dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation The serosim package is written in the R programming language. Package dependencies are ggplot2  [26] , data. table  [27] , tidyverse  [28] , patchwork  [29]  and reshape2  [30]. All code is publicly available on GitHub (https://github. com/AMenezes97/serosim). For the following sections describing the code, typewriter font refers to function arguments while bold font refers to R functions. The core of serosim is contained within the runserosim function, where users specify each of the required model inputs (S1 Table ). This function must take a number of default arguments described in S1 Table, but additional optional arguments may also be provided for user-written functions. This gives the user flexibility to tailor their own settings, models and parameters to their population and disease system(s) of interest.
Users are not constrained to data structures of particular dimensions or types, and thus all of the model components can be extended to any desired level of complexity. The serosim package includes tools to help the user generate the necessary inputs for the runserosim function, discussed below in their respective sections. Additionally, there are some ready-to-use model functions built into the serosim package for convenience (S4-8 Tables). If any of these model functions meet the needs of the user's disease system and simulation model, the user simply has to specify the desired function as its corresponding argument within the runserosim function. These ready-to-use functions also provide a helpful framework if users wish to create their own function or make modifications to an existing function.
The following subsections 1. 1-1. 7 step through the process of building and specifying the required inputs for a simulation presented in the same order as Figure  3. We have also integrated the README example available within the package within this description. In order to build a simulation with serosim, users must follow the 7 steps outlined here and in the methods section to specify the required inputs and models for runserosim. Steps 1-3 specify initial simulation inputs while steps 4-7 specify the bulk of the unobserved and observed processes. For steps 4-7, we outline the user-specified inputs in the left column which are used for the user-specified models as depicted by the sampling statements in the middle column. Lastly, the generated outputs produced once the simulation is complete are depicted in the right column.
Case studies The serosim package contains two example case study vignettes to illustrate how to use serosim and how to structure the required inputs for runserosim. Case study 1 provides an example of a longitudinal singular biomarker serological survey simulation structured around measles, a one-pathogen system with vaccination, but also applicable to other vaccine preventable diseases. Case study 2 provides an example of a cross-sectional multi-biomarker serological survey structured around diphtheria and pertussis, a two-pathogen system with bivalent vaccination, but also applicable to multi-pathogen systems with multivalent vaccines. Figure  6  provides run times for simulations of varying complexity by adjusting the number of individuals for the README example and 2 case studies. serosim is not limited to either of these scenarios and we hope it will have applications in other systems including wildlife pathogens  [20, 36].
Conclusion, limitations and future directions The serosim package was designed to simulate serological data under different survey designs and underlying immunoepidemiological processes while also providing a systematic framework for validating inference methods. Many epidemiological studies using serological data rely on pre-defined definitions of seroconversion, seropositivity or antibody kinetics to draw inferences about the underlying epidemiology of a pathogen  [37]. However, such studies often use real-world datasets without first considering the biases that may arise when the fitted model differs substantially from the true data-generating process. Simulation studies are useful for identifying the limitations, biases and accuracies arising from sample variability, misspecified models or neglected variables  [16, [38] [39] [40].
serosim is a generalized approach to this problem, allowing researchers to simulate serological data representing a disease system of interest by explicitly specifying the within-host processes, patterns of infection and vaccination, population demography and other factors which determine observed biomarker quantities. By generating simulations within a single framework, we anticipate better generalization and comparison of methods between pathogen systems and serologic assays  [1, 14, 41]. A key use case for serosim is for researchers and public health officials to improve serological survey design. We envision that serosim can be used to simulate datasets with different sample sizes, spatial distribution of samples, and sampling frequencies, providing a tool for power calculations. There are a number of existing frameworks for evaluating survey designs based on seroprevalence estimates, taking into account both the contribution of imperfect sensitivity, specificity and model variability to parameter estimation uncertainty  [16, 42].
However, few tools explicitly consider the full complexity of the underlying biology and epidemiology which determine an observed sample of antibody titers  [40]. By separating each step of this generative process into distinct modules, serosim requires users to account for sources of measurement error, variation and bias that cause deviation from the underlying parameters of interest. With this generative model in place, inference models (e. g. , estimating force of infection using a serocatalytic model  [12]  or estimating time-since-infection using antibody waning estimates  [35] ) can be applied to simulated datasets of different sizes and sampling designs to assess their power in recovering ground-truth parameters. These so-called "simulation-recovery" experiments are especially useful as the model being fitted will be, by definition, a misspecified version of the true generative model (with parallels to statistical model misspecification  [43] ).
Designing studies using these simulated datasets also provides interim data with which researchers can develop their inference methods as data collection progresses. Although we have structured the serosim model and code in a flexible, generalized framework, there are limitations to its ease of application to some systems. Some parts of the generative process are very poorly understood for some pathogens (e. g. , correlates of protection for the immunity model may be complex  [44] ; the relationship between time-since-infection and multiple biomarker quantities may be highly variable  [45] ), and the relationships between exposure and measurements of some modern multiplex serological assays are not yet precisely characterized  [25]. We have included a range of ready-to-use functions to encompass various epidemiological and immunological contexts here (S4-8 Tables), however, this is not an exhaustive list as we encourage users to construct new model inputs to match their desired system (e.
g. , constructing their own antibody kinetics model). There is also a trade-off between framework flexibility and computational speed. Structuring serosim as distinct modules places responsibility on the user to ensure that custom code is fast enough to not substantially bottleneck the simulation, particularly for systems with a large number of exposure types, biomarkers, or individuals. However, given that serosim is not intended for integration in multi-iteration code (e. g. , nested within a Markov chain Monte Carlo algorithm), speed is unlikely to be an issue for most use cases. Part of the motivation behind serosim is to support the rise in development of complex models relying on quantitative antibody measurements as opposed to simple binary serostatus data. These models explicitly describe the relationship between antibody titer and time-sinceinfection, often estimated using longitudinal data, and then use this relationship to back-calculate seroincidence from cross-sectional data  [13, 14, 35, [46] [47] [48].
Fast fourier transform and power-spectrum density calculation All collagen images were computed in MATLAB (version 9. 12. 0. 1884302, The MathWorks, Inc. , Natick, Massachusetts, United States). Two-dimensional Fast Fourier Transform (2D FFT) was performed for each AFM image taken. Images were converted to the frequency domain by means of MATLAB's fft2 function, which returns the two-dimensional discrete Fourier transform of an image size M × N pixels using a fast Fourier transform algorithm (Eq. 1): where x and y represent the spatial coordinates of the image, u and v are the frequency components in the x and y directions, and i = √ -1  24. The frequencies represent changes in the pixel intensities. While the Fourier Transform is often complex and contains real parts Re(u,v) and imaginary parts Im(u,v), the conversion of the Fourier transform into a power spectrum without imaginary parts generates the PSD (Eq.
Advanced image analyses of collagen ultrastructure All 5 × 5µm images (512 px) were segmented into 16 macro-pixel of 1. 25 × 1. 25µm (128px) using ImageJ software (version 1. 53). Next, we perform the 2D FFT analysis for each macro-pixel. On each of the resultant 2D FFT images of the macro pixel, an orientation vector was orientated through the center of the two first harmonic arcs to establish the mean direction of the collagen fibrils in the area used to compute the 2D FFT. Phantoms AFM images of collagen fibrils were created with defined fibrillar alignment to control the level of structural anisotropy in the field of view. These phantoms were created from an AFM image by duplicating the same fibrils to form a stack of fibrils in different orientations using Image J. One singular collagen fiber, which displays a clear D-banding periodicity, was cropped to create the desired images.
D-banding measurements using FFT and PSD In tissues such as skin, type I fibrillar collagen is readily identified by its repeating banding pattern, D-banding periodicity of 64-67nm along the fibril's length, arising from the careful staggering of collagen molecules (300 nm long, 1. 5 nm wide)  41, 42. Measuring the D-banding periodicity has been carried out using multiple approaches involving Transmission Electron Microscopy  [43] [44] [45]  , Scanning Electron Microscopy  [46] [47] [48]  , or Atomic Force Microscopy 31,49-52. While it is relatively trivial to measure the D-banding periodicity along a single isolated collagen fibril, it can be challenging to measure the average D-banding period over a broader area in which multiple fibrils are present, overlap and intersect. To facilitate this process, we developed a MATLAB code to automatically measure the average collagen D-banding of all fibrils in an AFM image and compare its outcome to the manual method.
An example of this approach is presented in Fig. 3  and relies on optimizing the intensity of the first harmonic peak present in the PSD by rotating the image. The mean value obtained for the wild-type mice skin collagen was (64 ± 2) µm when measure computationally and (63. 3 ± 1) µm when calculated manually. For Col1a1 Jrt/+ mice (63. 2 ± 1) µm was determined computationally and (63. 5 ± 1)µm manually. Statistical analysis by one-way ANOVA of obtained values showed no significance between code employed or manually acquired. These measurements follow previous FFT and manual measurements for collagen type I D-banding  16, 34, 35, 53, 54. It is, however, interesting to note that measuring the D-banding periodicity of ten fibrils manually yields the same D-banding value as that computed for the whole image.
Our contribution We propose a model-based framework for investigating long-term sustainability in urban system deployments. Our response to (RQ1) relies on a novel green heterogeneous computing paradigm called GEN-ESIS, which has been validated in a small laboratory demonstration. As a result, it aims to meet the energy and digital service requirements of urban systems. In Fig. 1 , an example of its typical deployment in a city environment is shown. The heterogeneous nature of our solution stems from the combination of a large number of green nodes with a small number of in-door mini data centers (1-10 racks of servers within 1-25 square meters of compute space  [7] ) in order to create a city-level data center. Mini data centers (Fig. 1  bottom-right) are housed in secure facilities. There is a connection between them and the power grid.
The green nature of the solution comes from the usage of specific nodes. Such nodes may combine in the same module: a server, a renewable energy harvester, local energy storage, and custom programmable power electronics systems handling energy transfer between interconnected green nodes  [8]. In particular, we consider solar panels and batteries for energy harvesting and storage. All of the electronic equipment, including servers and batteries, is located on the roof,  1  as shown in Fig. 1  (top-right). They are installed just behind the solar panels to take advantage of passive cooling via appropriate packaging that ensures secure isolation. The data processing requirements of urban areas are met by using the most local computing resources, preferably green nodes to take advantage of the availability of free renewable energy.
middle-right). To investigate the impact of a large-scale city-wide deployment of GENESIS, we have developed and implemented a simulation tool that allows evaluation, for example, of local energy consumption. It is intended to answer (RQ2). As part of the defined tool, a Python implementation of several functionalities is orchestrated through a workflow: processing census and topographic data in alphanumeric form, retrieval and processing of solar ephemeral data necessary to calculate the solar radiation rate, and integration and mapping of models of energy consumption and supply. Our tool typically generates maps of urban areas (e. g. , see left-hand side of Fig. 1 ) with estimates of energy production and consumption over time in accordance with local weather conditions. It uses data from public city databases as input for the sake of relevance.
The tool is partially calibrated with data obtained from the GENESIS demonstrator, such as computing resource dimensioning, energy harvesting and storage resource sizing, and energy transfer efficiency. It tool is therefore a promising decision-making tool that is essential in city governance policies. Several GENESIS deployment scenarios are examined as proofof-concept in two different French cities: Nantes and Montpellier. Both cities differ in terms of their urban layout, building types, and weather conditions. From an energy perspective, we analyze whether such deployments are sustainable in relation to typical digital activities in each examined city district. We discuss several relevant perspectives opened by the current work for sustainable urban systems.
Demand is primarily determined by the building typology (construction period, materials, construction size, nature and type of openings, etc. ), regardless of the actual number of residents or users. A database of ISTAT data is used to estimate the energy consumption of buildings based on their architectural characteristics. Contrary to our modeling approach,  [25]  does not take into account any detail about key parameters such as the number of inhabitants and their activity modes or usages, which are necessary for addressing more realistic urban scenarios. On the other hand,  [24]  does not consider advanced technologies, such as GENESIS, which aims at combining the usual energy demand with the high demand for computing resources and data storage in urban environments.
Software part The aforementioned technology is managed by a dedicated software infrastructure. The software stack consists of a full set of tools enabling the use of green computing nodes in conjunction with mini data centers. The ultimate objective of the infrastructure is to optimize overall system behavior in terms of operating costs and the quality of service provided to end users. Technically, this involves the implementation and integration of the following components. A database is used by the monitoring component to retrieve information about the operation of the deployed hardware computing system. In order to optimize workload execution and energy allocation in the system, the optimization component exploits some heuristics. The resource allocation problem is formulated and solved using mixed integer linear programming (MILP)  [26]  in conjunction with a rolling horizon heuristic  [27].
Nevertheless, if the conveyed energy has high transfer losses, the utility grid may be used to provide the demanding node with energy. The integration of all the aforementioned modules with the Open-Stack infrastructure  [28]  allows for the smooth and transparent management of the virtual machines (VMs) running on the various green nodes and remote indoor mini data centers. OpenStack is a popular and active open-source cloud computing infrastructure software project. It enables us to handle job migration across a heterogeneous system, such as between supported green nodes and mini data centers. The considered resource allocation problem in our target distributed system takes advantage of both data (or virtual machines) and energy migrations across green nodes. A major goal is to reduce the amount of energy drawn from the utility grid as much as possible.
Modeling of GENESIS urban deployment We are now able to evaluate the relevance of GENESIS in actual urban areas based on its hardware and software capabilities. We seek to develop a model that compares the combined supply of digital and energy with the demand for digital in buildings. First, it is necessary to quantify the territory's capacity for hosting GENESIS and, consequently, the amount of energy available for digital needs. In addition, we would like to measure the amount of energy that is consumed by residents of the territory in order to run their information technology devices. The spatialization of our model, i. e. anchoring it on a given territory, requires geospecific or geotypical data. Three types of data are available. They must first allow us to characterize the solar deposit on the surfaces or roofs on which the photovoltaic panels will be installed.
Mapping of GENESIS to energy demand As described in the previous sections, all of the processing is described in the workflow shown in Fig. 6 , which is written in Python. This relies on the integration of various libraries, including Pandas  [29]  for processing alphanumeric census data; shapely  [30]  and GeoPandas  [31]  for processing topographic data; Pysolar  [32]  for retrieving and processing the solar ephemeral required for solar radiation calculations, and t4gpd  [33]  for integrating and mapping the energy consumption and supply models. Additionally, the use of the NumPy  [34]  library provides an extremely efficient way of calculating energy usage. In our simulator, for example, we have translated Eq. (  3 ) as dot(matmul(D, multiply(S, C)), E), using the operators imported from NumPy.
Fine-grain analysis of deployments By focusing on specific GENESIS deployments, a previous analysis can be further refined. We can examine the evolution of the utility grid energy demand over time in relation to three relevant variables: the energy supplied by GENESIS solar panels, the accumulated battery charge, and the energy required for IT equipment. Fig. 10  illustrates two district-level deployments of GENESIS in Montpellier and Nantes. For Pas du Loup (8 city blocks in Montpellier) and Madeleine (33 city blocks in Nantes) districts, simulation times are respectively 214 s and 255 s, using the same desktop PC as previously. For the former district, the scenario consists of 2294 green nodes that provide 50% coverage of ribbons. Among these nodes are 1147 modules each containing a 3 kWh battery.
Some preliminary insights A case study such as the one presented above illustrates how an edge computing/smart grid solution such as GENESIS can fill the energy demands of urban digital activities in theory. Two very different districts Fig. 10. Evolution of the estimated energy collected by all GENESIS nodes (in green), the estimated Wh consumed by IT equipment (in dotted red), the charging rate of the batteries embedded in the nodes (in blue), and the demand for additional energy from the utility grid (in black) over a given week (in winter, spring, and summer). of the French cities of Montpellier and Nantes have been examined. Specifically, our simulation tool uses a geographic information system (GIS) to map the digital power demand of a neighborhood against the energy harvesting capabilities of the distributed GENESIS modules deployed within the neighborhood.
This can result in the activation of the system's integrated fans in extreme temperatures. Green nodes use generic integration interfaces that support any computer motherboard, including those found in server-class systems, personal computers, and embedded boards. Sound emissions vary depending on the options selected, but they must be reasonable to enable GENESIS to be used in a manned environment. When deploying technology like GENESIS, it is imperative to take into account the visual transformation of urban ecosystems as well as sound emissions. The current prototype module, called the green node, is quite small and resembles a typical photovoltaic panel. Furthermore, we believe that the visual presence of these modules raises awareness of the existence of infrastructures related to city dwellers' digital activities and their environmental impact.
Thus, from this perspective, it seems worthwhile to make the system visible, just as the city makes its energy distribution and waste management visible. Electricity providers such as Enedis in France may be able to utilize GENESIS by integrating its harvested green energy with other sources of energy. The ability of GENESIS to inject harvested energy into the utility grid along with its smart energy routing mechanism facilitates this integration. Integrating GENESIS into an urban ecosystem also involves managing the heat generated by the system's photovoltaic panels, computing systems, and energy management systems. In the current small-scale prototype, heat is dissipated into the ambient air by means of controlled fans when necessary. As a result, the module may appear to be a thermal polluter similar to heat pumps installed for air conditioning.
There are a number of ways in which this heat can be recovered. Based on the model of the digital heaters promoted by the Qarnot 7 https://qarnot. com/ (Accessed March 2022). Company  [36] , we can imagine using the heat dissipated by GENESIS to contribute to building heating, assuming the evolution of GENESIS' technical infrastructure. Incorporating GENESIS modules into urban agriculture structures installed on city roofs is another avenue to pursue. In this case, the heat dissipated by GENESIS could directly be used for crop cultivation.
We fit halo-based SFR models to our data and compare the recovered SFR with direct measurements of this quantity. We find a qualitatively good agreement between both independent datasets, although the details depend on the specific halo model assumed. This constitutes a useful robustness test for the physical interpretation of the CIB, and reinforces the role of CIB maps as valuable astrophysical probes of the large-scale structure. We report our measurements of SFR as well as a thorough account of their statistical uncertainties, which can be used to constrain star-formation models in combination with other data.