dataesr/scientific-paragraphs-categorization huggingface dataset:
Homocysteine concentrations did not mediate these associations. Vitamin B-6 might be of added value in preventing age-related decline in physical performance, especially in cases in which increasing physical activity is not feasible. We acknowledge all members of the TiFN research program Mitochondrial Health for their valuable input. The authors' responsibilities were as follows-PG, MM, and LCPGMdG: designed the research; AS, CF, SF-T, EJMF, and LCPGMdG: designed the original NU-AGE trial; AAMB, AS, NM, CMB, AB-D, KR, and AJ: were responsible for data collection; AAMB, EJMF, and LCPGMdG: designed and executed the collection of dietary intake data; CPJD, IPK, and SJLB: were responsible for the biochemical analysis; AS: coordinated the NU-AGE data collection across centers; PG: was responsible for the data analysis and wrote the first draft of the manuscript; and all authors: read and approved the final manuscript.
Tracking data We downloaded all available seabird tracking data that overlapped OSPAR Region V from the BirdLife Seabird Tracking Database (www. seabirdtracking. org) in 2016. These data comprised 2188 tracks of 23 species, from 105 colonies within 16 countries and 25 large marine ecosystems (LME), located in both the North and South Atlantic (Figure  S1 ). We excluded all tracks from colonies that had less than 2% overlap with the study area, unless other colonies in the same LME had higher overlap. The final analyses included 1524 tracked birds of 21 species, from 56 colonies, across 17 LMEs and 16 countries/jurisdictions (Table  1 ). Phenology and population data for each species and colony were obtained from the literature and verified by experts (Table  S1 , Figure  S2 ). Following advice from the scientific community, and in line with other studies (e.
, 2012; Kopp et al. , 2011). Our analysis included tracking data from major colonies of each species in the Atlantic, including those that represent more than 90% of the global or Atlantic population for many species (e. g. , Cory's Shearwater, Sooty Shearwater, Great Shearwater; all gadfly petrels, genus Pterodroma). We only used tracks of adults, because relatively few juvenile and immature birds have so far been tracked, which can have an influence on the overall distribution of seabirds  (Carneiro et al. , 2020). Indeed, a recent study also found the area to be used by immature Cory's Shearwaters  (Campioni et al. , 2020). Thus, our estimates should be considered a minimum. Population estimates for each colony are subject to natural variation and recording error, and the number of birds using the area should ideally be updated regularly.
A research and monitoring plan should be adopted alongside the managed MPA, to understand more about the features supporting seabirds and other taxa, to monitor the effectiveness of the MPA, and to inform additional management measures if required. There are also increasing opportunities for monitoring remote areas using satellite and biologging technologies that could be explored  (Harcourt et al. , 2019; Sutherland et al. , 2016). The current ABNJ MPAs under OSPAR are managed through a "Collective Arrangement" (NEAFC & OSPAR, 2015), and a collaborative approach to management would also be needed for this proposed MPA. Overall, our analysis has demonstrated that multispecies tracking data can identify important sites in ABNJ that are suitable for protection. The ongoing work of the OSPAR Commission presents a unique opportunity for this study to inform policy and practice to benefit seabird conservation on the high seas.
The data are deposited on BirdLife International's Seabird Tracking Database http://www. seabirdtracking. org/ C O N F L I C T O F I N T E R E S T The authors declare no conflict of interest. O R C I D Tammy E. Davies https://orcid. org/0000-0003-2535-1328 Marta Cruz-Flores https://orcid. org/0000-0001-9905-4727.
Our experiments show that this model significantly improves the quality of the CoP trajectory predictions, compared to a commonly used Langevin model, including when subjects' vision and/or standing surface were manipulated. Additionally, we show that the same results can be obtained on another publicly available dataset of postural control  [35]. We also used this new model to estimate the relative importance of each force, and our results show that these parameters can be used to differentiate between distinct populations and experimental conditions, highlighting their possible use to improve the understanding of several aspects of postural control. Overall, the present study supports the hypothesis that the CoP dynamic is intrinsically and deeply intertwined with the CoM dynamic, and that the Langevin model has the potential to quantify interesting components of postural control and can greatly benefit from encoding joint dynamic of the CoP and CoM instead of their marginal behavior.
During trials with eyes open, participants were asked to fix a target which was located at eyes height, at two meters distance. Trials were acquired in blocks of two consecutive recordings, in order to reduce confounding factors, such as fatigue or learning  [23]. In between blocks, subjects were allowed to rest by sitting or walking around. We replicated our results by using the public dataset  [35]  (hereafter referred to as population 2), which contains three-dimensional kinematics and the ground reaction forces of 49 subjects (27 young individuals -15 males, 12 femalesbetween 18 and 40 years old; 22 older adults -11 males and 11 females -60 years old or older). The database contains 588 recordings in total, among which 17 were removed as their kinematics time series were missing. All subjects were recorded in similar conditions as the first population, and both subjects' vision and the standing surface were identically manipulated.
B. Hardware For our study, CoP data were collected using a ground-level six-channel force plateform (AccuSway, AMTI, Watertown, MA, USA), which sampled the three-dimensional ground reaction forces and moments at 100 Hz. A poster was used to provide a 5-cm fixation target that was displayed approximately two meters in front of the participant, at eye level, during eyes open conditions. In order to standardise the shoe-platform interface, participants were recorded while wearing standardised socks. Kinematic data were collected using an OptiTrack system (NaturalPoint, Corvallis, OR, USA) at a sampling rate of 100 Hz using 18 cameras. Each participant wore a full body suit, on which markers were placed to track the position of key anatomical locations, which were used to compute the position and trajectory of the CoM during the recording.
C. Data Preprocessing Data from the force platform and the Optitrack system were collected and synchronised using Motive (NaturalPoint, Corvallis, OR, USA). Data preprocessing and analysis software were written using Python (v3. 7, Python Software Foundation, OR, USA). Raw force plateform data were processed with a fourth-order, zero-lag, low-pass Butterworth filter with a  10 Hz cutoff frequency, in accordance to  [23]. CoP position was calculated with the usual formula  [38] : CoP x = -F x c -M y F z and CoP y = -F y c + M x F z , where CoP x (respectively CoP y ), F x (resp. F y ), M x (resp. M y ) denote the coordinates of the CoP, the ground reaction forces and the moments on the medio-lateral, resp. antero-posterior axis, F z denotes the ground reaction force coordinates in the vertical axis, and c is the calibration parameter of the force platform.
A. Model Evaluation In all our analyses, the local recall model produced significantly larger values of explained variance (see Figure  2 ). As shown in Figure  2a , this improvement was observed for every recording condition of our experiment ( p < 10 -8 compared to the global recall model, p < 10 -10 compared to the average model). Similar results were obtained on the larger population 2, which is the public dataset of  [35]  (Figure  2b , all respective p-values are < 10 -40 ). This is particularly  interesting as the population included in this second dataset is larger and far more diverse, including young individuals and older adults, as well as individuals with a history of falls. It is also interesting to note that the average model, which tries to infer the acceleration of the CoP using solely the acceleration of the CoM, achieves adjusted coefficients of determination closed to zero, and significantly lower than the other models.
However, the analyses also showed that by adding information about the CoM position in the Langevin model, the local recall model produces better estimates of the CoP dynamic than the global recall model. This result suggests that the trajectory of the CoM is important to understand the CoP dynamic, and that the Langevin model may provide relevant insights into the CoP behavior with respect to the CoM. Crucially, the predictions accuracy improvement was consistent, and occurred for each trajectory of both datasets, regardless of the protocol or of individual characteristics. This observation that the CoM is key to understand the CoP dynamic is in line with the results discussed by previous works. In  [31]  a method was proposed to decompose the CoP trajectory in two components, rambling and trembling, where the latter is assumed to reflect the oscillations of the CoP around a reference point trajectory.
However, Langevin models do not explicitely incorporate ankle stiffness and further work is required to link quantitatively the recall parameter to biomechanics components. Conversely, the parameter , which measures the strength of the damping, i. e. the force which opposes to the velocity of the system, was shown in our experiments on Pop 2. to decrease in the ML axis when going from a rigid surface to a foam surface. This result shows that this force, preventing the velocity of the body becoming too strong, is a component of postural control that can be vulnerable to sensorial perturbations induced by unstable surfaces  [44] , and thus be indicative of some specificities in sensorimotor profile. The fact that this result is not observed on Pop 1. could be explained by some differences existing between the type of foam used in each dataset.
51  Identifying and improving weak links in the local chain of survival, paired with targeted approaches to improve the effectiveness, has resulted in positive outcomes achieved in several geographic regions. OHCA causes are classified into cardiac and noncardiac causes. 47, 55, 56  Approximately 80% of individuals presenting with OHCA reached by EMS, and in whom resuscitation is considered possible, have a cardiac cause. 56  OHCA can affect seemingly fit and healthy athletes, young adults, 23 000 to 1 in 200 000 athletes per year, depending on a number of factors including populations studied. 57, 58  In a retrospective analysis of the Rescu Epistry database of consecutive OHCA attended by EMS in a specific area of Ontario, Canada, the incidence of SCD during participation in competitive sports was reported to be 0.
| Investigation of sudden cardiac arrest survivors: genetic evaluation Although most SCA survivors will have an indication for an implantable defibrillator for secondary prevention of a cardiac arrest,  6  genetic evaluation may influence final diagnosis, treatment recommendations, and family screening (Figure  15 ). In some cases, genetic evaluation may enable therapy specific to the disease mechanism. Recent technological advances in genetic evaluations, establishment of reference databases of genetic variants, systematic annotation of causal genes,  305  and standardization of variant interpretation  306  have enabled efficient and comprehensive genetic assessment. However, the pace of genetic discovery and variant interpretation is evolving rapidly, creating a complex landscape surrounding genetic evaluation of SCA survivors.
Multidisciplinary cardiac genetic clinics Multidisciplinary meetings Registry CARDIAC GENETIC SERVICE Clinical leader Lead coordinator Regional coordinators/ Secretarial support nurse specialists Database Adult and pediatric administration cardiologists Governance Genetic counselors Clinical geneticists Pathologists Psychologists/ social workers Clinical scientists Noncardiac specialists as needed University and research links Molecular genetics 8 Participants in a cardiac genetic service. "Pathologists" includes forensic pathologists. Modified with permission from Elsevier 69 inheritance risk to family members and need for clinical screen- ing can add an additional stressor at a time of intense grief.
Nel caso del DL, a partire da una mole più elevati di dati annotati forniti, è il sistema stesso che identifica le caratteristiche distintive adeguate, tramite reti neurali artificiali multi-layer. Su un algoritmo di ML supervisionato, il Random Forest (RF), si sono basati alcuni studi recenti  [Grilli et al. 2019; Teruggi et al. 2020] , che hanno mostrato come la classificazione delle nuvole di punti 3D basata sulla scelta di feature geometriche ad hoc possa essere applicata efficacemente anche al caso dei beni culturali, per distinguere elementi architettonici ricorrenti. Per quanto riguarda il DL, invece, le applicazioni ai beni culturali restano a oggi molto limitate  [Matrone et al. 2020a] , da una parte a causa della difficoltà di estendere il riconoscimento di classi di elementi a dataset diversi, vista la complessità morfologica e tipologica di ogni edificio storico, e dall'altra per via della scarsa disponibilità di una mole sufficiente di dati annotati per l'allenamento dei modelli predittivi  [Matrone et al.
Nell'approccio metodologico proposto: a) dapprima, si esegue la segmentazione semantica del set di dati 3D, tramite algoritmi di apprendimento supervisionato derivati dall'IA; b) quindi, la nuvola di punti contenente la suddivisione in classi di elementi tipologici (muro, colonna, solaio, copertura…) è sfruttata per generare e organizzare in maniera più efficace modelli H-BIM. Nella prima fase (a), si annota una parte ridotta della nuvola di punti, identificando le classi. Questa operazione, insieme alla scelta di opportune feature, consente di fornire al sistema di apprendimento un numero adeguato di esempi 'annotati' per classificare correttamente la parte restante del dataset. Nel ML supervisionato, le feature sono identificate e scelte di volta in volta in relazione al caso studio, e possono essere: -colorimetriche, ovvero relative al dato di colore associato a ogni punto 3D; -geometriche, che descrivono la disposizione spaziale reciproca dei punti della nuvola.
Risultati Il patrimonio delle certose è di fondamentale importanza per le prime significative sperimentazioni della metodologia proposta. Per esso, infatti, la collocazione spaziale degli ambienti segue le pratiche e gli usi dei monaci certosini, e c'è per questo una grande ripetizione di elementi archetipici: il chiostro principale, i chiostri minori, le celle dei padri e la chiesa, con portici, gallerie, volte affrescate. Nel caso del Chiostro Grande, a partire dalla nuvola di punti iniziale, sono manualmente annotate su una porzione ridotta le 14 classi di elementi di fig. 3 , riconosciute a partire da trattati di architettura. Quindi, sono estratte e selezionate le feature geometriche (figg. 4, 5). L'annotazione manuale del cosiddetto training set, insieme alla selezione delle feature, permette di allenare un classificatore RF, che estende la segmentazione semantica all'intero dataset del chiostro.
Conclusioni Le tecniche di IA applicate ai beni culturali aprono la strada a nuovi modi di interpretare, conoscere e rappresentare il dato geometrico, 2D o 3D, ottenuto dal rilievo. Si acquisisce così un più diretto collegamento, formale e 'ontologico', tra dati metrici e modelli digitali integrati, come quelli del tipo H-BIM, per la documentazione del costruito storico. Ulteriori sperimentazioni vanno nella direzione del miglioramento del processo di costruzione della replica digitale e dell'estensione della metodologia proposta ad altri dataset. Così, si prevede di applicare l'approccio presentato a vari casi studio, relativi a edifici storici e beni culturali che siano stati costruiti in epoche diverse, o che si presentino in condizioni differenti di conservazione, per valutare le specificità di ogni nuovo contesto.
Introduction Drafting and representation are to date making increasing use of 3D surveying for digital documentation of cultural heritage. The evolution of data collecting techniques, such as laser scanning and photogrammetry, has in fact allowed for ever wider use of point clouds and meshes as starting points in applications for preservation, enhancement and dissemination of historical and architectural heritage. Indeed, modern Scan-to-BIM processes make use of 3D data to create digital information models. Although point clouds provide metrically verifiable input data, the reconstruction of H-BIM (Heritage-Building Information Modelling) parametric models based on these is to date a lengthy and cumbersome process, involving complex procedures of interpreting, detecting and subsequent modelling of 3D elements, therefore requiring substantial user interaction.
g. geometric and colorimetric features. Subsequently, the system is trained for the classification of new data, based on the detected features. In DL contexts, the system in itself detects the appropriate distinctive features, by means of multi-layer artificial neural networks. A supervised ML algorithm, the Random Forest (RF), provided the basis for some recent investigations  [Grilli et al. 2019; Teruggi et al. 2020] , that showed that classification of 3D point clouds based on specific geometric features can be effectively applied to cultural heritage, providing distinction of recurring architectural elements. Conversely, DL applications for cultural heritage are to date quite restricted  [Matrone et al. 2020a ], due on one hand to the difficulty to extend recognition of element classes to different datasets, given the morphological and typological complexity of each historical building, and on the other hand to the still inadequate availability of a critical mass of annotated data needed to train the predictive models  [Matrone et al.
2020b ]. For these reasons, investigation on ML-based segmentation provide to date the best outlook; anyway, proper attention to the effective use potential of 3D segmented data is still scarce, e. g. for enhancing H-BIM models. By fitting in this context, the present investigation aims at focusing on the transition from semantic point cloud to parametric H-BIM representations. -MiBACT), and, since 1978, the Natural History Museum of the University of Pisa (Pisa University Museum of Natural History). Since 2018, the University of Pisa has provided financial backing for a multidisciplinary research project aimed at the preservation, restoration and enhancement of the whole complex, to which the current investigation relates (fig. 2 ). The current work stems from the consideration that, in the morphological and constructive complexity of historical buildings, some recurring architectural typologies and shapes are still recognizable.
This allows for effective differentiation and, as a result, representation by reconstructing their 'shape grammar'. The process of semantic segmentation is in fact the very one that led Vitruvius and other major essayists to investigate, and pass on, the typical proportions of architectural orders, thus codifying languages and models of classical architecture. In the proposed methodological approach: a) firstly, semantic segmentation of 3D datasets is performed, by means of AI-derived algorithms of supervised learning; b) secondly, point clouds carrying the partitions for classes of typological elements (e. g. wall, column, floor, roof etc. ) are exploited to generate and more effectively manage H-BIM models. In step a), classes are identified by annotating point cloud portions. This operation, along with the proper feature selection, allows for providing the learning system with an adequate number of annotated samples for the subsequent correct classification of the remaining part of the dataset.
Results The heritage of the Charterhouse is essential for the first significative tests of the proposed methodology. In fact, here the spaces are laid out in accord with Carthusian practices and customs, which entail massive repetition of archetypal elements: the main cloister, the lesser cloister, the cells of the Fathers and the church, with colonnades, galleries and frescoed vaults. For the main cloister of the Pisa Charterhouse, starting from the initial cloud, 14 element classes, as per architectural treatises, are manually annotated on a small portion (fig. 3 ). Geometric features are then extracted and selected  (figs. 4, 5). The manual annotation of the so-called 'training set' allows to train a RF classifier, so as to extend the semantic segmentation to the whole cloister dataset.
Fig. 6  shows the classification result, that provides the initial data for subsequent construction of the H-BIM model. Each element class, upon segmentation and isolation from the complete cloister dataset, is separately imported in H-BIM environment (fig. 7 ). For each class, the reconstruction of the conceptual parametric shape follows the formalization approach  [De Luca et al. 2007 ] based on: interpretation of any knowledge referring to the shape; detection of the modelling methods; identification of the relations between the different shape constituents  (figs. 8, 9). Upon defining an ideal conceptual geometry, the latter is propagated to other point cloud sections identified as belonging to the same class, according to a reconstruction approach based on visual programming languages.
Conclusions AI techniques applied to cultural heritage lay the foundations for innovative ways of interpreting, recognizing and representing geometric data derived from surveying, both 2-and 3-D, establishing a stronger formal and ontological link between metric data and integrated digital models such as H-BIM, for more effective documentation of the built heritage  (figs. 10, 11). Further testing will aim at improving the construction of the digital twin and extending the proposed methodology to other datasets, applying the same approach to other case studies, involving historical buildings and cultural heritage from different construction periods or under different preservation conditions, so to assess the specificity of each new context. Lastly, AI applications for semantic mapping of decay levels or surface material restitution from 2D/3D survey data are currently investigated as further development of the present work.
Acknowledgement This study is part of the project  Studi conoscitivi e ricerche per la conservazione e la valorizzazione del Complesso della Certosa di Calci e dei suoi Poli Museali , promoted by the  University of Pisa. We therefore thank all the researchers and collaborators involved in the project. The research is partly funded by the  Université Franco-Italienne , within the framework of the  Vinci 2019  project -Mobility grants for co-supervised Ph. D. theses, which promotes collaboration between Italian and French research units.