dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection, segmentation and features extraction of individual berries. The objective of this step is to i) detect berries suitable for shape inference, defined as berries with more than half of their contours visible in a grapevine bunch image, ii) infer their complete shape, and iii) extract features that allow quantifying their size and colour. The first two substeps rely on deep-learning models which have to be trained on annotations of complete berry contours inferring their hidden part. The annotated dataset contains 159 images, sampled from the three experiments (Table  1 ).
a) Construction of the annotation dataset The sampling was done to best cover all stages of growth, and maximise the visual diversity of the berries in the dataset in terms of size, shape, colour, texture, blurring and shading. It also includes various levels of occlusions between berries or with other plant organs and objects. A total of 6,134 berries were manually annotated as polygons using Labelme  [Wada 2018 ]. Similarly to  [Miao et al. , 2021] , only berries with at least half of their contours visible were annotated. Berries that did not reach pea size stage were rejected based on the assessment of their morphological characteristics, as they are not relevant for studying ripening. For each berry, an average of only 8 points (at least 5) were placed along the uncovered parts of its contours (Fig.
b) Detection of measurable berries To detect measurable berries on an image of a grapevine bunch, a Yolov4 deep-learning object detection model  [Bochkovskiy et al. , 2020]  was trained to find bounding boxes around berries with at least 50% visible contour in 416×416 px sub-parts of the image (Fig. 2A-B ). 20,000 training instances were generated by cropping 416×416 px sub-parts of the training images, each being labelled by the list of parameters of the boxes entirely included in it. This dataset was further augmented with random adjustments of vignettes hue, saturation and brightness, and random flips of image-label pairs. It was then used to train the model, using the yolov4-tiny architecture and default hyperparameters  [Bochkovskiy et al. , 2020]. Model weights were stored every 500 iterations for a total of 65,000 iterations.
The weights leading to the highest Average Precision (AP) on the validation dataset were saved. For predictions, the 2048×2448 px source image is split into image sub-parts cropped over the entire pixel range with a maximum spacing of 270 px, which are then fed to the detection model, resulting in a set of predicted parameters of the box dimensions (𝑤 𝑏 ̂, ℎ 𝑏 ̂) and centre coordinates (𝑥 𝑏 ̂, 𝑦 𝑏 ̂). Because of sub-image overlaps, the same berry can be detected more than once. To remove these redundancies, non-maximum suppression is used to avoid having box pairs with an intersection over union above 70%. Berries detected with a confidence score below a threshold 𝑠 = 0. 89 are filtered out. This value of 𝑠 was chosen to maximise the F1score on the validation subset.
c) Segmentation of berries For each detected berry, a square vignette of size 𝑠 = max(𝑤 𝑏 ̂, ℎ 𝑏 ̂) / 𝑧 is cropped around its box centre coordinate (𝑥 𝑏 ̂, 𝑦 𝑏 ̂). A constant value 𝑧 = 0. 75 is used to ensure that all berries are entirely contained within their respective vignette, and occupy a similar space regardless of their size (Fig. 2C ). Each vignette is then resized to 128×128 px by bilinear interpolation, and fed to a U-Net  [Ronneberger et al. , 2015]  deep-learning model with a VGG16 [Simonyan and Zisserman 2015] backbone. The model was trained to output a binary mask representing the shape of the berry as if it were not occluded by any other element present in the image (Fig. 2D ). To train the segmentation model, 40,000 vignettes were extracted from the annotation dataset using the cropping method described above.
d) Extraction of berry morphology and colour features Assuming that the resulting mask has an elliptic shape, its contour points are extracted as in  [Suzuki and Be 1985] , to fit (𝑥 𝑒 ̂, 𝑦 𝑒 ̂, 𝑤 𝑒 ̂, ℎ 𝑒 ̂, 𝑎 𝑒 ̂) ellipse parameters  [Fitzgibbon et al. , 1999]. These parameters are then rescaled to the original image coordinate space (Fig. 2E ). For each berry the following features are computed: Colour: the raw hue ℎ 𝑟𝑎𝑤 of a berry is computed as the circular mean of the hue angle of the pixels contained inside the ellipse, after removing the pixels that are less than 𝑑𝑝 = max(3, 𝑤 𝑒 /4) px away from the ellipse's edges, and removing the pixels shared by other ellipses. Given ℎ 50 = 100° the mean value of ℎ 𝑟𝑎𝑤 for grape berries that are halfway through their colour change from green to black in our dataset, the centred berry hue 𝐻 is defined as: 𝐻 = (180 -ℎ 𝑟𝑎𝑤 -ℎ 50 ) % 180 (1) Volume: Berry volume 𝑉 is estimated as the volume of the sphere that has the same projection area 𝐴 as the ellipse fitting the individual berry shape, as in  [Dubois et al.
3B ). Each point can only be paired once, the closest pairs are matched first, and pairs with a distance above a threshold 𝛿 = 16𝑝𝑥 are discarded. The value of 𝛿 was chosen as the quarter of the median value of 𝑤 𝑒 in our annotation dataset, with the idea that such a low value strongly limits mismatches, even in dense areas of the bunch. This algorithm can be applied successively to pairs of sets (𝑆 𝑡 , 𝑆 𝑡+1 ) along a time-series of 𝑁 images, to propagate the correspondence of the initial set of labels.
For each berry, an 8-days moving median was used to smooth the raw volume measurements over time (Fig. 7A  and Additional File 2A; red curves), and a MAPE value was computed between the raw and smoothed volume data. The 10% berries with the highest MAPE were excluded from the analysis to reduce the noise, resulting in a final dataset of 73 berries. For each kinetics of a variable 𝑋 (either 𝑉 or 𝐻), a relative kinetics 𝑋 𝑟 and a scaled kinetics 𝑋 𝑠 are computed as: 𝑋 𝑟 = (𝑋 -𝑋 0 ) / 𝑋 0 (8) 𝑋 𝑠 = (𝑋 -𝑋 0 ) / (𝑋 𝑚𝑎𝑥 -𝑋 0 ) (9) 𝑉, and the median of the last 8 days for 𝐻. All the method conception and the data analysis were performed in Python.
Deep-learning segmentation allows accurate and robust shape inference of partly hidden berries Berry segmentation was performed on the 2020 and 2021 datasets (21,744 images), resulting in an average detection of 64 berries per image. Fig. 4  provides some examples of detection on the test subset, showing that the model was able to infer the full contour of overlapping berries from different genotypes varying in size, shape and aspect, even when these contours were not fully visible. Predictions on the full test subset were compared with ground-truth annotations to quantify both detection and segmentation accuracies. The detection of measurable berries had a Precision of 92. 3% and a Recall of 87. 5% on the test subset, resulting in a F1-score of 89. 8%. The remaining errors (64 FPs, 109 FNs) were further investigated (e.
0% (F1-score = 91. 8%). Concerning FNs (i. e. missed detections), pea sized berries alone account for 27% of the cases, which result in a slight under-representation of this class in the histogram of the size of berries detected. (Additional File 3B). The area of the ellipses segmented by the model closely matched those of the manual annotations on the test subset (Fig. 6 ; MAPE=4. 1%, R²=0. 976), with a low bias of -32px². This demonstrates that the segmentation model was able to accurately infer the size of berries with up to 50% of their contours hidden. A similar MAPE around 4% was obtained on genotypes either present (n=440) or absent (n=363) from the training subset, suggesting that the segmentation generalised well to the genetic diversity in our dataset. The coverage 𝑇 𝑐 of the berry tracking method was assessed for each of the 18 bunches (M4, Table  2 ).
was used to fit 5 ellipse parameters (𝑥 𝑒 , 𝑦 𝑒 , 𝑤 𝑒 , 𝑤 𝑒 , 𝑎 𝑒 ) to the set of points (Fig. 1B; blue lines), with (𝑥 𝑒 , 𝑦 𝑒 ) the centre coordinates of the ellipse, 𝑤 𝑒 and ℎ 𝑒 the respective length of minor and major ellipse axis, and 𝑎 𝑒 the ellipse rotation. 𝑤 𝑏 and ℎ 𝑏 were further deduced as the width and height of the smallest box enclosing the ellipse. This dataset (Table 1) was then split into training (129 images; 4,447 labels), validation (10 images; 814 labels) and test (20 images; 873 labels) subsets. Each subset includes different plants, to better assess the generalisability of the detection and segmentation models.
The MethaneSAT satellite instrument and its aircraft precursor, MethaneAIR, are imaging spectrometers designed to measure methane concentrations with wide spatial coverage, fine spatial resolution, and high precision compared to currently deployed remote sensing instruments. At 12960 m cruise altitude above ground (13850 above sea level), MethaneAIR datasets have a 4. 5 km swath gridded to 10m x 10m pixels with 17 -20 ppb standard deviation on a flat scene. It was deployed in the summer of 2021 in the Permian Basin to test the accuracy of the retrieved methane concentrations and emission rates using the algorithms developed for MethaneSAT. We report here point source emissions obtained during a single-blind volume controlled release experiment, using two methods: (1) The modified Integrated Mass Enhancement (mIME) method estimates emission rates using the total mass enhancement of methane in an observed plume combined with winds obtained from Weather Research Forecast driven by High-Resolution Rapid Refresh meteorological data in Large Eddy Simulations mode (WRF-LES-HRRR).
org/10. 5194/egusphere-2023-822 Preprint. Discussion started: 1 June 2023 c Author(s) 2023. CC BY 4. 0 License. more comprehensive bootstrapping approach to account for uncertainty from background concentrations and thresholding in addition to the winds. In the second set of results, this filtering algorithm designated "below detection limit" six emission passes from the dataset; the quantified emissions were slightly changed after unblinding due to the new bootstrapping results. Based on this comparison, we determined that the detection limit for the MethaneAIR 1x1 retrievals using the mIME method was approximately 200 kg/hr. This value is preliminary because it is based on a small sample size, for two days, in contaminated areas; future work is needed to fully characterize the detection limit (such as using methods presented in  Conrad et al.
Acknowledgements. This work was supported by  NSF   EAGER  grant  1856426  to  Harvard University  and by funding from the  Environmental Defense Fund and Harvard University. The controlled methane release experiment was funded by  ExxonMobil , the  Stanford Strategic Energy Alliance , and the  Stanford Natural Gas Initiative , an industry consortium that supports independent research at  Stanford University. This work benefitted from discussions with  S. Aminfard. We gratefully acknowledge the contributions of the flight crew and staff of  NSF's Research Aviation Facility  and discussions with the  MethaneSAT Science Advisory Group  and with  S. Hamburg , Chief Scientist of the  Environmental Defense Fund.
Background: Intraoperative hypotension is associated with organ injury. Current intraoperative arterial pressure management is mainly reactive. Predictive haemodynamic monitoring may help clinicians reduce intraoperative hypotension. The Acumen™ Hypotension Prediction Index software (HPI-software) (Edwards Lifesciences, Irvine, CA, USA) was developed to predict hypotension. We built up the European multicentre, prospective, observational EU HYPROTECT Registry to describe the incidence, duration, and severity of intraoperative hypotension when using HPI-software monitoring in patients having noncardiac surgery. Methods: We enrolled 749 patients having elective major noncardiac surgery in 12 medical centres in five European countries. Patients were monitored using the HPI-software. We quantified hypotension using the time-weighted average MAP <65 mm Hg (primary endpoint), the proportion of patients with at least one !1 min episode of a MAP <65 mm Hg, the number of !1 min episodes of a MAP <65 mm Hg, and duration patients spent below a MAP of 65 mm Hg.
The Acumen™ Hypotension Prediction Index software (HPI-software) (Edwards Lifesciences, Irvine, CA, USA) was developed using machine learning to predict arterial hypotension defined as a MAP of less than 65 mm Hg for at least 1 min. 20  By analysing arterial pressure waveform features, it quantifies the likelihood that a patient will develop hypotension on a unitless scale between 0 and 100. 20  HPI values over 85 trigger acoustic and visual alarms and a pop-up window providing the possibility to display additional haemodynamic variables that might guide clinicians in treating the cause of hypotension. There are only sparse data on the effect of using HPIsoftware monitoring on intraoperative hypotension in patients having elective major noncardiac surgery. We thus aimed to quantify the amount of intraoperative hypotension when using HPI-software monitoring.
Study design and objective We registered the EU HYPROTECT Registry at ClinicalTrials. gov (NCT04972266) on July 22, 2021, and we published the details of the study protocol and methods. 21  In short, EU HYPROTECT was a European multicentre, prospective, observational registry in patients having elective major noncardiac surgery in 12 medical centres in five European countries (France, Germany, Italy, Spain, and United Kingdom). Ethics committee approvals were obtained for each site. Patients gave written informed consent to participate in the registry (unless the need for informed consent was waived by the local ethics committee). The overarching objective of the registry was to quantify intraoperative hypotension when using HPIsoftware monitoring in patients having elective major noncardiac surgery.
Study protocol Patients in the registry were monitored using the Acumen™ IQ sensor (Edwards Lifesciences) and the HemoSphere monitoring platform (Edwards Lifesciences), which calculates and continuously displays HPI and haemodynamic variables, including arterial pressure. Because using HPI-software monitoring requires training and education, the registry was performed in medical centres that routinely use HPI-software monitoring with the Acumen™ IQ sensor and the HemoSphere monitoring platform. There was no specific treatment protocol for patients included in this registry; patients were treated according to each centre's clinical routine. HPI-software monitoring and data recording were started with the beginning of surgery and stopped when surgery ended.
Patients We planned to include at least 700 patients in the registry. We included consenting adults (!18 yr) who were scheduled for elective major noncardiac surgery under general anaesthesia that was expected to last at least 120 min and in whom intraarterial pressure and HPI-software monitoring were planned for clinical management. We did not include patients having emergency surgery, nephrectomy, and liver or kidney transplantation; patients with atrial fibrillation or sepsis (according to The Third International Consensus Definitions for Sepsis and Septic Shock); patients with ASA physical status classification 5 or 6; patients who were not able to understand the nature, significance, and scope of the investigation; pregnant women; patients without signed informed consent; and patients participating in interventional trials.
Endpoints We aimed to describe the incidence, duration, and severity of intraoperative hypotension when using HPI-software monitoring in patients having elective major noncardiac surgery. The primary endpoint was the time-weighted average MAP <65 mm Hg. The time-weighted average MAP <65 mm Hg (unit: mm Hg) is the area under a MAP of 65 mm Hg (unit: mm Hg Â minutes) divided by the total monitoring time (i. e. the total duration of surgery; unit: minutes). 22, 23  We also report the time-weighted average MAP <60 and <55 mm Hg; the area under a MAP of 65, 60, and 55 mm Hg; the proportion of patients with at least one !1 min episode of a MAP <65, <60, and <55 mm Hg; number of !1 min episodes of a MAP <65, <60, and <55 mm Hg; absolute duration and relative duration (% of surgical time) patients spent below a MAP of 65, 60, and 55 mm Hg; and the absolute maximum decrease lasting !1 min below a MAP of 65, 60, and 55 mm Hg.
Statistical analysis We performed descriptive analyses to quantify intraoperative hypotension, patient characteristics, and perioperative data. Continuous variables are presented as median (25% percentilee75% percentile), and categorical variables are presented as absolute number (percentage). All statistical analyses were performed using IBM SPSS Statistics version 24 (IBM, Armonk, NY, USA) or R Core Team (https://www. Rproject. org/). The sample size was estimated based on previously published data. A randomised trial reported a median timeweighted average MAP <65 mm Hg of 0. 44 (0. 23e0. 72) mm Hg without and 0. 10 (0. 01e0. 43) mm Hg with HPI-software monitoring. 23  Considering the wide inter-quartile range of the time-weighted average MAP <65 mm Hg, we planned to include at least 700 patients.
Results Between September 2021 and May 2022, we enrolled 749 patients in the registry. We excluded 47 patients because of inclusion or exclusion criteria violation (n¼33), technical problems with HPI-software monitoring or recording (n¼10), or lack of study personnel to initiate HPI-software monitoring (n¼4). We thus finally included 702 patients in the analysis (Table  1 ). The patients' median age was 64 (55e73) yr; 340 patients (48%) were female. The patients had surgery for a median of 209 (153e290) min. All patients had surgery under general anaesthesia, either as total intravenous anaesthesia (234 patients [33%]) or balanced anaesthesia (467 patients [67%]) (Table  2 ); 332 patients (47%) additionally had neuraxial or regional anaesthesia. During The EU HYPROTECT Registry -3 surgery, the patients were given a median total volume of crystalloid and colloid fluids of 2500 (1500e3537) ml.
Discussion The median time-weighted average MAP <65 mm Hg was very low in patients in this registry. This suggests that using HPIsoftware monitoring may help reduce the duration and severity of intraoperative hypotension in patients having noncardiac surgery. The median time-weighted average MAP <65 mm Hg was 0. 03 mm Hg. About 40% of the patients had no !1 min episode of MAP <65 mm Hg. Patients had a MAP <65 mm Hg for a median of 2 min or 1% of surgical time. Both the duration and severity of intraoperative hypotension are associated with postoperative complications. 27  We thus primarily quantified intraoperative hypotension as timeweighted average MAP <65 mm Hg because it reflects both hypotension duration and severity. Patients in this registry had a median time-weighted average MAP <65 of 0.
03 mm Hg, which is substantially lower than those reported previously in patients having major noncardiac surgery (Fig  1 ). 23,28e30  In patients randomly allocated to receive routine care in three trials on HPI-software-guided intraoperative arterial pressure management, the median time-weighted average MAP <65 mm Hg were 0. 44, 23 0. 14, 28 and 0. 50 mm Hg. 30  Interestingly, even in patients randomised to HPI-software-guided intraoperative arterial pressure management, the timeweighted average MAP <65 mm Hg was substantially higher than in patients included in this registry: 0. 10, 23 0. 14, 28 and 0. 16 mm Hg 30 vs 0. 03 mm Hg in this registry. Similarly, in a retrospective observational study, including 100 patients having noncardiac surgery, the median time-weighted average MAP <65 mm Hg was 0.
27 mm Hg when patients were treated based on pulse wave analysis data without HPIsoftware monitoring and 0. 10 mm Hg when HPI-software monitoring was used as well. 29  There are presumably different reasons why there was little intraoperative hypotension in patients in this registry. First, the HPI-software may predict hypotension several minutes before a hypotensive event occurs. 20, 31  The very purpose of the HPI-software monitoring thus is to allow the treatment of impending hypotension before it occurs. Second, using HPIsoftware monitoring requires training and education. We only recruited patients in medical centres where HPI-software monitoring is used per clinical routine. Most clinicians taking care of registry patients thus had experience with using HPIsoftware monitoring. Third, when considering the additional haemodynamic data provided on the secondary screen, HPI-software monitoring helps in treating or preventing intraoperative hypotension specifically and causally.
But, in contrast to troponin, serum creatinine is routinely measured after surgery in most participating medical centres. Postoperative mortality within 30 days after The EU HYPROTECT Registry -5 surgery was observed in 2% of patients, which is consistent with postoperative mortality in Europe 36 and the USA. 37  Large randomised trials are necessary to test the hypothesis that HPI-software-guided, compared with routine, arterial pressure management improves patient-centred outcomes in patients having surgery. A strength of this multicentre European registry is that it reflects hypotension exposure and clinical outcomes in a wide range of patients having noncardiac surgery monitored with HPI-software. Therefore, the results of this registry can inform the design and sample-size calculation of robust randomised trials investigating whether HPI-software-guided arterial pressure management reduces intraoperative hypotension and eventually perfusion-related patient-centred outcomes.
Such trials are needed because previous trials on HPIsoftware-guided arterial pressure management were small, only powered to investigate hypotension reduction, and showed conflicting results. 28, 38  The main limitation of this registry is that it does not allow us to directly compare intraoperative arterial pressures between patients with vs without HPI-software-guided haemodynamic management. We thus naturally cannot infer that using HPI-software monitoring was the only reason that there was little hypotension. Only randomised trials can answer the question whether there is a causal effect of HPI-software-guided hypotension reduction on patient outcome. However, comparing the median timeweighted average MAP <65 mm Hg observed in our registry patients with those from previous studies and trials suggests that the use of HPI-software monitoring contributed to hypotension reduction.
In summary, the median time-weighted average MAP <65 mm Hg was very low in patients in this European multicentre, prospective, observational registry. This suggests that using HPI-software monitoring may help reduce the duration and severity of intraoperative hypotension in patients having noncardiac surgery. However, randomised trials are needed to investigate whether HPI-software-guided haemodynamic management improves patient-centred outcomes by reducing hypotension.
Acknowledgements The authors acknowledge the contribution of the staff at  Edwards Lifesciences , especially  Edward Hembrow ,  Tim van den Boom ,  Anne Halfmann ,  Pierre Sibileau ,  Barbara Plasschaert ,  Volker Haag ,  Giulia Torricella , and  Alessia Longo. The authors further appreciate the excellent project management secured by  Daniel Greinert ,  Marie Zielinksi , and  Claudia Lu ¨ske  at the  Institute for Pharmacology and Preventive Medicine (Cloppenburg, Germany ). Edwards Lifesciences  had an active role in the design of the study and acts as the legal sponsor.