dataesr/scientific-paragraphs-categorization huggingface dataset:
5d ) in the image series (Fig. 5a, b ). The swelling appears between the 5th and the 7th frames. In order to improve the frame rate of 2D+time STED imaging while limiting the photodamages on mitochondria, one may adjust the STED imaging protocols (Supplementary Note 1). Accordingly, we built another live STED dataset, first to give an indication on how long, with STED acquisition in lower depletion conditions, we can image before the mitochondria are damaged and second, to control the efficiency of DeepCristae restoration over time. In what follows, these low-resolution (LR) STED images were referred to as Fast STED images. This goes at the depend of xy resolution (Fig. 5c , left bottom triangles in image series) for both the lateral width of mitochondria and the cristae width (Fig. 5d , Fast STED and Fig.
DeepCristae restore 3D+time images of mitochondria cristae by using intermediate high-resolution and diffraction limited microscopy STED nanoscopy is not the only high-resolution microscopy adapted to resolve internal mitochondria ultrastructures in live cells. Indeed, a number of works using adaptation of SIM approaches have been published over the last few years  16, 42  , some combined with conventional deep learning methods  20, 22, 39. Yet, the best compromise between Fast and 3D imaging still remains an issue. We next investigated the performance of DeepCristae prediction on intermediate HR microscopies chosen for their 3D optical slicing performance. Spinning disk confocal equipped with a Live-SR module (or SDSRM for Spinning Disk Super Resolution Microscopy) is one of those well-disseminated systems equivalent to SIM.
9 s for Live-SR. DeepCristae restoration was also tested with an AiryScan 5 LSM 980. It provided similar improvements, although for a 15 planes stack time of about 30s and with more artifacts appearing after DeepCristae, the nature of which most probably lies in the way the reconstruction of the AiryScan images was carried out from the values determined automatically by the commercial software (Supplementary Fig. 5 ).
This can be critical, if one wants to study interorganelle membrane interactions and their effects. We next initiate the investigation of endosome/lysosome-mitochondria dynamic interactions by addressing specifically the ultrastructural behavior of the cristae during these contacts. This was done by imaging multiple 3D+time double fluorescence series in Live-SR (represented as a single stack MIP in Fig. 7a , top left) or LLSM (represented as a single stack MIP in Fig. 7c , top left), where the membranes of the endo-lysosomal pathway were continuously labeled (Supplementary Note 1. 1). DeepCristae restoration was applied on both MIP datasets (respectively, Fig. 7 a, c , bottom left). A number of mitochondria dynamic events correlated with endosomal structure behaviors were captured. Only a few of them are here extracted as thumbnail time series (Fig.
7b, d ). While cristae resolution in LLSM does not reach that obtained with Live-SR, DeepCristae restoration brings values closer together (Fig. 6b, d ). In all situations and for both intermediate HR (Live-SR) and diffracted limited (LLSM) imaging modalities, DeepCristae restoration provides ultrastructural information on the positioning, density, and dynamics of mitochondria cristae. We then wanted to quantitatively assess how the dynamic architecture of the mitochondria internal membrane during endo/lysosomesmitochondria interaction could be revealed with DeepCristae. We focused on the fission process. To do so, we first selected 21 distinct 3D+time image series from the Live-SR datasets, in which mitochondria fission was monitored. Intensity line plots were measured along mitochondria on some time points framing the fission event.
While it has been trained on a dedicated dataset that was created from real high-resolution 2D STED images, we have shown that it operates for a wide range of optical resolutions, from diffraction-limited to intermediate high-resolution microscopy, providing researchers with a powerful tool to study cristae dynamics without compromising their structural integrity or functionality. While there are other deep learning approaches available for revealing cristae ultrastructure  21, 22, 38, 39  , DeepCristae offers unprecedented advantages. First, thanks to a welldefined training loss dedicated to the restoration of mitochondria signals, it outperforms stateof-the-art methods. Secondly, it not only makes it possible to visualize and restore cristae dynamics in 2D STED nanoscopy with minimal illumination and without damaging mitochondria but more importantly, it extends these capabilities to other high-resolution imaging techniques such as Live-SR, SIM and AiryScan, more suited to such 3D dynamics.
In this respect, DeepCristae might be an asset to compare the cristae dynamics in different cell types and in these various conditions. However, as with any image restoration method, scientists may be concerned by the robustness of DeepCristae to accurately restore mitochondria cristae and not hallucinate them. This is why we investigated the stability and limits of our method. First, we consider the RL algorithm in our proof-of-concept as it may be considered as the baseline algorithm for image deconvolution in fluorescence microscopy. The behavior of this algorithm is well established, including the "night sky" artifacts when iterated. The GTs shown in the manuscript may be improved if we consider more sophisticated deconvolution algorithms. We also worked out different conditions of use to be respected to guarantee good quality and truthfulness of the results.
Second, as our goal is to learn how to restore LR images to better reveal the mitochondria cristae, we paired the LR STED images 𝑆𝑆 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐿𝐿𝐿𝐿 with their restored counterpart 𝑆𝑆 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐺𝐺𝐺𝐺. These ground truths are obtained by deconvolving the real HR images 𝑆𝑆 𝐻𝐻𝐻𝐻 with the Richardson-Lucy algorithm  29, 30  , enhancing the mitochondria cristae. Other deconvolution algorithms were tried, such as SPITFIR(e)  32  or Wiener  31  , but the results obtained after training were not as good. Finally, the dataset 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 is made of 24 training images and 9 test images. To further increase the size of the training set, data augmentation and patch sampling (later described in Methods) are performed on the pair of LR images 𝑆𝑆 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐿𝐿𝐿𝐿 and ground truths 𝑆𝑆 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐺𝐺𝐺𝐺. The dataset is first augmented by applying three different rotations to the images (90°, 180° and 270°).
Then, a shrink transform, and horizontal and vertical flips are successively applied to 25% of the augmented dataset, randomly selected. The final training set is made of 1824 patches of size 128×128 pixels, whose 20% are used for the validation set (summary in Supplementary Table  1 ). Network architecture. We used the network proposed by Weigert et al. 19  as the backbone of the CNN architecture, itself built upon the U-Net  55. It has a contracting path and an expansive path, each one consisting of 3 sequential downsampling and upsampling blocks, respectively. Each block of the first path is skip-connected to the associated one of the expansive paths. The contracting path consists of two successive 3×3 convolutions, each followed by a Rectified Linear Unit (ReLU), and a 2×2 max pooling operation with stride 2 for downsampling.
Acknowledgements This work was supported by the  France-BioImaging Infrastructure (French National Research Agency ,  ANR-10-INBS-04-07 , " Investments for the future ") and the  Labex Cell(n)Scale  ( ANR-11-LABX-0038 ) as part of the  Idex PSL  ( ANR-10-IDEX-0001-02 ). We acknowledge the  Cell and Tissue Imaging (PICT IBiSA, Institut Curie)  and the  IMACHEM (Collège de France)  platforms, also members of the national infrastructure  France-BioImaging  ( ANR-10-INBS-04-01 ) for access to and maintaining the spinning-disk, Airyscan and STED microscopes. We also wish to thank  M. Maurin  from  Inserm U932  for his help in a preliminary study on STED image acquisition.
Methods In this section, we present the main features of DeepCristae. We first present the dataset we created from real 2D STED images to train the network. Then, we overview our network architecture and present the novel learning loss function, which prioritizes the restoration of specific pixels. We finally detail the image patch-sampling steps, which is a crucial stage in the model pipeline. Generation of the 2D STED dataset -𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠. As mitochondria are living organelles, mostly organized as a quite fast-moving network in RPE1 cells (Supplementary Note 1. 1), the acquisition of a pair of high and low-resolution images at the exact same time point is impossible. To train and quantitatively validate DeepCristae, we thus created a dataset, called 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 , from 33 acquired high resolution (HR) 2D STED images (25 x 25 nm) that we denote 𝑆𝑆 𝐻𝐻𝐻𝐻 (Fig.
patch sampling for the training step. Our model is trained on the dataset 𝐷𝐷 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 containing 24 images (96 after data augmentation) of different sizes. In order to homogenize and increase the training dataset, we performed patch sampling. We sampled each input training image 𝑆𝑆 ∈ 𝑁𝑁 𝑊𝑊×𝐿𝐿 , defined over the grid 𝛺𝛺 of size 𝑊𝑊 × 𝐿𝐿, within 𝑁𝑁 𝐼𝐼 = �.
The electrical distance between VPPs is as in Table  2. • Fast-ADMM convergence criterion is ξ = 1e -3. Programming and model solving: The proposed GNB-based model and algorithm are modeled in the Yalmip environment in Matlab R2020b. The solver uses for Subproblem 1 is Gurobi with an academic license and IPOPT for Subproblem 2.
2000; Nentwig et al. 2018). However, although assessment methods on which these lists are based generally mention the costs caused by invasions, the lack of broad-scale cost information, and the flaws and untested assumptions of many local and regional cost studies  (Bradshaw et al. 2016; Diagne et al. 2021 ) are major drawbacks. While broad-scale economic cost estimates had been made previously, they are often based on unsupported extrapolations, unrealistic assumptions or undocumented sources  (Pimentel et al. 2000 (Pimentel et al. , 2001 (Pimentel et al. , 2005;; Kettunen et al. 2009; Pimentel 2011). Fortunately, structured and reproducible syntheses of monetary impacts from biological invasions have gained traction recently through the InvaCost database  (Diagne et al. 2020; Leroy et al.
InvaCost has started a procedure to measure and report the reproducibility of each cost record; however, it is a laborious process for the more than 13,000 records currently reported in the database. The onus is therefore on cost reporters to be vigilant, detailed, and authentic. To demonstrate these issues, consider the four major papers that provided cost estimates of invasive species at a global, national, or regional scale  (Pimentel et al. 2000 (Pimentel et al. , 2001 (Pimentel et al. , 2005;; Pimentel 2011 ) prior to the advent of the InvaCost database. These four sources, updated in succession, have consistently served as primary references in invasion cost-related studies, to date receiving several thousands of citations, underpinning other influential studies. Despite these acknowledged limitations, values from these publications are still cited today without recognition of the limitations embedded.
Due to the lack of precise cost estimates, flawed syntheses became widely cited and led to unsubstantiated values, which in turn provided ammunition for denialists to challenge the legitimacy of the field. Given this background and limitations in existing cost estimates, there was a clear need for improved calibration and synthesis of the economic costs of biological invasions worldwide, and most importantly including traceability of sources for costs. This need resulted in the creation of an interdisciplinary team of experts from ecology, economy and invasion sciences starting in 2014 (invacost. fr), who together contributed to the public release of the InvaCost database  (Diagne et al. 2020).
Evolution of the InvaCost database InvaCost is a global compilation of available estimates of the costs associated with invasive species and largely fills these gaps. Estimates in InvaCost are derived largely from peer-reviewed scientific articles and published grey literature, with particular attention paid to reproducibility and data descriptors. The original InvaCost database ('v0') only included data related to the damage and management costs incurred by invasive insects, containing 260 entries (i. e. , rows of data, each including a cost estimate) at a global scale  (Bradshaw et al. 2016). The first officially released version of InvaCost (v1. 0) had grown to 2419 entries by September 2020 (doi:10. 6084/m9. figshare. 12668570. v1). The next publicly released version (v3. 0) included 9823 entries and an associated complementary database consisting mainly of references not yet processed, as well as a database compiling data from 10 non-English languages  (Angulo et al.
2021b). In June 2021, the number of entries increased to 13123 (v4. 0), with the most-recent version (v4. 1, January 2022) of InvaCost contains 13553 entries and compiles literature in 22 languages  (Diagne et al. 2022; Kourantidou et al. 2022a). Moreover, the InvaCost project now includes a 'living figure' that uses the latest database version to represent the most up-to-date cost breakdowns across geographic and taxonomic contexts graphically  (Diagne et al. 2021; Leroy et al. 2021). The ongoing growth in cost information inevitably modified summaries of cost trends reported over time (Fig. 1 ), including number of database entries (Fig. 1a ), number of species  (Fig 1b) , and annual costs (Fig. 1c ). These trends each display a similar dynamic, characterised by a rapid accrual of the number of documents reporting costs, number of species reported as the cause, and the costs themselves, followed by an apparent decline in the most recent years.
Capturing the multi-faceted nature of invasion costs There are large differences in how agencies assess the costs of biological invasions, reflecting differing, and sometimes competing, interests at local, regional, and national scales. The earlier attempts on reporting the costs of invasive species focused primarily on the United States (e. g. ,  Pimentel et al. 2000 Pimentel et al. , 2005;; Pimentel 2011 ), whereas elsewhere, cost estimates were also provided for several other nations (including Australia, Brazil, India, South Africa, United Kingdom)  (Pimentel et al. 2001)  and European countries  (Kettunen et al. 2009 ). These studies were constrained not only by geography, but also by taxonomic coverage, targeted sectors and habitats. Recognising and addressing such limitations, 23 publications that have used the InvaCost database have provided cost assessments on national, regional or continental scales (Fig.
A focus of invasion biology research is to raise awareness about the economic costs of biological invasions and to compel policy makers to increase focus on these species. Since its conception in 2014, scientific publications based on the InvaCost database have been cited in several notable policy documents. To date, 10 InvaCost studies spanning 5 thematic categories have been cited in at least 8 policy documents or governmental reports. A few prominent examples are (see Supplementary Information Table  S2 for   Environmental Management for the Seas of East Asia 2021), is focused on the regional state of oceans and coasts. That report stressed the severe economic impacts of aquatic invasive alien species on native fisheries, agricultural productivity, public utility operations, property values, tourism, and outdoor recreation, as well as the global expenditure of US$345 billion associated with their control  (Cuthbert et al.
it; vespavelutina. eu). Therefore, public engagement about the costliest species could improve their monitoring, reporting and allow for more effective management while reducing future potential costs. Leroy et al. 2021). Here, we outline further developments that are currently in the process for the launch of InvaCost 5. 0. The maintenance of an up-to-date database ensures ongoing access to the most recent information, but also requires the development of assistance to the users so that they can appreciate the full content and diversity of data descriptors. Improved data accessibility can assist potential users and provide a baseline for environmental decision-making. By developing dashboards where data can be visualised interactively, users can obtain their desired information quickly without requiring programming skills.
The development of open-source projects like InvaCost should ideally include tools facilitating data exploration such as shiny, an R package to build interactive web interfaces for data visualisation. InvaCost members are now developing such apps that will assist in creating a more interactive environment for non-experts. At the same time, expertise from additional disciplines has been added to InvaCost. Previous versions of the InvaCost database were assembled primarily by natural scientists, but recently social scientists (e. g. , resource economists) have been working to strengthen the economic dimensions associated with the cost data collected. These new efforts encompass a more detailed analysis of the methods used for the estimation of costs in every study, that will ultimately allow for an assessment of the methodology used.
The need for a more granular analysis of the methods for every cost entry has arisen from the need to understand whether and to what degree the cited study is reproducible and/or reliable (Box 1). Because the sources of costs vary (e. g. , in terms of quality, estimation methods, nature of costs estimated), this analysis will provide a qualitative indicator of each of the entries in the database and will help to better specify the evaluation methods used and the costs assessed. Beyond this, the InvaCost database will continuously be developed to standardise the methods of classification so that future contributors can identify these different cost characteristics for every study.
2021b ). However, while information in 21 languages has been added, there remain substantial gaps, particularly across large areas of Africa and Asia where future targeted searches would bolster outreach and the shared knowledge base. Both these language-and benefits-based initiatives could be further advanced to improve our socioeconomic understanding of costs and our ability to address how impacts affect and are distributed across stakeholders. Future directions to improve communication among stakeholders could involve public, industry, and policy engagement events to raise awareness of the importance and relevance of costs and open-access databases such as InvaCost, as well as through implementing more streamlined processes to add data from different regions, cost types, and sources. For example, the French Invasive Alien Species Resource Centre (especes-exotiques-envahissantes.
Improved resources and better communications with decision makers (e. g. , municipalities or ministries) could provide increased awareness for better future recording/classifications of costs, and more reliable cost estimates that could be incorporated in future versions of InvaCost. Engaging with affected industries could further catalyse actions by governments, given their operational impacts and policy influence. Dynamic databases and the resources that flow from them allow for future corrections and updates, such as ensuring old and new taxonomy are aligned to facilitate ongoing regulatory efforts. Because invasive species require adaptive management strategies, such tools enable the assessment of the effectiveness of particular strategies across contexts (after a reasonable period that accounts for lags), and the need for refinement if conditions change (e.
Linking InvaCost to other biodiversity databases Biodiversity databases compile, collate, and standardise information on biological diversity, monitor compositional and functional changes across different levels of biological organisation, provide a basis for exploring relationships between species and their environments, and identify specific geographical, taxonomic, ecological, and other knowledge gaps related to biodiversity. Although challenging, correlating data from different sources is recommended  (Hobern et al. 2019; König et al. 2019). Linking the InvaCost database with pre-existing biodiversity databases (e. g. , on traits, niche, and genetic datasets) has the potential to provide invasion science with better tools to quantify the links between ecological mechanisms and invasion impacts, and establish relationships between costs, ecosystems composition, and invasion dynamics  (Heger et al.
2021; Ricciardi et al. 2021; Daly et al. 2023). Integrating information on costs to a broader spectrum of biological groups (not solely invasive) and invaded socio-ecosystems can provide insights on (i) how invasive species impact non-native environments, (ii) areas where more resources might be needed (financial, technological, or research), and (iii) cost efficiency of current and alternative management options. In particular, this information will enable management efforts to be prioritised on economically vulnerable sites and on the most costly species. Over the past decade, many online databases and repositories have been established, providing digital information in thousands of primary studies on taxonomic, temporal, and spatial information, allowing broad syntheses  (Hardisty et al.
2013; Guralnick et al. 2016; Dornelas et al. 2018). Although the InvaCost database has many similarities to those pre-existing databases, it differs in that it serves rather specific purposes of standardising costs of invasive species and therefore has a different structure, content, and functionality. One database that has been used to augment several cost analyses so far is the Standardising and Integrating Alien Species workflow  (Seebens et al. 2020) , which includes information from five taxon-specific databases and two cross-taxon databases. This database leverages standardised biodiversity terminology (Darwin Core) to provide the most comprehensive distribution data for invasive species worldwide, with dates of first record wherever available, as well as evidence of impacts. Comparisons to InvaCost  (Crystal-Ornelas et al.
2021)  have provided estimates of missing data within the latter to test the predictiveness of first-record dates in invasion costs  (Turbelin et al. 2023) , and measure the total spending burden of invasive species  (Cuthbert et al. 2022). Even when only considering species deemed impactful in the Standardising and Integrating Alien Species workflowwhich itself has biases and data gapscomparisons reveal widespread gaps, especially in taxonomic and geographic coverage. Merging such databases with extrapolations of the real, but unrecorded, cost of invasions will therefore provide a more comprehensive overview of the true economic impact of invasive species. One of the greatest challenges of connecting biodiversity databases is the lack of standardisation  (Feng et al. 2022) , due to their particular objectives and different protocols for data acquisition and filtration, sometimes requiring specialised training  (Maldonado et al.
2015). Another shortfall in integrating multiple datasets is the reliability of data entries  (Harris 2003)  or the lack of data to sustain evidence-based actions  (Dickey et al. 2020). On the other hand, monetary loss is an international and defined proxy, capable of connecting and quantifying values arising from biodiversity losses. To stimulate the next stage of data integration between InvaCost and multiple existing and future derivative databases, automated tools such as machine learning and artificial intelligence should be developed to integrate and analyse these different but complementary data sources  (Jeschke et al. 2021). Automation could facilitate comparisons to identify taxonomic, geographic, and other research gaps across datasets, thereby providing an evidence-based tool for decision-making within and outside invasion science.