dataesr/scientific-paragraphs-categorization huggingface dataset:
Data-centric active learning AI-driven precision segmentation of entire lung airways Automated quantification of bronchial and acinar NP dose ratios requires precise segmentation of the entire lung bronchial tree, which is a challenge in terabyte-scale LSFM datasets. Manual annotation of a single 3D LSFM image stack, consisting of 200-500 2D slices, would require over 100 hours. Segmenting all ca. 120 LSFM images included in this study would thus be impossible if performed manually. To address this, we used a data-centric active learning pipeline combining limited expert annotations with gradually added training samples through manual corrections of model predictions. Initial ground truth (GT) annotations of entire bronchial trees were generated by four lung specialists and underwent quality control.
Example reference annotations (GT) in 2D and 3D are shown in Figure  3a  and Video S1 and S4_1. We applied a modified version of nnU-Net  32  to the manually annotated training images from three lungs and then used human-driven active learning to select and correct cases for annotation, retraining the model. This allowed us to quickly expand our initial training set of 3 cases to 21 lung images (Figure  3b ). LSFM datasets are particularly challenging to segment due to their wide range of imaging artifacts, such as shadows, blurring, inconsistent illumination, and obscure lung regions, among others. Furthermore, LSFM was carried out using both visible (AF1, i. e. , excitation/emission (ex/em) =540/590 nm, Video S5) and infrared light (AF2, ex/em=740/790 nm, Video S6), which further complicates the segmentation task.
We thus incorporated dataset-specific data augmentation techniques simulating common imaging defects into the nnU-Net framework (Figure  3b ). Our iterative data generation and targeted augmentation approach enabled us to generate a highly robust segmentation method, capable of producing high-quality segmentations of entire airways in remaining unannotated images (>100 full lungs). The AI model and all airway segmentations are made publicly available to catalyze future research in the field. The AI method exhibits exceptional performance, as demonstrated by exemplary samples that display commonly found LSFM imaging artifacts (Figure  3c , Figure  S6a-d , Video S7). The modified nnU-Net also demonstrated remarkable resilience to worst-case scenarios with low AF intensity, massive artifacts, out-of-focus, and multiple imaging errors (Figure  S7 ).
The AI-generated airway segmentations were quantitatively evaluated using 5-fold Cross Validation during development and a held-out test set. For Cross Validation, the training data was randomly split into five folds and five models were trained on four folds each, with evaluation on the remaining one. The test set consisted of three full lung images not used during development, with Dice Score and centerline Dice Score (clDice) computed for every image and the mean reported. The validation set achieved a Dice Score of 0. 92 and a clDice of 0. 94, while the test set achieved a Dice Score of 0. 89 and a clDice of 0. 93. (autofluorescence AF in green), segmented GT (in blue), and merged images. b) Data-centric active learning AI approach conquered multiple imaging challenges for high-performance airway segmentation.
Datasets challenge compromised imaging shadows, blurring effects, out-of-focus center (poor illumination in lung center), inconsistent slice illumination, poor and variable signal-to-noise ratios, false gray structures, etc. With the data-centric active learning approach and method improvement, this study achieved superior segmentation quality as demonstrated with both visible light AF channel (AF1) and infrared light channel (AF2). c) Data-centric active learning AI-driven excellent labeling of complete lung airway. 2 exemplary AI segmentations of complete bronchial trees from either the whole-lung or lung lobe with different imaging errors (left: imaging shadow and right: blurring) were displayed. d) Novel regional deposition features like bronchial/acinar (B/A) deposition ratio (d1), Linear relationship between C/P ratio and B/A deposition ratio, 95% confidence interval indicated with dashed lines (d2), apparent NP-volume to dose in bronchial and acinar regions (d3 and d4), as well as longitudinal NP acinar and bronchial dose fractions (d5 and d6) were quantitatively computed by AI-assisted segmentation and analysis of NP dose in airways and acini.
In order to examine 3D spatially-resolved profiles like the ratio of NP deposition in the bronchial versus acinar regions, it was necessary to accurately segment the entire lung bronchial tree. To achieve this, we employed a data-centric active learning AI pipeline involving limited expert annotations and gradually added training samples by manually correcting the model's predictions. We discussed the challenges of segmenting LSFM datasets due to various imaging artifacts and applied dataset-specific data augmentation methods to generate highly robust segmentations. Exceptional performance of the modified nnU-Net in segmenting airways in LSFM images, even under worst-case scenarios, was achieved. Leveraging these AI-empowered airway models, we revealed that most of the delivered NPs were delivered to acinar regions with ratios of 0.
The NPs were mainly phagocytosed by AMs but also partially by IMs, and BAL collection could not completely remove all AMs from the lung. Additionally, an ex vivo lung model demonstrated that the relocation of NPs is due to cellular activity rather than passive respiration-induced motion. We also showed that breathing plays an important role in macrophage-mediated NP redistribution. Multispectral LSFM of intact lungs further disclosed widely-distributed NPs with respect to multiple lung networks particularly the endothelial systems. All data gathered here suggests both AMs and -to a lesser extent-IMs were considered to contribute to the relocation of NPs. This study challenges the traditional view of static lung TRMs  23  and shows that TRMs actively mediate intra-acinar NP transport through phagocytosis and patrolling, which is crucial for the long-term retention of NPs in the lung  24, 25.
Materials Melamine resin fluorescence particles (MF, ex/em = 636 nm/686 nm; volume median diameter (VMD): 474 nm, stock suspension: 25 mg/mL, microParticles GmbH, Berlin, Germany) were employed as the imaging tracer for multiple delivery routes and longitudinal observations under LSFM as described in our previous studies  19, 28. Native preparations of MF NPs were examined by transmission electron microscopy (TEM, Zeiss Libra 120 Plus, Carl Zeiss NTS GmbH, Oberkochen, Germany). Pictures were acquired at 4000-12500x magnification, using a Slow Scan CCD-camera and iTEM software (Olympus Soft Imaging Solutions, Münster, Germany). Intratracheal liquid instillation (ITLI) was applied to deliver a certain amount of MF particles (50 µL of 1:50 dilutions of MF stock suspension in 0. 9% saline water) to the mouse lung as previously described  28.
1 equipped with an incubation system, Zeiss). The lung core biopsy was submerged into the sample chamber containing phenol-red-free DMEM/F12 (Gibco) and 1% FBS. The sample chamber was heated to 37°C and 5% CO 2 was applied. After sample positioning, Z-stacks were acquired for every 20 minutes using a water-dipping 20x Plan-Apochromat objective (Zeiss). For subsequent analysis, all z-stacks were converted into a maximum intensity projection (MIP) by using ZEN2009 software (Zeiss). Confocal time-lapse microscopy was implemented on an LSM710 system (Zeiss) containing an inverted AxioObserver. Z1 stand equipped with phase-contrast and epi-illumination optics and operated by ZEN2009 software (Zeiss). Here, lung tissue was used as precision-cut lung slices (PCLS), which were submerged in phenol-red-free DMEM/F12 (Gibco) and 1% FBS in an imaging cell-culture dish with a glass-bottom.
This imaging dish was placed into a PM S1 incubator chamber (PeCon/Zeiss) and kept at 37°C and 5% CO 2. Z-stacks were acquired at a resolution of 512x512 pixels for every 20 minutes using an EC Plan-Neofluar DICI 10×/0. 3 NA (Zeiss). Z-stacks were taken according to the thickness of the PCLS and were ranging between 150 and 300 µm. For subsequent analysis, all z-stacks were converted into a maximum intensity projection (MIP) by using ZEN2009 software (Zeiss).
Microscopic images were obtained with a water dipping objective (20x, NA 1. 0, Zeiss, Oberkochen, Germany). Light from the specimen was separated with a beam splitter (T 580 lpxxr Chroma Technology Corp. , Bellows Falls, VT, USA) and acquired with two Rolera EM2 cameras and VisiView Imaging software (Visitron)  64. For visualization of TRMs, the PKH26 dye (PKH26 Red Fluorescent Cell Linker Kit for Phagocytic Cell Labeling, Sigma-Aldrich, Germany) was applied at least 5 days before imaging via oropharyngeal application  24. For particle inhalation, 40 µL 1:50 diluted MF particles were added to the nebulizer. Anaesthetized animals were euthanized by cervical dislocation at the end of the experiments.
Data-centric AI development for airway segmentation and NP regional dosimetry The automatic segmentation of airways has been accomplished using nnU-Net  32  , which is a self-adapting framework that automatically adapts a U-Net-based segmentation pipeline  31  to the dataset it is applied to. This is made possible by creating a dataset fingerprint which is then used to One batch consists of two patches, where one is taken from a random location and the other is guaranteed to contain annotated airway pixels. The loss function is the sum of the Dice and cross-entropy loss. During training the model is optimized using the SGD optimizer with an initial learning rate of 0. 01, which is decayed using a poly learning rate schedule. Data augmentation contains rotations, Gaussian noise and blur, brightness and contrast transformations, gamma corrections, simulation of low resolution and mirroring.
For inference a sliding-window approach is utilized with half-patch size overlap as well as a Gaussian patch center weighting. Moreover, mirroring along all axes is applied as a test time augmentation. Throughout the data-centric model development further dataset-specific adjustments were employed. The target spacing, which nnU-Net automatically sets to the median spacing of each axis, was doubled to decrease the image size by a factor of 2, allowing a more efficient training. Moreover, the data augmentation pipeline for training has been adapted to the relevant imaging artifacts present in the dataset. A custom slice illumination transform was used for simulating dark lung centers caused by illumination issues. Also blurring, contrast, brightness and sharpness transforms were applied locally in images to imitate blurred areas, halos, and different signal-to-noise ratios.
Lastly, blank rectangle transforms were used to mimic missing information due to artifacts in the images. Using the additional augmentations, we were able to train a model that is highly robust. Once the model was trained, unlabeled data was predicted and resulting airway structures were assessed. Afterwards, selected images were corrected and used for another iteration of model training. Manual labeling and correction were accomplished with a segmentation workflow using ImageJ (https://imagej. nih. gov/ij/) with the processes of e. g. setting an intensity threshold to obtain binary 2D images, applying a cut function to clean false airway structures, and using the paintbrush tool to draw missing airways and to close holes. The labeling of the initial images was performed by four lung specialists.
Labeling a whole lung consisting of 200-500 images with pixels of e. g. 2048×2048 for each image takes a few weeks even for lung anatomists. Consequently, only correcting predictions of our AI model instead of labeling a whole lung highly increased time efficiency. The selection of cases for a manual correction was based on a visual inspection of all validation images. Selected examples needed to have severe segmentation errors. However, diversity within the types of errors as well as dataset properties was considered to cover a maximum range of possible errors in each iteration, leading to improved models after every training iteration. Quality control in the form of cross-examination and corrections was implemented, resulting in a highly precise 3D segmentation of complete airway trees. With the complete 3D lung airway models generated from AI and deep learning pipelines, the particle doses in the airway (bronchial) and non-airway (acinar/alveolar) regions can be readily determined.
After centrifugation at 300g for 5 minutes, red blood cells were removed with 1mL lysis buffer for 30-40s. Single lung cells were then generated with addition of 5-6 mL RPMI+10%FCS to the sample tube, washed in 1 ml 1x fluorescence-activated cell sorting (FACS) buffer, and centrifuged for 5 mins at 300g at 4⁰C, and counted for total cell numbers and overall cell viability as described above. The single cell numbers were counted at most 1 million per 100 µL FACS buffer/tube and incubated with 50 µL Fc block (CD16/CD32, 1:100 in FACS buffer) for 20 mins at 4⁰C. After 30 min incubation with antibody suspension for 30 mins at 4⁰C, the single cell suspensions in 200 µL of MACS were then analyzed using BD FACSFortessa running FACSDiva software v8. 0. 1. Cell types differentiation and particle quantifications were performed using FlowJo v10.
Statistical analysis All data are presented as mean ± standard deviation (SD) and plotted using GraphPad Prism v9 (GraphPad Software, San Diego, CA). Comparison of results between two groups for normal distributed data was carried out using a two-sided or one-sided student t-test. Comparisons of more than two groups were performed using a one-way analysis of variance (ANOVA) followed by pairwise multiple comparison procedures (Holm-Sidak method). Significances are defined in exact values. Figure  S3 : Single-slice (and merged) view of particle distribution pattern in mouse lungs over a time course ranging from 0h to 14d after intratracheal liquid instillation (ITLI, a-d) and ventilator-assisted aerosol delivery (VAAD, e-h). Figure  S7 : Active learning AI-driven approach achieved high-quality labeling of lung airways from specimens with different kinds of imaging artifacts.
Acknowledgements This research was partially funded through the  EU Horizon 2020 research and innovation program  under grant agreement no. 686098  (project  SmartNanoTox ) and grant agreement No. 953183  (HARMLESS). This study was also partially funded by  Helmholtz AI  through a research grant  ALEGRA  no. ZT-I PF-5-121. Part of this work was funded by  Helmholtz Imaging , a platform of the  Helmholtz Incubator on Information and Data Science. The authors are grateful to  David Kutschke ,  Carola Voss ,  Andreas Schroeppel , and  Annette Feuthinger  for their excellent technical support.
Data availability The data supporting the finding of this study are available within the article and its Supplementary information, or from the corresponding author upon request. The raw LSFM lung images generated from LSFM, reference lung annotations, and AI-generated airway models are publicly available via Zenodo (https://doi. org/10. 5281/zenodo. 7413818) and computing codes for the AI model are provided via GitHub (https://github. com/MIC-DKFZ/MurineAirwaySegmentation).
Application development The CAITOM® application is software with a e-checklist and graphic media that aim to help initial assessment of patients with facial trauma. Its target audience is surgery students, juniors or seniors' residents, or specialists in the OMFS eld. The sequence diagram describes the processes in the use of the CAITOM® by a user (Fig. 3 ). First, the user should do the free downloading of the application for smartphone android system operation in the link cited [18 ]. Then, the user should put information about patient and evaluator identi cations and the date of assessment. So, the evaluator will use the e-checklist for managing patients' assessments. When signs or symptoms are present, they must be targeted in the illustration. Any observation also can be adding the length of the assessment.
In this work, the second-order work criterion is integrated at the structure scale and used to anticipate the effective failure of the structure. This allows us to stop the strength reduction procedure and save computation time. The open-access FEM code Cast3M, working as a command language, is used to code the proposed approach. The methodology is then tested on two different types of embankments: a heuristic slope without considering seepage and an existing earth dam in a normal operating situation. These two examples allow us to show that the proposed approach can be used to obtain relevant safety factors for cases of varying complexity.
Implementation Procedure of the Second-Order Work Criterion for Slope Stability in a FE Code This section focuses on the technical implementation of Hill's criterion in an FEM code to assess the stability of slopes from the strength reduction technique. The global implementation procedure is schematically described in Figure  2. It consists of three main parts: the construction of the FE model, the strength reduction technique loop, and the second-order work computation. In this article, the FEM code Cast3M is used  [29]. (Unlike others FEM code, Cast3M does not work like a 'black box'. Cast3M has a command language consisting of a series of operators that allow the user to manipulate data and results in the form of objects, giving them names with a specific language, which is both a programming and a modeling language  [29].
A complete code has been written for this work using Cast3M tools). A detailed description of the different steps of the implementation procedure is given as follows: Step 1-Initialization (pre-processing) of the FE model: The model geometry and the mesh are created and the conditions are set. For the material properties, an elasto-plastic constitutive behavior following Mohr-Coulomb yield surface, with no hardening/softening and a non-associated flow rule (ψ = ϕ = 0), is selected and the initial parameters are set. Step 2-FEM processing n • 0 (Initial equilibrium state): An initial equilibrium state is reached by FEM processing after the incremental application of the different loads up to their nominal values. This step constitutes a classical FE routine computation using an elasto-plastic model based on Mohr-Coulomb criteria.
Standard values have been selected for these parameters, which are believed to not have much influence on the safety factor value  [9]. Note that ψ′ is chosen to be different from φ′ which corresponds to the non-associated plasticity. No seepage is supposed to occur in the soil, so that the total stresses are considered in the computation. The values of the different parameters are reported in Table  1. The stability of this slope has been preliminary analyzed using the LEM with Bishop's simplified method. This approach provides a safety factor equal to 1. 21, corresponding to those computed with similar methods (i. e. , 1. 20, 1. To apply the methodology developed in the last section, the homogeneous slope is modeled using the FE code Cast3M  [29]. The mesh of the slope is similar to that used in  The stability of this slope has been preliminary analyzed using the LEM with Bishop's simplified method.
Properties Cohesion-Frictional Soil To apply the methodology developed in the last section, the homogeneous slope is modeled using the FE code Cast3M  [29]. The mesh of the slope is similar to that used in previous studies and is composed of four-node quadrilateral elements. These elements are triangular, close to the slope surface (see Figure  3 ). The FE model includes a nonassociated elasto-plastic behavior for the soil. In this case study, the Drucker-Prager criterion is selected as an approximation of the Mohr-Coulomb criterion. It smoothes out the singularities of the hexagonal pyramidal yield surface of the Mohr-Coulomb criterion. With the plane strain hypothesis, relationships exist to link the Drucker-Prager parameters with the shear strength parameters c and tan ϕ  [30]. Concerning the boundary conditions, the bottom of the mesh is considered fixed and only vertical displacement is allowed on the left boundary.
Since cold gas is the fuel of star formation, here we tackle this important question by exploring whether there is a direct link between 𝑀 BH and the cold gas content in galaxies. Our primary sample comprises of 69 central galaxies in the nearby Universe with direct estimates of BH masses derived from resolved kinematics of stars or gas  14, [17] [18] [19]. We have only included central galaxies to circumvent any environmental impact on the ISM properties of galaxies. The sample encompasses several types of galaxies, including spirals, lenticulars, and ellipticals. We obtained the HI 21-cm emission fluxes, which trace the atomic gas mass 𝑀 HI , through crossmatching with nearby galaxy databases and HI surveys. Details on the sample selection and derivation of their physical parameters are shown in Methods.
Sample selection: The BH sample The sample for galaxies with directly measured BH masses is primarily from Ref  14  , which includes 91 central galaxies collected from Ref  [17] [18] [19]. We excluded 18 sources with BH masses measured with reverberation mapping and only kept those measured with dynamical methods. We then added another 63 galaxies with measured BH masses from recent literature, which were matched with the group catalog  30  of nearby galaxies to select only central galaxies. We obtained the HI flux densities/masses of this sample by crossmatch with the well known nearby galaxy database, HyperLeda  31. Our final sample includes 69 central galaxies with 41 from Ref  14  and the remaining from the compilation of recent literature. In table  1 , we list the basic properties of our BH sample.
Bulge masses The bulge information is from Ref  36  and Ref  37  for the BH sample and galaxy sample, respectively. Linear Least-squares approximation We implemented linear regression for BH sample using python package LTS LINEFIT introduced in Ref  38  , which is insensitive to outliers and can give the intrinsic scatter around the linear relation. For galaxy sample, we implemented orthogonal distance regression with Python scipy. odr package.
Partial least square (PLS) regression To derive the most significant physical parameters in determining 𝑓 HI statistically, we utilized Python package Scikit-learn  39  with PLS Regression function, which implement non-linear iterative partial least squares (NIPALS)  40  algorithm. The PLS analysis has advantages in regression among highly-correlated predictor variables. It calculates the linear combinations of the original predictor datasets (latent variables) and the response datasets with maximal covariance, then fits the regression between the projected datasets and returns the model: 𝑌 = 𝑋 𝐵 + 𝐹, (4) where 𝑋 and 𝑌 are predictor and response datasets, 𝐵 is the matrix of regression coefficients and 𝐹 is the intercept matrix. We constructed 𝑋 and 𝑌 matrix as the set of 𝑀 BH , 𝑀 ★ , Σ star , 𝑀 bulge and the set of 𝑓 HI.
Data The data analysed in this article are based on a quantitative survey that we conducted in 2016 by means of an anonymous self-administered questionnaire (https://doi. org/10. 5281/zenodo. 3135076). The target group was made up of inhabitants and visitors present during the summer in the Arcachon Bay area. The questionnaire was distributed online and by post 3 , and we collected a sample of 351 respondents (234 residents of the 10 coastal cities around Arcachon Bay and 117 visitors). Our questionnaire consisted of three thematic sections (medicine use practices, disposal of unused medicines, and perception of environmental risk), each composed of questions linked to sustainable practices (see variables annotated with a star in Appendix 1). All practices were selected from the studies presented in Section 2.