dataesr/scientific-paragraphs-categorization huggingface dataset:
As expected, the data with the generally lowest values of IOPs belong to Group 3, the data with intermediate values of IOPs to Group 2, and the data with the highest IOPs (most turbid waters) to Group 1. We also note that the in situ dataset exhibits a somewhat wider dynamic range of variability than the synthetic dataset, especially when the IOP ratios, a g (440)/a ph (440), a dg (440)/a ph (440), a d (440)/a ph (440), and b bp (550)/a ph (440), are relatively high. While this result can reflect some degree of intrinsic difference in the dynamic range covered by the two datasets, it must also be recognized that some variability in the in situ dataset may be associated with the fact that these data were collected on numerous cruises by different groups of investigators using methodologies (instrumentation, data processing, data quality control, etc.
) that are unavoidably different across the different data sources. For additional comparative purposes, Fig. 7a  and d  include a few empirical relationships between the IOPs in question, which were established in previous studies based on considerable amount of field measurements collected mostly in open-ocean environments. As seen, the relationships between a g (440) and a ph (440) based on the studies of  Morel (2009)  and  Bricaud et al. (2010)  agree quite well with the central tendency of variation within our synthetic dataset. We note that  Morel (2009)  and  Bricaud et al. (2010)  reported relationships between a g (440) and Chla that were very similar in these studies. For the purpose of illustration in our Fig. 7a , we replaced Chla with a ph (440) using the formula Chla = a ph (440)/0.
05582. Similarly, the studies of  Huot et al. (2008)  and  Antoine et al. (2011)  reported on empirical relationships between b bp (λ) and Chla. After converting Chla to a ph (440) as mentioned above, these two relationship are plotted in Fig. 7d. Although these two relationships have different slopes, they are both generally consistent with the average trend of variation in the synthetic dataset. Radiative transfer is driven mainly by two ratios of IOPs, which are the scattering to absorption ratio, b(λ)/a(λ), which controls the number of scattering events  (Morel and Gentili, 1991) , and the molecular to total scattering ratio, b w (λ)/b(λ), which is the parameter controlling the weighted sum of the particle scattering and molecular scattering phase functions  (Morel and Loisel, 1998; Loisel and Stramski, 2000).
9a. For comparison, the range of in situ data is illustrated by the dashed contour lines. The maximum value of R rs (443) reached 0. 0165 sr -1 , which is in good agreement with in situ measurements performed in ultra-oligotrophic waters in the South Pacific Gyre during the BIOSOPE cruise (see Fig. 3  in  Stramski et al. , 2008). These results are once again illustrated using color coding to represent different optical water types, specifically Groups 1, 2, and 3. As seen, there is relatively good agreement between the synthetic data and the range of variability of the in situ data for Groups 2 and 3 (Fig. 9a ). For Group 1 (very turbid waters), however, the synthetic data exhibit a smaller range of variability compared with in situ data. This result is not unexpected because our primary goal was to generate the synthetic database that is most representative of open-ocean pelagic environments as well as coastal areas where water turbidity is low to moderate rather than very high.
As described in Sect. 2, turbid waters of Group 1 correspond to OWCs 1 and 2 as defined in  Mélin and Vantrepotte (2015). It is interesting to note that the synthetic optical database that was developed by  Nechad et al. (2015)  for coastal waters shows relatively good consistency between the synthetic and in situ data for Group 1 (Fig. 8b ). However, in contrast to our synthetic database, the synthetic data of  Nechad et al. (2015)    m * All optical variables in the database are spectral and provided at different light wavelengths between 350 and 750 nm at 5 nm intervals and different depths within the water column between the sea surface and the 50 m depth, except for R rs , and L w , which are defined at the sea surface. of variability compared with in situ data for Groups 2 and 3. Thus, the synthetic data of  Nechad et al.
(2015)  for turbid waters in Group 1 can provide useful complementarity to our synthetic database whose main focus is on water types from Groups 2 and 3. The scatter plot of the synthetic data of K d (490) 1 as a function of the blue-to-green band ratio of reflectance, R rs (490)/R rs (555), is shown in Fig. 10. These synthetic data are again color coded according to optical water classes defined in terms of Groups, 1, 2, and 3. For comparison, a few empirical relationships between these AOP variables established in previous analyses of field measurements are also displayed in  Fig. 10 (Mueller, 2000; Werdell, 2005; Werdell, 2009). The relationship of  Mueller (2000)  was formulated during the early phase of the SeaWiFS satellite mission to serve as an operational global algorithm for estimating K d (490) from ocean color observations.
Werdell (2005)  provided an updated relationship with a primary goal to improve the estimation of K d (490) at low values of K d (490) that correspond to high reflectance band ratio values. Figure  10  shows that these two relationships are generally consistent with our synthetic data across the entire range of variability encompassing data from Groups, 1, 2, and 3. This is reassuring given that the main purpose of our synthetic database and these two empirical relationships is similar in the sense of targeting the optical variability within the global ocean  Specifically, the relationship of  Werdell (2009)  presented in Fig. 9  is referred to as KD2S and is based on SeaWiFS spectral bands. In contrast to the relationships of  Mueller (2000)  and  Werdell (2005) , the relationship of  Werdell (2009)  deviates significantly from our synthetic data within the range of relatively high values of K d (490) 1 , which correspond to relatively low values of R rs (490)/R rs (555).
It is remark-able that this deviation occurs within the range where our synthetic data are classified as Group 1, so these optical water types are associated with high water turbidity. Another remarkable result illustrated in Fig. 10  is that the relationship of  Werdell (2009)  in this range is quite consistent with the main trend observed within the synthetic database of  Nechad et al. (2015) , which was developed for coastal environments. This result further supports the potential complementarity between our synthetic database and that of  Nechad et al. (2015). The scatter plot of Chla vs. the MBR of reflectance for the synthetic database is shown in Fig. 11. The monotonically decreasing trend of Chla with increasing MBR is consistent with the SeaWiFS-specific OC5 algorithm for estimating Chla from MBR (O'Reilly and  Werdell, 2019).
Summary We have generated a new synthetic database that consists of seawater IOPs as well as corresponding radiometric quanti-ties and AOPs within the ocean surface layer down to a depth of 50 m and at the sea surface. The radiometric quantities and AOPs were obtained from radiative transfer (RT) simulations performed with HydroLight code using the IOPs as input to the calculations. The list of variables included in the database is provided in Table  2. Because of the use of the absorption and scattering properties of pure seawater (assuming the salinity of 35 ‰) in the simulations, the present database cannot be applied to freshwater environments and special caution should be exercised for applications where water salinity is significantly less than 35 ‰ because of the decrease in pure seawater scattering.
This database is organized following an easy-to-read NetCDF structure and divided into two subsets of data for which the file name identifies the sun zenith angle and the RT simulation scenario related to the presence or absence of inelastic radiative processes within the water column. The first subset of data includes the seawater spectral absorption and backscattering coefficients as well as sea-surface radiometric quantities relevant to ocean color radiometry, R rs (λ), L w (λ), E d (z = 0 + , λ), and L u (z = 0 + , λ), where z = 0 + is just above the surface. The surface and depth profile values of several spectral radiometric quantities and AOPs, as well as PAR, are included in the second subset of data. The spectra of z eu and z 1 are also provided in the second file. More details on the organization and content of the database are included in the README file provided in the database.
Acknowledgements. We gratefully acknowledge all scientists and supporting personnel involved in the collection, processing, and dissemination of the in situ and satellite data used in this study as well as all agencies that provided support for these activities. We thank  Jérôme Vialard  for the generation of global SST data. We also thank  Jaime Pitarch  and two anonymous reviewers for comments on the manuscript. Financial support. This study was supported by the  ANR   CO2COAST  project (grant no. ANR-20-CE01-0021  awarded to  Hubert Loisel ) and the  National Aeronautics and Space Administration in the USA  through the  PACE  project ( NASA  grant no. 80NSSC20M0252  awarded to  Dariusz Stramski  and  Rick A. Reynolds ). Review statement. This paper was edited by  Salvatore Marullo  and reviewed by  Jaime Pitarch  and two anonymous referees.
d (λ) + a g (λ) = a dg (λ) represents the nonphytoplankton absorption coefficient with combined contributions of non-algal particles and CDOM. The backscattering coefficient of the different constituents are the spectral backscattering coefficients of phytoplankton, b b-ph (λ), and non-algal particles, b b-d (λ), such that the sum b b-ph (λ) + b b-d (λ) = b bp (λ) is the particulate backscattering coefficient. Among these constituent IOPs, the phytoplankton absorption coefficient, a ph (λ), plays the most fundamental role in the creation of the synthetic dataset of IOPs in this study. The a ph (λ) spectra in this dataset were derived from actual https://doi. org/10. 5194/essd-15-3711-2023Earth Syst. Sci. Data, 15, 3711-3731, 2023.
In addition, a microphone permits detection of snoring and snorting. The device provides data on sleep (time asleep, sleep efficiency, wake periods and sleep stages), pulse rate and respiration rate, and computes an apnea-hypopnea index (not available in the US). The Sleep Analyzer was included in a polysomnography (PSG) study involving 42 participants in a sleep laboratory. The respiratory rate had a bias of -0. 06 breaths per minutes and 95% limits of agreement (LOA) of [-0. 6; 0. 5] breath per minute. The heart rate had a bias of -0. 8 and a 95% LOA of [-4. 3; 2. 7] beats per minute  (30). Patients suspected to have obstructive sleep apnea syndrome completed a night at a sleep clinic with a simultaneous PSG and recording with the Sleep Analyzer (N=180). The sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) at thresholds of apnea-hypopnea index ≥ 15 events/h were, respectively, Se 15 = 88.
Advanced analytics: expert systems, threshold alarms These devices are installed with WiFi or Bluetooth, and some are also available in a cellular version (Scanwatch, Sleep Analyzer, BPM Connect, scale Body Pro, Thermo, and a glucometer) 6  provided with Withings Remote Patient Monitoring (RPM © ) solution. In this RPM solution, devices are pre-configured, shipped to the patient, and immediately available for use. After each measurement, the devices analyze the data and upload the results (e. g. , SpO 2 , body composition) to Withings HDS servers. Data visualization can be reviewed by the patient using the Withings Health Mate application on a smartphone, or with a web interface. Health Care Professionals (HCP) can also visualize their patient's data via the Withings web-based RPM interface.
Feedback and alarm sent to user and reference center Patients and clinicians can consult their data in the Withings Health Mate © application if not using the RPM solution application. In the RPM solution, patients are guided by a digital assistant (application on a smartphone), to maintain their adherence to monitoring on a daily basis. The digital assistant educates patients about the program and helps tracking, and a HIPAA compliant proactive phone support is provided to help patients who have not been able to activate their device. A setup confirmation is sent when devices are first installed. 6 Availability of the cellular devices depends on the geographical area. The RPM application is available in Europe but not in the US. Digital health tools to support and improve management of dialysis patients.
Finally, it reminds patients to take their readings, and shares tips on how to build a habit. The HCP-facing interface of the RPM is organized to display a complete history of the patients: all the health data from day one, alerts history, recent team notes, and current procedural technology (CPT) code completion history. Clinical potential of digital remote tools: the example of dialysisdependent chronic kidney disease patients' management, the case for Withings technology Remote monitoring of changes in volume status, vital signs (blood pressure, heart rate, respiration rate, oxygen saturation), sleep quality and physical activity are among the most useful and valuable when assessing ambulatory CKD5D patients  (32). Withings' digital health toolkit devices offer a fully automated, unobtrusive and remote monitoring of the main vital functions in ambulatory subjects.
Input Data, Data Structures, and Library The input of the algorithm presented in this paper is a 2-manifold triangulated surface mesh. A 2-manifold surface is characterized by the fact that each point of the surface admits a neighborhood homeomorphic to a disc and each border point admits a neighborhood homeomorphic to a half-disc. A triangle mesh is a type of polygon mesh commonly used in computer graphics. It is defined as a collection of vertices, edges, and triangular faces over which incidence and adjacency relationships are defined. Manipulating a 2-manifold triangle mesh, with reasonable time complexity, requires a dedicated data structure such as halfedge. This is particularly true for topological modifications. Our source code is based on the MEPP2 platform 3  , a C++ software development kit and GUI for processing and visualizing 3D surface meshes and point clouds.
The proposed source code is generic and can run with the following data structures: CGAL Polyhedral Surface, CGAL Surface Mesh, CGAL Linear Cell Complex, and OpenMesh. However, some CGAL restrictions occur currently on the geometric distance (e. g. Hausdorff or RMSE) computation between two meshes as this currently requires the CGAL 3D Fast Intersection and Distance Computation (AABB Tree) package which is not yet compatible with OpenMesh.
Correspondence Between Algorithms and Source Codes In this section, we point to the associated source files and functions that correspond to the two algorithms. Source files related to the progressive compression and decompression are located in the src/FEVV/Filters/CGAL/Progressive Compression folder. These source files are processing filters that work with the CGAL data structures: CGAL Polyhedral Surface, CGAL Surface Mesh, and CGAL Linear Cell Complex.
e. , features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.
The performance of classifiers run on a subset of handcrafted or black-box features is generally not scalable and usually sharply declines when used on datasets other than those used for classifier construction, lacking reproducibility and generalizability  3. The main reason is that the experimental samples available for the feature selection step are usually scarce or not so general to cover possible admissible variations, even occurring within the same biological conditions. In practice, when the results obtained on a smaller set of experiments are extended to a more general and independent plethora of cases, the performance is expected to degrade dramatically, as shown in Fig. 1  (left, red branch). No matter if in the context of handcrafted or Deep Transfer Learning (DTL) features  4, 5  (i.
To solve this problem, we present here a platform, named Deep-Manager (DM) (the blue branch in Fig. 1 ), that allows to identify and practically select the best features for a given classification task after extraction by customized functions or after transfer by a given user-defined pre-trained DL network. The term deep refers explicitly to deep features, for which the problem of efficient feature selection is unsolved, and the risk of bias is huge  3. However, as demonstrated in this work the platform may also run on handcrafted intensities and texture features commonly quantified in biomedical images. DM can therefore greatly help biologists in their everyday practice to verify the general validity of the rationally selected features. The DM platform identifies the extracted features that specifically represent the characteristics of the cell/tissue objects, discarding the nonspecific macroscopic variations that unintentionally occur in the training dataset.
These effects induce errors in the recognition model and misleading biological or clinical conclusions (e. g. , not-true drug response). In this regard, the DM platform allows efficiently selecting, among all the features extracted from a DTL neural network or by customized handcrafted descriptors, those that have a lower sensitivity to disturbances and, at the same time, a high discriminating power (Fig. 1  blue branch). After the application of the different degradation tests to the training dataset (Fig. 1  right expansion), features are characterized in terms of their discriminant power (DP) and sensitivity to the degradations (SENS), measured as the relative difference in DP values before and after degradation injection (see Methods for the details). A multi-threshold approach is then used to separate features with high DP and low SENS (cyan dots in Fig.
1  blue branch) from the other groups of features (low DP or/and high sensitivity, green and blue dots in Fig. 1  blue branch). Selected features can then be used in a classification task proposed by the user, where it is asked to upload an independent test set of labeled images, the test dataset, to verify the validity of the selected features by evaluating their DP over a different set (Fig. 1 ). The proposed approach is general and can be applied to any Deep Neural Network-based processing architecture by simply selecting a network among a proposed set of existing ones or, for users with programming skills, by modifying the Python software to integrate proprietary networks, new features, or also new perturbation tests. To demonstrate the usefulness of DM, we apply the software to five distinct case studies.
Case study #1: Fluorescence microscopy videos of chemotherapy-induced death of MDA-MB 231 breast cancer cells in the presence of a green fluorescent apoptosis reporter  10. Case study #2: TL microscopy videos of PC3 human prostate cancer cells, moving in a 2D environment in the presence of the chemotherapeutic drug etoposide  11. Case study #3: phase-contrast TL microscopy videos of immune cells moving in a 3D collagen gel inside microfluidic tumor-on-chip devices that mimic the tumor microenvironment  12. Case study #4: fluorescence microscopy videos of cancer cells forced to undergo apoptosis by cytotoxic T cells in 3D tumor-on-chip;  10  Case study #5: Phase contrast TL microscopy static images of BT-474 breast cancer cells from the recently presented public dataset LIVECell  13. In the following, we will refer to each case study simply using the case study number.
Results and discussion To demonstrate the effectiveness of the proposed Deep-Manager software platform, we selected five different use cases. The first use case concerns the exploitation of DM in extracting information from live-cell imaging, which is a common everyday practice among biologists. The clinical case refers to handcrafted intensity features related to GFP emission intensity in discriminating natural death from chemotherapy-induced death in MDA-MB 231 breast cancer cells  12. The second use case concerns the extraction of robust deep features by DTL in TL microscopy videos of PC3 human prostate cancer cells in a 2D environment in the presence of the chemotherapeutic drug etoposide  11. The third use case concerns deep features extraction in phase-contrast TL microscopy videos of immune cells moving in a 3D collagen gel inside microfluidic tumor-on-chip devices that mimic the tumor microenvironment  12.
The fourth use case considers the problem of extracting deep features in 3D fluorescence microscopy videos of cancer cells going into apoptosis due to the killing by cytotoxic T cells in 3D tumoron-chip  12. Finally, the fifth use addresses a classification task by using the deep features extracted using the DM platform. More specifically, we acquire phase contrast TL microscopy static images of BT-474 breast cancer cells from the recently published public dataset LIVECELL  13  , with the task to recognize the cancer growth factor after 4 h of culturing in a dish in a 5-day experiment. Details of each use case are provided in the next sections. Scores plot and classification results of the handcrafted features extracted (Case study #1). In this case study, we compared the individual discrimination performance of handcrafted features GFP derived in terms of the p-value of the Student t-test with and without the selection using DM-tool.
For the task of characterizing and discriminating between natural and chemotherapy-induced death, we selected four independent videos for each condition (8 in total), with a duration of 70 h, acquired at a time point of 1 h. We then automatically identified 431 crops, each containing a dying cell (i. e. , a cell that is going to die within 70 h) through the use of the STAMP code  10  , downloadable at https://cloudstore. bee. uniroma2. it/index. php/s/LEpHYTsPnDj4Ajt (password: STAMP2021). In particular, 96 crops are from natural death, and 335 crops are from cytotoxic T-cell death. Further details of the image processing step can be found in  10. Each crop has been characterized in terms of the following handcrafted features: average green emission in the crop, g mean , the total green emission in the crop g tot , the top 75th percentile of the green emission in the crop g 75 , the bottom 25th percentile green emission in the crop g 25.
With the aim of comparing different standard feature selection approaches with DM selection, we proceed as follows. First, we extracted the features from the original training dataset of images by performing transfer learning from a given CNN and related pooling layer. Then, we ranked the features according to a comparative method among those listed below and kept the first N sel where N sel is the number of features selected by DM. We then extract the DP and the SENS values for the selected features when computed over the artifacted dataset and use them for comparison. In this way, we verify the potential of selecting features using the sensitivity criterion in addition to the DP. For clarity, also METHOD 5 uses the DP criterion for ranking but without the evaluation of feature sensitivity.