dataesr/scientific-paragraphs-categorization huggingface dataset:
Towards the microscopy connectome: hybrid MRI-PLI tractography One of the primary limitations of the microscopy data in BigMac is that it only informs on the fibre orientations in the 2D plane of sampled slides, precluding 3D reconstruction of the microscopy connectome. In comparison, the diffusion data can provide orientational information in 3D, but with limited spatial resolution (0. 6-1 mm isotropic). Figure  7  demonstrates one approach to joint modelling  10  where we combine in-plane orientations from microscopy (here PLI) with through-plane information from postmortem diffusion MRI (dMRI) to reconstruct 3D 'hybrid dMRI-PLI fibre orientations' at the resolution of the microscopy data. For each PLI pixel, we compare the PLI in-plane orientation to orientations estimated from co-registered diffusion data using the Ball and Stick model  20  which have been projected onto the PLI plane (Fig.
7a ). The PLI through-plane angle is then approximated by that from the most similar Ball and Stick orientation. This produces a hybrid dMRI-PLI orientation that is both 3D and at the resolution of the microscopy data. These orientations can then be combined into a hybrid fibre orientation distribution (FOD) which can be directly compared with those from diffusion MRI and input into existing tractography algorithms for tract reconstruction. Here we reconstruct hybrid orientations using the PLI data in BigMac, though our current method is also applicable to histology slides analysed using structure tensor analysis. Figure  7b  shows example dMRI-PLI FODs at varying spatial resolutions. Reassuringly, the hybrid FODs show smoothly varying patterns in all three dimensions, even when the hybrid FODs are of higher spatial resolution than the diffusion MRI (0.
Discussion The BigMac dataset aims to characterise a single connectome in exquisite detail, combining MR signals with high resolution microscopy data throughout the macaque brain. The postmortem diffusion MRI includes high b-value ultra-HARDI data to characterise the diffusion signal in great detail, estimate white matter fibre orientations with high precision, and improve the reconstruction of white matter tracts through crossing fibre regions. Furthermore, with 1000 gradient directions we retain dense sampling on an arbitrary 2D plane for direct comparison with 2D microscopy data. The microscopy includes coronal slides of polarised light imaging  [12] [13] [14]  and myelin-stained histology that has been densely sampled throughout the brain. This high-resolution, high specificity data allows us to visualise tissue myeloarchitecture in detail to provide neuroanatomical insight -such as the orientational coherence of the PLI signal in the cerebellar molecular layer -and act as a pseudo ground truth estimate of tissue microstructure against which MR metrics can be compared.
Crucially, the MRI and microscopy data have been carefully co-registered, facilitating novel data fusion analyses including the hybrid diffusion MRImicroscopy tractography presented here. The dataset has several limitations including: the 2D nature of the microscopy, which precludes 3D visualisation of the cell bodies or nerve fibres within each microscopy section; the multimodal nature of the microscopy prohibits whole-brain 3D reconstruction of the tissue microstructure from a single contrast at high resolution (as in e. g. the BigBrain dataset 7 ); tissue processing artefacts (c. f. Methods: Polarised light imaging) and inconsistent staining (c. f. Methods: Gallyas silver staining) in some PLI and histology slides respectively; and the choice of MR data acquired where here we chose to comprehensively sample q-space in the long diffusion time regime, rather than, for example, acquire data at multiple diffusion times for sensitivity to restricted compartments, or acquire MRI at very high spatial resolution.
The latter could be overcome by acquiring additional MRI on a different monkey brain that was subsequently co-registered to the BigMac data, though such analyses would have to assume some level of anatomical consistency between brains. Finally, we acknowledge that this data was acquired from a single macaque brain, with lesions in the orbitofrontal cortex and an abnormality in the left hemisphere. This may limit the datasets suitability for specific applications. Nonetheless, as a unique, multimodal resource that complements existing open data (for cross-modality, cross-subject or cross-species investigations, as well as data from invasive macaque studies not possible in humans), the BigMac dataset will enable neuroscientists to ask new and fundamental questions. For example, it will enable researchers to (i) link brain connectivity across spatial scales spanning four orders of magnitude; (ii) understand the relationship between relatively crude but non-invasive MRI signals and cellular properties measured by invasive microscopy; and (iii) develop novel approaches to extract rich information from in vivo MRI data by leveraging the specificity of microscopy-derived gold standards.
The BigMac dataset provides an open access platform from which we can interconnect microstructural features with MRI signals throughout the brain. Details on how to access the data, further documentation and code are provided in the Data and Code Availability sections below. Future work will include characterising the unique information provided by the hybrid MRI-PLI tractography, as well as completing ongoing microscopy work to add Nissl-stained histology and other complementary stains (e. g neurofilament protein SMI32 or glia Iba-1 or GFAP) to the BigMac resource.
Data acquisition At the centre of the BigMac dataset is the brain of a single adult rhesus macaque (Macaca mulatta, male). The animal was cared for, and data were acquired by, researchers at the University of Oxford, UK. All procedures were performed under licences from the United Kingdom (UK) Home Office in accordance with the UK Animals (Scientific Procedures) Act 1986 and with European Union guidelines (EU Directive 2010/63/EU). During it's adult life, the macaque was scanned in vivo over multiple scan sessions. At 11. 7 years of age, the brain was perfusion fixed in formalin after which extensive postmortem MRI data was acquired. After scanning, the entire brain was sectioned along the anteriorposterior axis, with consecutive slices processed for different microscopy contrasts. The acquisition timeline for presented data was as follows: • In vivo MRI session 1: April 2010 (4 years old) • In vivo MRI session 2: January 2017 (10 years 9 months old) • Perfusion fixation: December 2017 (11 years 7 months old) • Postmortem MRI: March-April 2018 (3-4 months postmortem) • Postmortem microscopy: 2018 onwards Tissue pathology.
9  (bottom) shows example postmortem structural images from the BigMac dataset. Note how the contrast is inverted when related to the in vivo T1-weighted images. Here we purposefully acquire T2/T2*-weighted postmortem data as conventional T1w typically don't give good contrast postmortem due changes in relaxation times. Due to their high image quality and anatomical detail, the postmortem structural MRI act as a crucial intermediary in the co-registration of both the diffusion MRI and microscopy data (c. f. Co-registration) and the in vivo and postmortem MRI. T 1 mapping. A T 1 map was acquired using similar imaging parameters as the 0. 6 mm postmortem diffusion MRI data but now with an inversion recovery preparation: TE/TR = 8. 6 ms/10 s, FOV = 76. 8 × 76. 8 × 76. 8 mm, resolution 0. 6 mm isotropic and 12 inversion times (TI) from 10 to 6000 ms.
Registration The ultra-HARDI data for both b = 7, and 10 ms/μm 2 were acquired within the first scanning session. At specific time points throughout the week-long acquisition, the central scanner frequency was recalibrated. This occurred three times during the b = 7 ms/μm 2 acquisition and 4 times during b = 10 ms/μm 2. Because of the recalibration, images acquired with different scanner central frequencies are shifted (translated) with respect to one another. To correct for these translations, the data were rigidly registered to a reference S 0 image (i. e. a volume with negligible diffusion weighting) from the ultra-HARDI dataset. Here the reference image was taken to be the mean S 0 image from the first 'set' of images which were all acquired with the same central frequency. The registration was performed using FLIRT with spline interpolation of the data  40, 66.
Finally, to integrate the BigMac dataset with other datasets, nonlinear transformations  41, 64  were computed between the postmortem structural image and the F99 standard template  67. Here we utilise a T 1like image, created from the structural MRI using hand-edited white and grey matter masks, because non-linear registration requires images with similar contrast and the BigMac ex vivo structural image has inverted contrast when compared to the in vivo F99 T 1.
Data normalisation Most diffusion models approximate the S 0 image by taking the mean S 0 image across all volumes with minimal diffusion weighting, assuming that the signal magnitude is constant across time. In contrast, here we found the S 0 signal to vary between scanning sessions and b-shells. Were diffusion models naively applied to concatenated data from the BigMac dataset, the results may be biased (e. g. the kurtosis would be misestimated). Consequently, the diffusionweighted data was normalised to the mean S 0 of the b = 10 ms/μm 2 ultra-HARDI data. Combining linear and spherical tensor encoding Combining data with linear and spherical tensor encoding allows for the separation of effects due to the fibre orientation distribution and the diffusion properties, to estimate additional microstructural parameters such as μFA  47, 48.
In BigMac, data with spherical tensor encoding were also acquired at b-values of 4, 7 and 10 ms/μm 2. The gradient waveform was optimised using the NOW toolbox in Matlab  68. Due to additional stress on the magnetic gradients when performing the spherical tensor encoding, the repetition time (TR) was increased with respect to the ultra-HARDI data: TE/TR = 42. 5 ms/6. 4 s; FOV = 76 × 76 × 76 mm; 1 mm isotropic resolution. For each b-value, 30 images were acquired with spherical tensor encoding and 1 with negligible diffusion weighting. Complementary data with linear tensor encoding and the same TR were also acquired: 50 gradient directions per shell with δ/Δ = 14/24 ms, plus 2 volumes with negligible diffusion weighting. The gradient amplitude G was adjusted to produced the required b-values of b = 4, 7 and 10 ms/μm 2.
Microscopy Using the BigMac dataset, we can link the MRI signal to microscopy data which has both micrometre resolution and high specificity. In BigMac, the brain was sectioned, stained, and imaged ('processed') in two batches. The brain was first cut around the level of the posterior tip of the central sulcus to create two tissue blocks, representing the anterior half and posterior half. First, the anterior block was sectioned, stained, and imaged, after which the posterior block was processed using a highly similar protocol. Each tissue block was sectioned on a frozen microtome along the anterior-posterior axis to produce thin coronal tissue sections. Consecutive sections were allocated, in order, to one of six contrasts: 1. Polarised light imaging to visualise myelinated fibres (50 μm thick) 2.
Cresyl violet staining of Nissl bodies (50 μm thick) 3. Unassigned section (50 μm thick) 4. Gallyas silver staining of myelin (50 μm thick) 5. Unassigned section (100 μm thick) 6. Unassigned section (50 μm thick) Each contrast was repeated every 350 μm throughout the brain. The unassigned sections were returned to formalin and stored for longevity. The imaging of the tissue sections is very time consuming. Hence, slide digitisation is an ongoing process where the Nissl and other complementary stains will be released at a future date. Polarised light imaging. Polarised light imaging (Fig. 3 ) utilises the birefringence of myelinated axons to estimate the primary fibre orientation per pixel  [12] [13] [14]. Here unstained tissue sections were imaged using a Leica DM4000B microscope with an automated stage (Leica, Germany, using Leica Application Suite X software) adapted for PLI with an LED light source, a polariser, a quarter wave plate with its fast axis at 45 degrees to the transmission axis of the polariser, and a rotatable polariser (the analyser).
Due to the large size of the BigMac tissue sections, multiple fields of view were acquired across each sample and later stitched together to form a whole slide 'mosaic' image. For each field of view, images were taken as the analyser was rotated from 0 to 180 degrees in 9 equidistant steps. A 2. 5x magnifying objective produced an imaging resolution of ~4 μm per pixel. PLI processing was performed using inhouse developed MATLAB scripts  4. Background correction was performed  4, 71  to account for light source variations across the image, after which a sinusoid was fitted to the pixelwise image intensity as a function of the analyser rotation. Maps of transmittance, retardance, and in-plane angle were derived from the sinusoid phase and amplitude  [12] [13] [14]. Supplementary Figure  11  shows example PLI mosaics from the BigMac dataset.
Initial values and ranges for the transformation parameters were defined in a configuration file that we fine-tuned for the BigMac dataset in a trial and error process ("the optimised TIRL protocol"). The transformation parameters were then optimised in predefined combinations in an automated three-stage process. (1) We first provided approximate coordinates for the centre of the microscopy image in the MRI volume. Along with some tolerance of error, this defined a "slab" of the structural MRI within which the registration was optimised. The microscopy images were first resampled to the resolution of the structural MR data and then registered into the 3D imaging volume. The registration started with a rigid search to find a 3D surface in MRI space that best represented the "cutting plane".
6b. Finally, the orientation dispersion index (ODI  45  ) of each 2D FOD (from BAR, PLI and histology) was calculated according to ref. 4. An ODI of 0 indicates no dispersion, whilst an ODI of 1 describes isotropic dispersion. The ODI values were compared on to DTI FA  43  and microscopic FA from the simultaneous analysis of linear and spherical tensor encoded postmortem data (b = 4, 7 & 10 ms/μm 2 , 1 mm isotropic)  47, 48, 69, 70. The analysis for Fig. 6c , d was repeated with in vivo MRI data. Session 1 data (b = 1 ms/μm 2 , 81 unique gradient directions, 1 mm isotropic) were similarly processed using the Ball and Rackets  44  and diffusion tensor models  43  , and estimates of BAR ODI and DTI FA were compared to microscopy ODI estimated in the in vivo MR space. For each scatter plot, the correlation coefficient r was calculated using MATLABs  73  fitlm function, and the p-value calculated using an F-test comparing the regression model to a degenerate model with only a constant term.
Acknowledgements The authors would like to thank  Katherine Bryant ,  Jesper Anderson ,  Frederik Lange ,  Moises Hernandez Fernandez ,  Paul McCarthy ,  Michiel Kleinnijenhuis  and  Menuka Pallebage Gamarallage  for their advice at stages throughout this project. AFDH and IN were supported by the  EPSRC ,  MRC  and  Wellcome Trust  (grants  EP/L016052/1  and  WT202788/ Z/16/A ). IN was also supported by the  Clarendon Fund  in partnership with the  Chadwyck-Healey Charitable Trust  at  Kellogg College , Oxford. AK and NS were supported by  Cancer Research UK  (grant number  C5255/A15935 ). RM was supported by the  BBSRC  (grant  BB/N019814/1 ). JS was funded by  ANR  grant ( ANR-21-CE37-0016 ). KLM and SJ were supported by the  Wellcome Trust  (grants  WT202788/Z/16/A ,  WT215573/ Z/19/Z  and  WT221933/Z/20/Z ).
Data availability The BigMac data, including minimally preprocessed data, are openly available via the Digital Brain Bank 6 : https://open. win. ox. ac. uk/ DigitalBrainBank/#/datasets/anatomist. Example data can be accessed via the online viewer and the full dataset is available via a data sharing agreement to ensure the data is used for purposes which satisfy research ethics and funding requirements. Further documentation, including example images of all modalities, is available via https://open. win. ox. ac. uk/pages/amyh/bigmacdocumentation. Since the full dataset requires a considerable amount of memory (~1. 8 TB), users may wish to request only a subset of relevant data. To facilitate this, the documentation includes an extensive file tree, listing available files as well as approximate memory requirements for different parts of the data.
This paper presents SmartSPEC, an approach to generate customizable synthetic smart space datasets using sensorized spaces in which people and events are embedded. Smart space datasets are critical to design, deploy and evaluate systems and applications under issues of heterogeneity, scalability and robustness, leading to cost-effective operation which improves the safety, comfort and convenience experienced by space occupants. However, many challenges exist in obtaining realistic smart space datasets for testing and validation, from a lack of fine-grained sensing to privacy/security concerns. SmartSPEC is a smart space simulator and data generator that leverages a semantic model augmented with user-defined constraints to represent important attributes, relationships, and external domain knowledge for a smart space.
We employ machine learning (ML) approaches to extract relevant patterns from a sensorized space, which are used in an event-driven simulation strategy to generate realistic simulated data about the space (events, trajectories, sensor observation datasets, etc. ). To evaluate the realism of the generated data, we develop a structured methodology and metrics to assess various aspects of smart space datasets, including trajectories of people and occupancy of spaces. Our experimental study looks at two real-world settings/datasets: an instrumented smart campus building and a city-wide GPS dataset. Our results show the realism of trajectories produced by SmartSPEC (1. 4x to 4. 4x more realistic than the best synthetic data baseline when compared to real-world data, depending on the scenario and configuration), as well as sensor data derived from such trajectories which adhere to the underlying semantics of the smart space as compared to synthetic sensor data baselines, even under hypothetical changes.
Understanding expected energy-efficiency in building operations has also been studied to evaluate planned structures  [4]. In general, designing techniques to promote safety, security, and sustainability for such applications -and ensuring that they operate wellrequires real data, at scale. However, such data can be difficult and non-trivial to obtain. A key challenge is the cost and complexity of real data collection. This includes purchasing and deploying sensors, which becomes expensive as buildings get larger and more sensors need to be instrumented. Participants must also carry devices and be observed over long periods of time, which can be time-consuming for a dataset limited in its scale (both spatially and temporally). Moreover, depending on the deployment itself, the resulting dataset may not even contain the type and granularity of sensing required for user-level applications, even in ideal settings.
To address this problem, ML-based techniques have been applied to directly learn modeling parameters  [7, 8]. While this can help in understanding properties of the space for similar scenarios, adopting them for other settings of interest is difficult without considering more complex attributes and relationships in the smart space. For example, consider a user who wishes to understand how a pandemic has affected human behavior in a building. Here, if the only data collected (or models learned) captured people during a non-pandemic time, then these results would not accurately reflect the desired setting to study. Thus, understanding the semantics underlying a smart space ecosystem is critical to accurately model the activities and operations within the space. In this paper we present SmartSPEC  [9] , a framework with an event-driven approach to generate customizable smart space datasets using a flexible semantic model of a smart space ecosystem incorporating spaces, people, events, and sensors.
To facilitate the definition of models, SmartSPEC, uses potentially limited input data from available sensors (e. g. , WiFi/Bluetooth), and applies ML-based algorithms to extract higher-level metamodels that characterize a smart space. Then, these metamodels leverage semantics to generate synthetic datasets to capture the occupancy of spaces, trajectories of occupants and consequent sensor observations. We also develop a structured methodology to assess the realism of the produced synthetic datasets. Our experimental results show that SmartSPEC generates realistic smart space datasets (e. g. , occupancy, trajectory, sensor data) in real-world settings at the building level/city scale. Key contributions of SmartSPEC include: • A framework to generate multiple realistic, customizable smart space datasets that uses semantics of a provided set of spaces, people, events, and sensors.
• A learning mechanism to extract semantic models and complex relationships between people, events, and trajectories from input seed data. • A plug-and-play approach to generate new synthetic sensor datasets for various types of people-activated smart space sensors that preserve the semantics of the learned models. • A methodology to assess realism of generated smart space datasets that uses well-established similarity measures and clustering techniques. • Applications and evaluations of the SmartSPEC system and approach in two real-world smart spaces (campus-and city-scale), including comparison against baselines. A key novelty of SmartSPEC that further extends the initial contributions in  [9]  is a rigorous approach to incorporate domain-specific semantics into the data generation process.
Related work In this section, we describe related literature in datasets for indoor and outdoor spaces. We also examine the process of generating synthetic sensor data, with a focus in the IoT domain. sampling, and are widely popular due to the ease of implementation. Sensor models, in general, have also seen many improvements due to the rise in machine learning: models based on regression patterns and random forests  [7, 31]  as well as Markov chains  [8, 32]  have been explored in this context. However, these approaches typically ignore semantics inherent to smart space entities, and thus are unable to properly express certain dependencies in data between people and their attended events. To address this, techniques utilizing seed-based approaches explored the effects of replication and modification of existing data.
UpSizeR  [33] , ReX  [34]  and SmartBench  [35]  utilized relationships between data attributes/semantics to scale datasets up/down while ensuring similarity to the original data wrt. specified query results. A common fault for such approaches based on directly learning or utilizing such queries is that they are limited by the initial dataset. As a result, they are unable to adapt to any many hypothetical changes in the semantics of the underlying space. Moreover, when considering all types of sensors in all potential situations, the general approach of generating synthetic sensor data by learning specific sensor models becomes infeasible in practice. In contrast to the above approaches, SmartSPEC focuses on generating realistic smart space datasets through a two step process, where we first extract higher-order properties of relevant components from readily available connectivity datasets (e.