dataesr/scientific-paragraphs-categorization huggingface dataset:
Also, the present work focused on WMH detection in asymptomatic or presymptomatic adults, with the assumption that WMH found in these adults are primarily early stages of age-related WMH of presumed vascular origin, thus combining MRi-Share and MWC dataset for training our model. However, in reality, WMH found in MRi-Share may represent mixed pathology, with lesions in some subjects caused by pre-or sub-clinical inflammatory conditions  (Hosseiny et al. , 2020). While the model learning the WMH in a given training data is agnostic about their etiology, it can learn any global or local spatial and intensity features of the lesions present in the training dataset. To the extent that there are etiology-specific patterns in WMH appearance and spatial distributions, it is possible that SHIVA-WMH has learnt predominant WMH patterns in the specific training dataset we used, and may be less sensitive to lesions found in other conditions we did not explicitly focus on, such as MS (training dataset for SHIVA-WMH did not contain any incidental MS subjects).
Conclusion To summarize, we presented the SHIVA-WMH detector, a 3D-Unet based model trained with both MRi-Share and MWC dataset with the specific aim to improve detection of small WMH in asymptomatic or pre-symptomatic adults in population-based studies. Our tool outperformed both a classic WMH segmentation tool (LST-LPA) and existing state-of-the-art Unet-based tools (PGS and HPM) in segmenting small WMH in non-clinical, community-dwelling adults represented by MRi-Share and UKB. Our tool can effectively segment WMH across a wider range of WMH burden than existing methods, and thus can be a valuable tool for studies aiming to characterize the emergence and progression of WMH lesions. Such studies are essential for understanding the pathophysiology and early-life factors associated with the most common etiology of WMH in the population, namely cSVD.
Our demonstration of altered diffusion properties of small WMH in MRi-Share, bearing the hallmark of compromised microstructural integrity similar to those found in WMH of older subjects or MS patients, also underscores the importance of early detection and intervention. To encourage more research on the early detection and characterization of WMH, we make the SHIVA-WMH detector freely and openly available at (https://github. com/pboutinaud/SHIVA_WMH).
Data/code availability statement MRi-Share data used in this study cannot be shared through a public repository due to French regulations regarding sharing of the medical imaging data. However, de-identified data can be requested to the i-Share Scientific Collaborations Coordinator (ilaria. montagni@u-bordeaux. fr) with a letter of intent (explaining the rationale and objectives of the research proposal), and a brief summary of the planned means and options for funding. MICCAI 2017 WMH segmentation challenge dataset used in the present work is freely and publicly available at the challenge homepage (https://wmh. isi. uu. nl/data/). This work also used the neuroimaging dataset obtained from the UK Biobank Resource (application number 18359 and 94113). Source codes for the statistical analysis presented in the manuscript are available on GitHub (https://github.
Acknowledgements We would like to acknowledge  Prof. Bernard Mazoyer  for the initial conception of the project to characterize early signs of white matter anomalies in the young subjects of MRi-Share and his reviewing of raw T1w and FLAIR images in this database to select 50 subjects with ranging amount of both WMH and PVS. We are also indebted to the following individuals for their invaluable contribution to the MRi-Share project:  Serge Anandra ,  Amandine André ,  Gregory Beaudet ,  Christophe Bernard ,  Bruno Brochet ,  Aurore Capelli ,  Claire Cardona ,  Arnaud Chaussé ,  Christophe Delalande ,  Vincent Durand ,  Louise Knafo ,  Morgane Lachaize ,  Alexandre Laurent ,  Hugues Loiseau ,  Elena Milesi ,  Marie Mougin ,  Maylis Melin ,  Guy Perchey ,  Clothilde Pollet ,  Thomas Tourdias ,  Cécile Marchal ,  Guillaume Penchet ,  Cécile Dulau ,  Igor Sibon ,  Sabrina Debruxelle ,  Sophie Auriacombe ,  Caroline Roussillon ,  Nicolas Vinuesa , and the i-Share "relay" students.
Funding This work has been supported by a grant overseen by the  French National Research Agency (ANR)  as part of the " Investissements d'Avenir" Program   ANR-18-RHUS-002. This work was supported by a grant from the  French National Research Agency  ( ANR-16-LCV2-0006-01 ,  LABCOM Ginesislab ). The i -Share study  has received funding from the  ANR (Agence Nationale de la Recherche)  via the ' Investissements d'Avenir' programme  (grant  ANR-10-COHO-05 ). The  MRi-Share cohort  was supported by grant  ANR-10-LABX-57  and supplementary funding was received from the  Conseil Régional of Nouvelle Aquitaine  (ref. 4370420 ). The work was also supported by the  " France Investissements d'Avenir" program  ( ANR-10-IDEX-03-0 ).
It is also by far the most common algorithm used as a reference method in studies proposing new WMH detection methods (29 out of 37 studies reviewed in Balakrishnan et al. 2021). The PGS is a 2D Unet-based method and the current winner of the MWC 2017 challenge (https://wmh. isi. uu. nl/results/), with the VL-and CL-Dice scores of 0. 81 and 0. 79 respectively in the held-out test dataset of the challenge (Park et al. , 2021). It combines the Unet architecture with what the authors call a multi-scale highlighting foregrounds approach, in which the ground truth labels are down-sampled at multiple scales to allow loss minimization at each layer of the Unet. This approach has the effect of emphasizing the contributions of small lesions and the voxels in the lesion boundaries during the network training, and as a result should improve accurate detection of WMH voxels with high uncertainty due to partial volume effect, including small lesions.
Another recently published Unet-based model, HPM uses 3D input like our SHIVA-WMH detector. It was chosen based on the high reported performance in cases of very mild WMH burden in a multi-cohort dataset of 50 subjects with mean WMH volume of ~2 mL, with VL-and CL-Dice scores of 0. 84 and 0. 72, respectively (Mojiri Forooshani et al. , 2022).
Weather data and validation Validation of the weather data model involves comparing predicted and measured solar radiation and ambient temperature. The measurements are provided by the Algiers Meteorological Station (Centre for the Development of Renewable Energy of Algeria. , 2021), while the forecasts are obtained from the TRNSYS software (Meteonormsoftware8. , 2022). Figure (3a) and (3b) highlight the measured solar radiation intensity and ambient temperature at the site of Algiers in 2020. A Good agreement between predicted and measured results was observed. The model demonstrated high accuracy in predicting the site's solar radiation and ambient temperature, allowing the system's performance to be estimated with reasonable accuracy. It can also be noted that the location has a high solar radiation intensity, reaching 250 kWh/m 2 in July, one of the highest potentials in the world, with an average ambient temperature of 18ºC.
Design Evolution The design of a product can be upgraded based on data feedback from other lifecycle phases. This includes functional or routing upgrades. Fig. 2. Flowchart of selected publications in the review (status in June 2022). CBM descriptions discerned in the RESOLVE framework after browsing the full texts. In this way, 59 papers were retained to explore the contribution of the IoT to CBM, see Fig. 2. Then, a quantitative analysis of how the IoT reduces environmental impacts in CBM was conducted. For this, from the included papers we acquired 23 results that a) quantified impacts on energy-related indicators in circular economy (e. g. , energy use or CO2 emissions) and b) compared these impacts before and after using IoT technology.
Exchange business model This model could acquire strength by adopting additive manufacturing and IoT systems with monitoring capability  (Despeisse et al. , 2017). 3D printers are able to process renewable and sustainable production. Based on interaction between organizations and customers, some companies are able to manufacture customized products by using databases incorporating 3D printers, where IoT helps to save time and material use in additive manufacturing. These kinds of functions make IoT easier to achieve CE principles. Typical cases are listed in Table  8.
Quantitative analysis of how the use of IoT in CBMs reduces energyrelated environmental impacts Within the collected papers, 20 papers provide quantitative data on how the use of IoT in CBMs can reduce environmental impacts. These papers provide 23 cases. We included them in our quantitative analysis for they mainly use energy savings as the primary measurement. Fig. 7  depicts how much energy is saved by using IoT in the six ReSOLVE business model categories (Virtualize is missing due to lack of data). The X-axis shows the ReSOLVE business model categories and their number of samples in each. The Y-axis represents the relative energy savings compared to a baseline scenario without IoT enhancement (i. e. , the energy use in the baseline is set on 100%). Supplementary Table  2  contains a list of cases and their references.
sources (e. g. , databases, search terms) used to identify studies. Risk of bias 5 Specify the methods used to assess risk of bias in the studies included. Synthesis of results 6 Specify the methods used to present and synthesize results. RESULTS Included studies 7 Give the total number of studies included and participants and summarize relevant characteristics of the studies. Synthesis of results 8 Present results for main outcomes, preferably indicating the number of studies included and participants for each. If a meta-analysis was carried out, report the summary estimate and confidence/credible interval. If comparing groups, indicate the direction of the effect (i. e. , which group is favouredsummary of the limitations of the evidence included in the review (e. g. , study risk of bias, inconsistency and imprecision).
Two studies reported that only lateral interactions between chromosomes and microtubules form during prometaphase/metaphase  55, 56. However, a recent study reported that end-on kinetochore-microtubule attachments are predominant in prometaphase/metaphase and are essential for chromosome separation in anaphase  57. Here we have addressed this issue, using 4D simultaneous twocolor confocal fluorescence microscopy at high spatiotemporal resolution in live C. elegans oocytes. We successfully tracked all six bivalent chromosomes and the meiotic spindle with unprecedented spatial and temporal resolution during the first meiotic division in oocytes with wild-type or disrupted kinetochore protein function. These datasets allowed a systematic and comprehensive quantitative analysis of the kinetochore contribution to chromosome movements during meiotic prometaphase/metaphase.
Full 4D tracking of chromosomes and spindle poles during meiosis I To analyze chromosome movements relative to the spindle axes with high spatiotemporal accuracy during the first meiotic division in live C. elegans oocytes, we acquired four-dimensional (4D) in utero datasets on anaesthetized C. elegans adult worms stably expressing mCherry-H2B and EGFP-αTubulin during ovulation (Fig. 1b ). To avoid blurring of the 3D stacks due to passage of the oocyte through the worm spermatheca during the first meiotic division, accompanied by fast movements of the spindle and chromosomes within the oocyte, we acquired the two fluorescent channels at each Z position simultaneously on two cameras. We combined the simultaneous two-channel acquisition with fast Z-scanning using a piezo-controlled motorized stage and a super-fast stream acquisition mode (10 ms per acquisition) to acquire 9 μm-thick Z-stacks (30×0.
3 μm) every 10 s throughout meiosis I and II. Live imaging did not appreciably perturb cell division as evidenced by successful first polar body extrusion in 100% of oocytes. These 4D datasets were processed to semi-automatically segment and track all chromosomes and the spindle poles (Fig. 1c, d  and Supplementary Movie 1). The resulting individual chromosome and spindle pole tracks were interactively validated and manually corrected as needed. To understand the mechanisms that govern chromosome movements inside the spindle, we first analyzed the kinetics of chromosome behavior at high spatiotemporal resolution. Specifically, we analyzed three parameters: (1) chromosome orientation relative to the spindle long axis, (2) chromosome congression to the spindle equator, and (3) chromosome axial compaction along the spindle short axis.
Kinetochores are essential for chromosome orientation, congression and accurate segregation, but not physical separation of chromosomes during meiosis I To gain functional insights into the mechanisms governing chromosome movements during prometaphase/metaphase I, we next analyzed the effect of depleting the kinetochore scaffold protein KNL-1 by RNAi  19, 45  (Fig. 1a ). We noticed that the chromosome segregation phenotype of KNL-1-depleted oocytes was aggravated when filming in utero (even with low Z sampling, Supplementary Fig. 1c-e ) as compared to filming oocytes ex utero (Fig. 2a-c , see discussion). We thus decided to generate 4D datasets of ex utero-filmed oocytes, which is compatible with high-throughput analyses yet restricted to the last 200 s preceding anaphase onset (Fig. 2c  and Supplementary Movie 2).
The power of lasers were measured before each experiment with an Ophir VEGA Laser and energy meter. Fine stage control and focus correction during acquisition was performed using a PZ-2000 XYZ Piezo-driven motor from Applied Scientific Instrumentation (ASI). 30 Z-plans, separated by 0. 3 µm, were acquired every 10 s using a Nikon CFI APO λS 40x/NA1. 25 water immersion objective. Movies were acquired with a 1 × 1 binning. Control of acquisition parameters was done using the Metamorph 7 software (Molecular Devices). Image analysis was performed using the Fiji  82  and Imaris (Oxford Instruments) software. Live ex utero imaging GFP::TBA-2 α-tubulin -, or GFP::TBB-2 β-tubulin -, and mCherry::HIS-11  H2B  expressing adults worms were dissected in meiosis medium (5 mg/mL inulin, 25 mM HEPES, 60% Leibovitz's L-15 media and 20% fetal bovine).
Acknowledgements We thank all members of the  Dumont lab  for support and advice. We are grateful to  Patricia Moussounda ,  Téo Bitaille  and  Vincent Maupu-Massamba  for providing technical support. We thank  Vincent Contremoulin  from the  ImagoSeine facility  for his technical help with the Imaris Software. We are grateful to  Meredith Price  for her support with the Imaris Software during the Covid-19 lockdown. We thank  Arshad Desai  and  Dhanya Cheerambathur  for providing some C. elegans strains used in this study. Some strains were provided by the CGC, which is funded by  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). This work was supported by  CNRS  and  University Paris Cité , by  NIH   R01GM117407  and  R01GM130764  (J. C. C. ), and by grants from the  European Research Council   ERC-CoG ChromoSOMe 819179  and from the  Agence Nationale de la Recherche   ANR-19-CE13-0015  (J.
I. INTRODUCTION S YNTHETIC Aperture Radar (SAR) is a pow- erful tool for studying the ocean surface. C-Band SAR are sensitive to variations in sea surface roughness, and have been used to detect various meteorological and ocean processes, referred to as metocean, such as atmospheric or ocean fronts  [1] , icebergs  [2] , oil surfactants from pollution  [3]  or generated by plankton  [4] , and some species of seaweed  [5]. They are particularly useful for studying waves  [6]  and extreme events like cyclones  [7] ,  [8]. There has been particular attention given to estimating wind speed using these sensors. As the number of satellite missions with C-SAR sensors increases and archives of these data accumulate, it is becoming easier to build large SAR datasets. This paper focuses on the Sentinel-1 mission from the Copernicus program, which consists of two satellites, Sentinel-1A (launched in 2014) and Sentinel-1B (launched in 2016, which has been out of operation since December 2021).
These models are known to be able to tackle denoising  [24]  and inpainting  [25]  tasks because they use contextual information to estimate the original signal. This paper is dedicated to estimating wind speed in rainy areas using a model that does not require an explicit rainfall prior and only uses the parameters available to GMFs. In the first section, we present the SAR data used to train the model and the ancillary information available. The second section describes the methodology used to build the dataset, with special attention given to ensuring a balanced representation of rainfall observations. The final section presents the results on the training set and confirms them with in-situ measurements from buoys, demonstrating the model's ability to correct for rain overestimates.
II. DATASET The SAR measurements used in this chapter come from 19,978 IW observations acquired globally between March 03 rd , 2018 and the February 23 rd , 2022, inclusive. Each of these observations covers approximately 44 000 km² and has a resolution of 100 m/px, downscaled from the GRDH products available at 10 m/px. The radiometric information used as input is the Sea Surface Roughness (SSR) defined in  [26]  as the normalized radar cross section σ 0 divided by the σ 0 of a wind of 10 m/s and a direction of 45°relative to the antenna look angle. The σ 0 of this neutral wind is given by the GMF CMOD5. N. Obtaining global information on rain that can be used in conjunction with SAR observations can be difficult. A previous study conducted using a global Sentinel-1 dataset found only 2,304 partial collocations with the satellite-based radar GPM/DPR  [27]  out of 182,153 IW.
B. Dataset balancing procedure In this section, we describe the process for building a balanced dataset. Our goals are to (I) ensure that the wind distribution of each dataset is close to the real-world distribution, (II) prevent information leak between the training, validation, and test subsets, (III) ensure that the groundtruth wind speed, obtained from an atmospheric model, accurately represents the real-world wind speed, and (IV) include enough rain samples to allow the model to learn from them. a) Rain and rainless patches selection: As discussed earlier, rainfall estimation is provided by a deep learning model at a resolution of 100 m/px, on the same grid as the SAR observation. Therefore, it is possible to separate the observations into two A + = {x : Rainf all(x) >= 3mm/h} (Eq. 1) A -= {x : Rainf all(x) < 3mm/h} (Eq.
• P -as the wind speed distribution on n -. • P as the wind speed distribution on n -∪ n +. Balancing the dataset to the real-world wind distribution translates to the following condition: ∀x, P (x) = n + P + (x) + n -P -(x) n + + n - (Eq. 4) As we choose to keep all rain patches and to set n + = n -, Eq. 4 leads to: ∀x, P -(x) = 1 2 (P + (x) -P (x)) (Eq. 5) For some wind speeds x, P + (x) is higher than twice P (x). In these cases, we relax the condition from Eq. 5 in order to avoid removing rain patches. Fig. 3  depicts the wind speed distribution for rain and rainless patches. The mean squared error between P and 1 2 (P + + P -) reach 8. 8%. The dataset can be further balanced to ensure that, for each wind speed, the number of rain and rainless patches is equal. However, this leads to remove of 84% of the data.
B. Application to SAR observation with groundtruthed in-situ data However, ECMWF wind speeds are model data and not in-situ, which can be obtained using anemometers on buoys. Using the dataset created in  [35] , 4732 collocation points between Sentinel-1 and NDBC buoys are identified. The rain prediction model estimates that 4643 of these points are rainless, 75 record rainfall of more than 1 mm/h, and 14 record rainfall of more than 3 mm/h. On a side note, the height at which in-situ measurements were taken varies, with most being between 3. 8 m and 4. 1 m above sea level. As mentioned in  [35] , the SAR inversion and deep learning prediction are both normalized to the altitude of the corresponding in-situ measurement. Denoting w the wind speed at 10 m estimated from the SAR, the wind speed at elevation h is given by the exponential law  [36] : w h = w • h 10 0.
11 (Eq. 7) Table  II  indicates that the performances of the deep learning are higher than the GMF for both the Root Mean Square Error and the Pearson Correlation Coefficient for all rain ranges. The RMSE decreases by 0. 04, 0. 37 and 1. 33 m/s for rainless, light rain, and moderate rain-situations, respectively. The bias decreases by 0. 26 and 1. 01 m/s under light and moderate rain, but increases by 0. 02 m/s for rainless situations. Table II also demonstrates the importance of the dataset building scheme as a dataset composed of collocations without the aformentionned sample selection, referred to as the "neutral dataset," consistently has lower performances than the balanced dataset. Fig. 5  presents a comparison of the performances against NDBC buoys for various wind speeds. Unfortunately, collocations are scarce at very low and high wind speeds, which are the intervals where both the GMF and deep learning models have higher errors.
In the following, we observe two cases where rainfall was detected on the buoy position at the time of observation. high wind speeds, higher than 20 m/s (Fig. 6. d ). The deep learning model attenuates these values to between 6 m/s and 8 m/s (Fig. 6. e). For NDBC 46054, only one measurement of wind speed and direction per hour is available. It recorded a wind speed of 6. 3 m/s eight minutes before the SAR observation. The GMF and the deep learning model estimated wind speeds of 15. 1 m/s and 5. 9 m/s, respectively. While the temporal resolution of NDBC 46054 is one measurement per hour, NDBC 46053 records data every ten minutes. Furthermore, the gust front appears to be moving toward the right part of the observation. This can be seen in the time series in Fig. 6  as a large variation in wind direction between 02:40:00 and 03:00:00.
The variation in wind speed seems to precede the variation in direction, first increasing then decreasing to a lower wind regime. On NDBC 46053, the GMF and the deep learning model agree on a wind speed of 4. 5 m/s, which is slightly lower than the in-situ data of 5. 4 m/s. Since the distance between NDBC 46054 and NDBC 46053 is approximately 60 km, the progression of the gust front can be estimated to be around 90 km/h. With a width of around 5 or 6 km, the whole system would pass the buoys in three minutes. This means that even NDBC 46053 may not have been able to accurately estimate the wind speed due to its low temporal resolution. However, it is worth noting that even the gust speed at NDBC 46054, defined as the maximum wind speed over a given number of seconds, does not record a speed higher than 9 m/s.
V. CONCLUSION Previous studies have shown that high-resolution rain signatures can be automatically extracted from SAR observations. Using this SAR rainfall segmenter, we built a wind estimation dataset where 50% of the patches contain rainfall examples. Samples were chosen so that a SAR-based and a SARindependent wind speed model agree on non-rain pixels, ensuring that their estimates are close to the true wind speed. A UNet architecture was trained on this dataset to estimate wind speeds based on the SAR-independent atmospheric model. We tested several input combinations and found that the most important parameter was the wind speed prior from the geophysical model function, which the deep learning model had difficulty emulating. Collocations with buoy in-situ measurements show that the model outperforms the current Geophysical Model Function (GMF) on rain areas, reducing the RootMean Square Error (RMSE) by 27% (resp.