Spacecraft propagation tools describe the motion of near-Earth objects and interplanetary probes using Newton's theory of gravity supplemented with the approximate general relativistic n-body Einstein-Infeld-Hoffmann equations of motion. With respect to the general theory of relativity and the long-standing recommendations of the International Astronomical Union for astrometry, celestial mechanics and metrology, we believe modern orbitography software is now reaching its limits in terms of complexity. In this paper, we present the first results of a prototype software titled General Relativistic Accelerometer-based Propagation Environment (GRAPE). We describe the motion of interplanetary probes and spacecraft using extended general relativistic equations of motion which account for non-gravitational forces using end-user supplied accelerometer data or approximate dynamical models.
General relativity provides our currently accepted theory of gravitation. Within the framework of general relativity, quantities constructed from the metric tensor components g μν such as Christoffel symbols, curvature scalars and the Ricci, Riemann and Einstein tensors encode the geometry of spacetime which we associate with classical gravitational phenomenon. GRAPE assumes the local structure of spacetime is unperturbed by non-gravitational forces, and all associated gravitational effects are contained within the choice of spacetime metric. Consequently, the general relativistic analogue of the first two terms on the right-hand side of Eq. (  3 ) is accounted for by a suitable choice of metric g μν. Further, we note the spacecraft equations of motion are always accompanied by a quadratic invariant quantity unique to general relativity; offering an opportunity to exploit sophisticated structure-preserving numerical integration techniques irrespective of whether non-gravitational perturbations are being considered  (Hairer et al.
The paper is organised as follows: in Sect. 2, we provide an overview of the mathematics employed by the GRAPE orbitography software. We derive the equations of motion for near-Earth objects and interplanetary probes subject to non-gravitational perturbations and discuss the associated quadratic invariant for spacecraft whose wordline is parameterised by proper time τ. Further, we present a brief overview of the tetrad formalism and discuss a novel method to determine the magnitude of the global force components obtained using both Lorentz and tetrad transformations which does not appear to have been previously discussed in the literature. In Sects. 3 and 4, we derive the general relativistic extension of the classical accelerometer equation using a modified geodesic deviation approach  (Pireaux et al.