dataesr/scientific-paragraphs-categorization huggingface dataset:
MIRA leverages joint topic modeling and RP modeling to reveal the circuitry regulating developmental trajectories MIRA leverages joint topic modeling and RP modeling of expression and chromatin accessibility in single cells to determine the regulatory mechanisms driving key fate decisions within developmental continuums (Fig. 1a -b, Methods). Probabilistic topic modeling has been employed in natural language understanding to elucidate the abstract topics that shape the meaning of a given collection of text  15. Recently, topic modeling has been applied to scRNA-seq and scATAC-seq separately to describe either transcriptional or epigenetic cell states  16, 17  as "thematic" groups of co-regulated genes or cis-regulatory elements, respectively. MIRA's topic model uses a variational autoencoder 18 approach, intersecting deep learning with probabilistic graphical models, to learn expression and accessibility topics defining each cell's identity (Extended Data Fig.
1 ). MIRA accounts for the distinct statistical properties of each modality by using different generative distributions for overdispersed scRNA-seq counts and sparse scATAC-seq data. A sparsity constraint is employed to ensure cells' topic compositions are coherent and interpretable  19. MIRA's Bayesian hyperparameter tuning scheme finds the appropriate number of topics needed to comprehensively yet non-redundantly describe each dataset. MIRA next combines the expression and accessibility topics into a joint representation used to calculate a k-nearest neighbors (KNN) graph. The KNN graph is then leveraged to construct a high fidelity lineage tree using a new method we developed to define the branch points between lineages where the probabilities of differentiating into one terminal state diverges from another (Extended Data Fig.
2 ). approach to model the transcription and chromatin accessibility topics defining each cell's identity with a sparsity constraint to ensure topics are coherent and interpretable  19. MIRA's hyperparameter tuning scheme learns the appropriate number of topics needed to comprehensively yet non-redundantly describe each dataset. MIRA next combines the expression and accessibility topics into a joint representation used to calculate a k-nearest neighbors (KNN) graph. This output can then be leveraged for visualization and clustering, construction of high fidelity lineage trajectories, and rigorous topic analysis to determine regulators driving key fate decisions at lineage branch points. b, MIRA's RP model integrates transcriptional and chromatin accessibility data at each gene locus to determine how regulatory elements surrounding each gene influence its expression.
MIRA joint topic modeling determined regulators driving key fate decisions in hair follicle differentiation Applied to hair follicle maintenance differentiation assayed by SHARE-seq 3 , MIRA's joint topic representation constructed a lineage map whose latent structure mimicked the follicle's true spatial layout  22, 23  (Fig. 2a , Extended Data Fig. 3 ). MIRA's inferred lineage tree reconstructed the ancestral hierarchy of follicular lineages, with outer root sheath cells leading to early matrix progenitors, which subsequently branched into descendant inner root sheath (IRS) followed by medulla and cortex lineages (Fig. 2b ). Accurate lineage trees are a crucial prerequisite to determining the factors directing cell fate decisions at lineage branch points. MIRA contrasts the flow of expression and accessibility topics across the inferred lineage tree using stream graphs  24  to expose the regulatory modules driving cell fates along distinct developmental paths (Fig.
MIRA captured two distinct spatiotemporal axes of differentiation in the interfollicular epidermis We next applied MIRA to a separate system in the same dataset 3 , the interfollicular epidermis (IFE). Two spatial axes of differentiation specify the IFE, one controlling the differentiation of basal stem cells into increasingly superficial epidermal layers (epidermal stratification axis) and another controlling basal cell invagination and follicular formation (follicular axis)  29  (Fig. 5a ). The latent structure of MIRA's joint topic representation again mimicked the spatial layout of this differentiation system, reconstructing the two axes of differentiation (Fig. 5b , Extended Data Fig. 6 7 ). Furthermore, unlike prior reported analysis of this dataset 3 that did not jointly model expression and accessibility, MIRA identified two distinct basal-spinous-granular trajectories.
MIRA elucidated regulators driving key fate decisions in embryonic brain development We next applied MIRA to an embryonic brain dataset (assayed on a different platform, 10x Multiome)  14  to determine the key factors driving the decision between inhibitory and excitatory neuronal fates, the balance of which is critical for normal brain development. In contrast to separate analyses of expression and accessibility, MIRA joint topic modeling constructed a high fidelity lineage tree demonstrating continuous flow from common progenitors to either astrocytes or neuronal progenitors that subsequently branched into inhibitory or excitatory neurons (Fig. 6a-b , Extended Data Fig. 9 ). MIRA's high fidelity lineage tree was crucial to precisely contrasting the dynamics of expression and accessibility across the developmental continuum (Fig.
Discussion In sum, MIRA leverages cell-level topic modeling and gene-level RP modeling to rigorously contrast the spatiotemporal dynamics of single cell transcription versus chromatin accessibility to reveal how these mechanisms interact to orchestrate key fate decisions in developmental trajectories. In each system we analyzed, MIRA demonstrated the power of joint topic modeling of expression and accessibility data to infer high fidelity lineage trees that accurately captured complex spatiotemporal axes of differentiation. Mapping expression and accessibility topics onto the joint lineage tree illuminated the key regulators driving fate decisions at pivotal lineage branch points. MIRA contrasted the dynamics of transcription and local chromatin accessibility to define the chromatin differential at each gene locus, revealing discrete gene modules regulated by primarily LITE or NITE mechanisms.
Intriguingly, in all three systems that we tested from the skin 3 and brain 14 datasets, earlier-expressed genes were enriched for LITE regulation. LITE regulation of earlier-expressed genes may reflect the importance of strict regulation requiring extensive chromatin remodeling for their expression followed by strong silencing in fates where their aberrant expression would have devastating consequences. Conversely, gene batteries important for maintaining terminal cell function were less reliant on local chromatin remodeling for their regulation, suggesting larger influence by mechanisms such as cell signaling that allow titration of transcription to fulfill fluctuating cell needs. Among NITE-regulated genes, we also noted genes with primed accessibility at lineage branch points that showed subsequent lineage-specific activation in response to a fate-defining force such as signaling, presumably via binding or activation of a factor with minimal impact on local accessibility.
Methods Complete methods available in Extended Data. developed MIRA, designed analyses, and analyzed the 10x dataset. HL and MB contributed to analysis design. XSL and CAM designed analyses and supervised the work. AWL, CVT, XSL, and CAM wrote the manuscript. AWL and CAM originated the work. All authors edited and approved the manuscript. Competing interests: MB is a consultant to and receives sponsored research support from Novartis. MB serves on the SAB of H3 Biomedicine, Kronos Bio, and GV20 Oncotherapy. XSL is a cofounder, board member, SAB member, and consultant of GV20 Oncotherapy and its subsidiaries; is a stockholder of BMY, TMO, WBA, ABT, ABBV, and JNJ; and received research funding from Takeda, Sanofi, and Novartis.
Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis; on the other hand, it involves different challenges such as intermittent sensors and integrity of acquired data. To this effect, edge computing emerges as a methodology to distribute computation among different IoT devices to analyze data locally. We present here a new methodology for imputing environmental information during the acquisition step, due to missing or otherwise out of order sensors, by distributing the computation among a variety of fixed and mobile devices.
Introduction The spreading of the Internet of things (IoT) enabled cities to become urban data sensing platforms where a huge quantity of devices, connected to the Internet, are capable of providing various information about the environment  [1]. Data collected from IoT devices can be used for different purposes. A non-exhaustive list includes: improving road infrastructure and urban traffic, understanding energy consumption patterns, and studying social and community dynamics  [2]. Data analysis is also important in the context of decision support systems, where the information is used not only to provide optimized services to citizens but also to predict their behavior and to act opportunely to ensure both security and a better quality of life. The increasing quantity of data produced by sensing devices leads to challenges regarding large-scale data analysis, privacy issues, autonomous learning and integration of noisy and heterogeneous info  [3].
Therefore, in this context edge computing is a pertinent way for distributing AI mechanisms at large-scale. Mobility is a key aspect to build a better and sustainable future. Typically, urban planning is done in a top-down fashion, where decisions are made on sampled observations of the physical environment. However, joint use of personal and mobile IoT devices with proper sensors, such as smartphones or connected vehicles, allows acquiring data in a participatory manner, thus involving citizens and their sensing devices; this has the advantage of leveraging a significant quantity of data from both ad hoc and personal sensors. However, using personal sensing devices opens new challenges concerning their temporary availability and mobility. We aim to motivate the use of Multi-Agent Systems (MAS) within EI as a key enabler to develop large-scale data analysis solutions.
The proposed MAS-based approach imputes missing environmental data due to intermittent mobile devices (e. g. , smartphones). The advantages of our proposal lie in: This paper is organized as follows: Section 2 reviews different state-of-the-art techniques that use IoT devices to pursue large-scale data analysis; Section 3 motivates the need for efficient techniques to impute missing information at large-scale; Section 4 presents our technique on both ad hoc sensors and mobile devices. Section 5 shows and discusses the obtained results; Section 6 concludes our work.
Related Work In this section, we describe different state-of-the-art edge computing solutions to perform data analysis at large-scale by using IoT. In  [7]  it was proposed to use both edge and fog computing paradigms with IoT technologies: • to facilitate the integration of new intelligent and interoperable services in automated and nonautomated buildings; • to allow the distribution of smart services among the building's subsystems. The difference between edge and fog computing lies in the place of computation: a fog environment homes intelligence at the local area network (LAN), whereas the edge computing chairs processing power and intelligence in edge devices  [8]. The proposed computing model was based on a two-level architecture, namely edge and fog layers, which interoperability allows developing new services through the information acquired from a great variety of devices (i.
e. , sensors, controllers, actuators). The edge layer contains different devices with limited computational capabilities that process data using AI algorithms. The fog layer includes computational nodes that process data in IoT gateways, server facilities, or other devices with processing, communication and storage capabilities. This latter layer can also perform the edge node functions in services with a few units. An IoT-cloud healthcare system was presented in  [9] : it automates patients' vital data collection, delivery and elaboration by Raspberry PI  [10]  as edge devices to optimize the analysis of data with little energy consumption. Moreover, an advantage of this approach is the reduction of network latency and bandwidth. This is of great interest because the number of sensors is increasing, as well as the amount of information generated, resulting in a significant amount of data traffic.
• Sensing Network-this module includes devices capable of acquiring health-related information and communicating these data through a wireless channel to the IoT-Cloud; • IoT-Cloud-this component comprises the database and edge devices. The edge devices are capable of performing data analysis tasks through AI techniques; • Graphical user interface-this element offers users simple access to information. An intelligent Edge-IoT design for smart farming was described in  [11]. That solution collects information from IoT devices to monitor cow health and state, milk production, or transport safety. The acquired data is shared through a reliable and transparent platform to provide valuable news to stakeholders, who manage activities through AI-assisted support and to customers, who know the health status of the animals.
The proposed technique includes the following sources inside barns: temperature, humidity, hazardous gas such as methane, hydrogen sulfide, ammonia and formaldehyde. An intelligent intrusion detection system that relies on IoT devices and machine learning to detect anomalous events in the incoming network traffic was described in  [12]. The proposed technique is capable of learning the dynamics of network traffic flow and identifying anomalies. Detection of anomalies is performed endogenously, that is, based on the data feedback acquired from IoT devices only. This allows one to avoid the intervention of external users. The above techniques have been applied in different, heterogeneous contexts. An important insight is pointed out: the techniques discussed can benefit from AI mechanisms to extract knowledge for use in decision making from information acquired over time.
This difficulty is mainly related to installation and maintenance costs and the use of statistical techniques proves to be efficient to fill the info gaps  [14]. In particular, imputation in statistics is the process of replacing missing data with substituted values, thus addressing the pitfalls caused by the presence of missing data  [13]. These statistical techniques have two advantages: 1. provide estimates in case sensors are not present or temporarily unavailable due to unpredictable malfunctions; 2. presence of sparse data. In this case, we suppose that working IoT devices cannot perceive the physical environment because they may not embed specific sensors (such as temperature, humidity, CO2, etc. ). Considering the properties of the physical environment is still a scientific challenge  [15] , even if many techniques to impute missing data have been developed.
Experimental Results To evaluate the correctness of the proposed technique we considered a real dataset of environmental information provided by Arpae-SIMC, the weather service of the Emilia-Romagna region in Italy which provides weather warnings to the Italian Civil Protection Department. Beyond climatological data, Arpae-SIMC provides tools, products and indicators to evaluate the impact of storms on the coast  [24]. About 48% of this region consists of plains while 27% is hilly and 25% is mountainous. We considered the average daily air temperatures at 2 meters of altitude, collected in 196 days from 8 September 2017 by 80 weather stations. The exact geographic locations of these stations were corrected to the Cartesian plane through the Mercator projection  [25]. We performed 1000 simulations, involving 80 weather stations and 20 mobile devices (corresponding to 20% of the total number of measurements).
Both weather stations and mobile devices are "agentified" as described in Section 4: the weather stations are fixed in space and are associated with agents that have a Voronoi region; the mobile devices are not associated with any Voronoi region. For each simulation, the mobile devices follow a different random trajectory. These paths have been generated by a random walk algorithm: a simple process of visiting the nodes of a graph in some random order  [26]. The walk starts at some random node and at each step; it moves daily to a neighbor of the current node chosen at random (Figure  2 ). We carried out all the experiments on a machine equipped with i7-7820HQ, 32GB RAM, and Windows 10. With such a configuration, imputing a value requires less than a second. We developed the proposed approach in MATLAB language without any particular agent-based technology.
Conclusions and Future Perspectives A growing number of AI solutions deal with huge quantities of data generated from IoT sensors with the aim of collecting, processing, and analyzing data. Edge computing is an emerging computational paradigm capable of dealing with such challenges by distributing the computation among the available IoT devices, which are more and more powerful and low-cost. Our goal is to provide an efficient and simple way to address the objective of edge computing through the use of intelligent agents associated with IoT devices. MAS lets our approach be scaled horizontally by adding new sensing devices without any particular reconfiguration. Moreover, the local computation by the agents allows for the reduction of bottlenecks in network transmissions. In this paper, we showed how MAS and IoT can be coupled to impute missing values at a large-scale through intermittent mobile devices.
However, the success of marker-assisted selection was limited by the great distance between the causal mutations and genetic markers. Thus, even though advancements made over time were very important to ensure genetic progress, the use of genomic selection  (Meuwissen et al. , 2001)  allowed rapid genetic progress in dairy cattle  (Schaeffer, 2006). Recently, the genomic relationship matrix  (VanRaden, 2008)  has been combined with the traditional pedigree-based relationship matrix, in order to create a hybrid matrix that is used to simultaneously generate breeding values for genotyped and nongenotyped animals, a method known as single-step GBLUP (ssGBLUP,  Misztal et al. , 2009; Aguilar et al. , 2010) , which has become a gold standard for genomic evaluations in livestock. Current research efforts have focused on developing and testing more computationally efficient algorithms and methods to facilitate the implementation of ssGBLUP in large datasets (e.
, 2019). Several strategies have been sought to overcome these issues. First, breeding schemes can consider GxE in genetic and genomic evaluations to identify the most suitable genotypes for each condition. This can be done through the use of reaction norms using routinely recorded datasets such as milk yield and climatic variables or direct indicators of resilience and welfare  (Berghof et al. , 2019; Brito et al. , 2020b). Different tools and approaches can be adopted to collect phenotypes to be used for genetic selection. For instance, precision technologies (e. g. , activity sensors, feeding behavior recorders, automated milking robots, computer vision) can generate a wealth of data to maximize genetic progress for traits related to resilience and welfare, as reviewed by  Berghof et al.
As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for? ", "What would the ideal phantom for validation fiber tractography look like? " and "What phantoms, phantom datasets and tools used for their creation are available to the research community? ". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.
Furthermore, it should have realistic magnetic properties such as surface relaxivity (relevant to material wettability) or magnetic susceptibility (relevant to phantom materials). These characteristics are partially satisfied by existing phantoms which will be covered in this review. Though the current phantoms have inherent issues and drawbacks, these serve as a valuable reference system for studying the limitation and accuracy of tractography algorithms. This manuscript provides an overview of the methodologies, datasets and use-cases of the physical and digital phantoms for validating tractography and artifact removal pipelines. We will discuss the characteristics of these phantoms and how they can serve as potential ground-truth for the validation process. Sections 2 and 3 introduce the two types of phantoms (physical and digital respectively) and the cases where one serves as a better ground-truth than the other will also be discussed along with the gaps between the existing phantoms and the ideal phantom.
Sections 2 and 3 also review the tools available for developing the phantoms and the datasets available for evaluating the tractography that can provide the tractography community an opportunity to validate and compare various aspects of the connectome estimation process ( Fig. 1  ). Furthermore, we address the artifacts that could be represented by these phantoms and their potential use for evaluating the dMRI processing pipelines in Section 3. Ideally, these pipelines should remove the artifacts (e. g. thermal noise, eddy-current artifacts) that adversely affect the true water-diffusion signal, restoring the quality and integrity of dMRI data. Section 4 provides the future directions for phantom generation and tractography evaluation based on the limitations and challenges discovered by the validation studies.
The "Fiber Cup phantom " was built to meet the needs of the "Fiber Cup" tractography challenge held in London in the frame of the MIC-CAI conference. This physical phantom was designed at NeuroSpin made from 17 m diameter acrylic fibres immersed in pure water  ( Fillard et al. , 2011 ). It mimics a coronal section of a human brain, depicts both long deep and superficial U-fiber bundles and embeds various complex fiber configurations including 2 crossings at 90°and 69. 35°, 2 splittings at 45°and 24. 35°, 1 kissing between a rectilinear fiber bundle and a semi-circular fiber bundle ( Fig. 8  ). The fiber density was close to 1900 fibres/mm 2. Diffusion MRI dataset were acquired using a Tim Trio 3T MRI system (Siemens, Erlangen) and made available at various spatial resolutions (3 and 6 mm isotropic) and b-values (600, 1500 and 2000 s/mm 2 ).