dataesr/scientific-paragraphs-categorization huggingface dataset:
That means each 3D volume in the following experiments only has a size of 512×512×120. Particularly, there were 9200 2D images with the size of 512×512 selected from twenty-one patients in the TCIA dataset to simulate the cone-beam geometry as described in this section to generate the training dataset. Then, another 1000 2D images of the same size from different three patients were used to generate the validation dataset. Last, 1200 2D images selected from another three patients were utilized to generate the testing dataset. Specifically, the images from training, validation, and testing datasets belonged to different patients. The FDK algorithm was used to reconstruct the reference images from the full-sampled projection data. Two scanning angular ranges [0, 90°] and [0, 110°] were performed to assess different methods.
Because of the powerful feature extraction ability, DL-based methods get better evaluation scores than traditional methods in all scanning angular ranges. Compared to MSWDNet, DDNet brings at least 0. 7 dB promotion in terms of PSNR, which claims that the network with a more complex architecture may improve reconstructed results. Although DCAR gets worse scores in PSNR than DDNet, it performs well in SSIM, which means the image content provided by the DCAR is closer to the reference image due to the data consistency constraint. Notably, the proposed CROSS framework gains the best evaluations of all methods, which states the effectiveness of the multi-domain-based processing. To further evaluate the performance visually of various algorithms, Fig. 4  illustrates the reconstruction results and the corresponding regions-of-interests (ROIs) of FDK, TV,    c4 )).
5 (g2)(g3)) and accurate features (as marked by the red circle and arrow in Fig. 5 (g1)(g4)). Fig. 6  illustrates the selected coronal and sagittal views from the reconstructed CT volumes of the simulated dataset to further evaluate the visual performance of different reconstruction methods. As shown in Fig. 6 , all the DL-based methods can reduce most artifacts and preserve the most of tissue features. More specifically, our proposed CROSS framework works well in detail restoration (as suggested by the red arrows in Fig. 6 (e1)(e2)). However, the proposed method leads to obvious horizontal artifacts at different slices (as pointed by the yellow arrow and circle in Fig. 6 (e1)(e2)). This is because all the reconstructed results are 3D images but the proposed method processes them slice by slice, which ignores the relationship between adjacent slices in the z-axis direction.
B. Real Dataset Results To further investigate different reconstruction methods for limited-angle CT, real mice dataset experiments were conducted. Again, the cone-beam scanning model was adopted and its configuration is as follows. The tube was Hamamatsu L9421-02 and the detector was Dexela 1512, respectively. The tube voltage was 60 kV and the current was 130 μA. The size of the detector was 944 × 768 and each element represented 0. 072×0. 072 mm 2. The distance between the source and the detector was 44 cm and the distance between the source and the object was 22 cm, respectively. 1000 projections were collected via 360° as the full-sampled measurement data. The reconstructed 3D volume was 872×872×600 and each voxel covered the area of 0. 072×0. 072×0. 072 mm 3. There were four mice performed for this work.
Specifically, two mice (1200 images) were used as the training dataset, one mouse (600 images) was used as the validation dataset and the rest (600 images) was used as the training dataset. Similar to the previous section, we only reconstruct the central 600 slices for each mouse to alleviate the cone-beam artifacts. The FDK algorithm was adopted to reconstruct the reference images from the full-sampled projection data. One scanning angular range  [0,110°] was performed to assess various methods. Fig. 7  shows the selected reconstruction images between different algorithms. For better visual observation, all the images in Fig. 7  were cropped to the size of 448×448. All the DL-based methods can generate high-quality images with fewer artifacts than the FDK algorithm. Moreover, in comparison approaches, DDNet produces better results in edge preservation (as indicated by the yellow arrow in Fig.
C. Comparison Between CROSS and DIOR Deep iterative reconstruction methods have been applied for limited-angle CT and achieved superior performance to traditional DL-based methods  [43, 44, 47]. To compare the deep iterative method and the proposed method, additional experiments were conducted from the simulated dataset with the scanning angular range of 90°. The DIOR  [47]  was employed as the representative deep iterative method, which adopted the CNN model as the regularization function in the residual space to improve the image quality via the iterative process. Fig. 8  demonstrates the reconstructed results of the DIOR and CROSS methods. Both the DIOR and CROSS perform well in artifact removal and tissue restoration (as pointed out by the yellow arrows in Fig. 8 (b2)(c2)). In addition, the CROSS framework can restore accurate structural features (as marked by the red circle in Fig.
D. Computational Cost Table II lists the computational cost of various methods. All the time was computed based on the simulated dataset with 200 images and a scanning angular range of 110°. MSWDNet and DDNet are single-domain-based methods that take the least time. IR-based methods (DCAR and DIOR) consume much longer time than other algorithms. Because of three networks and one forward-backward operation, the proposed method spends more time than MSWDNet and DDNet. However, compared to DIOR, CROSS can bring similar results within an appropriate time.
e. , CROSS. Table  III  gives the quantitative evaluations of the progressive ablation study. It can be observed that the SS-Net brings higher scores than the baseline model in terms of PSNR, and SSIM because the structure enhancement sub-network could strengthen tissue preservation. Meanwhile, with the assistance of DC and ID-Net, the third model further promotes the reconstructed results both in CT values and image features. Replacing the DC with the learned DC, the fourth model can enhance the data consistency constraint and lead to improvements in all assessments. Notably, the proposed CROSS method gains the best evaluations in all comparison models, claiming the validity of the RDC and residual-space processing. Besides, to explore the mechanism of different components in CROSS, the reconstructed results of various comparison models are demonstrated.
Besides, the residual-space-based processings can further improve CROSS in tissue restoration. In addition, the utilization of the structure enhancement module also results in the promotion of the reconstructed images. Both the simulated and real datasets are performed with different scanning angular ranges to validate the proposed method. Compared to existing methods, the CROSS framework performs well in artifact reduction and edge preservation. There are some similar works to the proposed method. For instance, to overcome the instabilities of deep learning, Wu et al. proposed an ACID framework with a combination of deep learning and compressed sensing theory  [37, 38]. The key idea of ACID is to employ the pre-trained network to constrain the residual image. Inspired by it, more advanced works have been developed for sparse-view CT  [39, 40].
This work was supported in part by the  State Key Project of Research and Development Plan  under Grants  2022YFC2408500  and  2022YFC2401600 , in part by the  National Natural Science Foundation of China  under Grant  T2225025 , in part by the  Key Research and Development Programs in Jiangsu Province of China  under Grant  BE2021703  and  BE2022768. (Corresponding authors:  Xu Ji ,  Hongbin Han ). This work involved human subjects or animals in its research.
For example,  Martucci et al. (2010a)  combine the height of maximum negative gradient and height of maximum variance to detect M BLH both during day and night together with lofted, decoupled aerosol layers. STRAT-2D  (Morille et al. , 2007; Haeffelin et al. , 2012)  uses the variance field to determine which wavelet-detected layer boundary is likely associated with M BLH by analysing the location of the EZ. pathfinderTURB  (Poltera et al. , 2017)  and STRATfinder (using advantages of STRAT+ and pathfinderTURB;  Kotthaus et al. , 2020)  combine gradient and variance field diagnostics before tracing M BLH. COBOLT  (Geiß et al. , 2017)  uses a combination of gradients, variance statistics and WCT, varying with solar angle to identify the M BLH. The profile-fit approach is combined with WCT by  Sawyer and Li (2013)  and with the negative gradient detection in the proprietary Vaisala BLview software  (Münkel, 2016) , respectively.
Provided sufficient SNR and careful data processing, CBLH from all retrieval methods can agree within a few hundred metres. A large entrainment zone (EZ) can result in a weaker delineation at the CBLH, increasing uncertainty in layer detection for all methods. As convective clouds can significantly challenge layer detection, daytime maxima of the layer estimates from temperature-, turbulence-, and aerosol-based methods are most similar in cloud-free conditions. However, also strong shear layers or elevated aerosol layers above the ABL can challenge turbulence-based and aerosol-based algorithms, respectively. The evening transition of the daytime CBL into the nocturnal boundary layer around sunset is directly monitored by turbulence profiles. While turbulence-based layer retrievals and thermodynamic methods are in general agreement at this time of day, aerosol-based MBLH results are particularly uncertain during this time of evening decay as it tries to track the history of recent turbulence activity.
Acknowledgements. This article is based upon work from  COST  Action  CA18235  PROBE, supported by  COST (European Cooperation in Science and Technology , www. cost. eu). Support from  COST Action   ES1303 TOPROF  is also acknowledged. SK is funded by  DIM-Qi2 (Réseau de recherche Qualité de l'air en Ile-de-France)  and  Agence National de la Recherche  under contract # ANR-20-CE22-0013  (H2C). LAA, JABA, JLGR thank the  Spanish Ministry of Economy and Competitiveness (MINECO)  for support through project  ELPIS  ( PID2020-120015RB-I00 ),  INTEGRATYON   3  ( PID2020. 117825GB. C21 ), the  Regional Government of Andalusia  through project  AEROPRE  ( P18-RT-3820 ) and  ADAPNE  ( P20-00136 ), and the  University of Granada ,  Programa Operativo FEDER Andalucia   2014-2020  through project  DEM3TRIOS  ( A-RNM-430-UGR20 ) and the  Earth System Excellence Units Program.
JABA received funding from the  Marie Sklodowska-Curie Action Cofund 2016 EU project -Athenea3i  under grant agreement no. 754446. MJC is co-funded by national  Portuguese  funds through  FCT -Fundação para a Ciência e Tecnologia , I. P. , in the framework of the  ICT  project with the references  UIDB/04683/2020  and  UIDP/04683/2020 , as well as through  TOMAQAPA  ( PTDC/CTAMET/29678/2017 ) project.
Multivariate data analysis including Principal Component Analysis and Partial Least Square Discriminant Analysis successfully identified statistically significant features responsible for group segregation. EAD fragmentation helped to identify unknown features and to confirm their molecular structure using fragmentation spectra DB annotation or machine learning. EAD database matching accurately annotated five glycerophospholipids, including the position of double bonds on fatty acid chain moieties. SIRIUS database predicted structures of unknown features based on experimental fragmentation spectra to compensate for database incompleteness.
Advancements were observed in experimental design, data acquisition methods, data preprocessing software, databases, and data processing  [3]. Research studies that aim to generate novel data for hypothesis generation of potential biomarkers typically utilize high-resolution mass spectrometry (HRMS) for untargeted data acquisition. In the untargeted approach, the simultaneous acquisition of high-resolution MS 1 and the corresponding MS/MS spectra of precursor ions can be achieved using either Data-Dependent Acquisition (DDA) or, conversely, Data-Independent Acquisition (DIA). Indeed, these acquisition modes are nowadays, routinely used to acquire high-resolution fragmentation spectra for thousands of compounds in one single analysis  [4]. In DDA, a limited number of precursor ions is selected for fragmentation based on their intensity exceeding a predefined threshold.
Indeed, after a biphasic extraction protocol (MTBE/MeOH), we examined the lipidome (RPLC fraction) and metabolome (HILIC) of female Gammarus fossarum, a freshwater amphipod at two distinct reproductive stages (C1 and D1). We compared data sets acquired in DDA, SWATH-DIA in terms of reproducibly detected features. Next, we evaluated the benefits of the Zeno trapping on the intensity of the fragments generated in SWATH-DIA as well as their numbers and especially for low abundant compounds. We examined the lipidome by RPLC separation (apolar fraction) and the metabolome by HILIC (polar fraction) of female Gammarus fossarum, a freshwater amphipod routinely used in Ecotoxicology  [37] [38] [39] , at two distinct reproductive stages (C1 and D1). Multivariate data analysis encompassing an unsupervised approach (principal component analysis, PCA) and supervised modelling (partial least squares discriminant analysis, PLS-DA) was applied to visualize and explain data variability and to highlight specific molecular features involved in reproductive stage differentiation.
9. Statistical analyses Statistical analyses were carried out using the R program (v4. 2. 1) with the ropls and mixOmics packages. Initially, Principal Component Analysis (PCA) was performed on all the different datasets. PCA enabled the assessment of analysis quality by projecting blanks and QC samples. It also facilitated the identification of differences between samples (reproductive stages C1 vs D1) and the detection of potential outliers. Subsequently, various models were developed, specifically Partial Least Squares -Discriminant Analysis (PLS-DA), to identify and select the variables responsible for the observed separations between the two reproductive stages. Variable selection was performed based on VIP scores and S-plots. The statistical significance of the models was assessed using the confusion matrix and validation set permutations.
10. Compounds annotation with MS-DIAL module and SIRIUS The raw data were acquired using SCIEX OS software (v3. 0) and subsequently processed with MS-DIAL software (v4. 9). The identification of compounds was performed using RIKEN Institute ESI-MS/MS DB from authentic standards databases, based on CID MS/MS spectra. For comprehensive lipid structural identification utilizing EAD fragmentation, the latest version of MS-DIAL (v5. 1) was employed, which includes the MS/MS-EIEIO lipidomics database. To predict the structure of unknown metabolites based on CID and EAD spectra, SIRIUS (v5. 6. 3) in conjunction with CSI:FingerID was utilized to obtain partial information about the molecular structure  [21] [22] [23] [24] [25] [26] [27].
Comparison of SWATH-DIA and ZENO-SWATH-DIA. For each dataset, we determined the features shared by SWATH-DIA and Zeno-SWATH-DIA. We then extracted and compared the corresponding MS/MS spectra for each pair of features in terms of fragment intensity for fragments present in both, as well as the total number of fragments for shared features (Fig. 2 ). In HILIC(+), a total of 343 fragments were used, 523 in HILIC(-), 53 in RPLC(+), and 28 in RPLC(-). To assess the increase in MS/MS sensitivity using the Zeno trap, the comparison was conducted at three n-fold increases: fragments detected with an intensity increase above 5-fold (> 5-fold), above 7fold (> 7-fold), and more than a 10-fold increase (> 10-fold) (Fig. 2A ). Overall, the > 5-fold increase accounted for approximately 50-60% of fragments depending on the dataset, with a combined percentage of 52% (Fig.
2A ). The > 7-fold increase covered 40% of the total fragments, while between 19-36% exhibited a > 10-fold increase, resulting in a global percentage of 22%. Notably, higher percentages were observed for RPLC datasets in terms of > 7-fold and > 10-fold increases (Fig. 2A ). RPLC emphasizes lipid species with fewer CID fragments compared to metabolites, making the increase more obvious on a smaller number of fragments, thus explaining the higher percentages for RPLC datasets. In conclusion, the Zeno trap significantly enhances MS/MS sensitivity for small biomolecules (lipids and metabolites) in both chromatography modes. This observation is particularly valuable for the molecular characterization of unusual biomolecules. This leads us to question whether the Zeno trap facilitates the detection of initially undetected low-abundance fragments.
To demonstrate this, we highlighted the Zeno trap's impact on the fragment number gain for shared features at three n-fold gains: > 2-fold gain, > 4-fold gain, and > 6-fold gain (Fig. 2B ). Globally, out of the 699 total shared features, 78% of them obtained at least twice as many fragments when using the Zeno trap. This trend is consistent across HILIC(-), RPLC(+), and RPLC(-) datasets, with a 32-38% increase for > 4-fold gains and an 11-18% increase for >6-fold gains (Fig. 2B ). However, HILIC(+) demonstrated notably high percentages with 67% for > 4-fold gains and 60% for > 6-fold gains (Fig. 2B ). This suggests that small positively ionized metabolites may have a significant number of initially undetected fragments due to their low intensity, which the Zeno trap makes visible even in small quantities.
To illustrate the benefits of the Zeno trap for putative MS/MS identifications, we used a combination of the Reverse Dot Product and the fragment presence ratio of MS-DIAL for comparison. This combination relies solely on the presence of reference fragments in the experimental spectrum to generate an annotation score, without being biased by artifacts. Putative identifications were conducted using the same MS/MS databases and identification score thresholds. The comparison was performed at three progressive identification scores: 50%, 70%, and 80% similarity scores (Fig. 3 ). In our case study, a score above 50% provides relevant insights into the structure of the unknown feature without necessarily confirming its identification. Scores above 70% indicate highly plausible identity suggestions, while scores above 80% practically confirm the putative identification.
In HILIC, an improvement of 41% in identifications with a score above 50% was observed when considering both polarities together (Fig. 3 ). This improvement increased to 46% for scores above 70% and 43% for scores above 80% (Fig. 3 ). For RPLC(-), no significant differences were observed due to the limited number of putatively identified features, providing little room for meaningful comparison. However, in RPLC(+), there was a substantial increase of 92% for scores above 70% and 72% for scores above 80% (Fig. 3 ). Overall, there was a 57% increase in almost certain identifications with a score above 80% when considering both HILIC and RPLC datasets together. Therefore, the Zeno trap demonstrates promising results for the putative identification of unknown small biomolecules in complex biological matrices.
4. Multivariate statistical analysis applied on HILIC and RPLC datasets. To assess the suitability of the Zeno-SWATH-DIA method, we investigated molecular differences of two reproductive stages of female crustacean Gammarus fossarum. After a biphasic extraction (MTBE/MeOH), both fraction aqueous (polar metabolites) and organic (nonpolar compounds, mainly lipids) were analysed in HILIC and RPLC respectively. Collected data in both polarities were subjected to unsupervised analysis. PCA analysis of the HILIC(+) dataset exhibited a clear separation between the C1 and D1 groups as well as a well-defined cluster of QCs after correction and a cluster of off-centre blanks (Fig. S8 ). Similar trends were observed in HILIC(-), RPLC(+), and RPLC(-) data sets (Fig S8 ). PLS-DA models were built to identify the variables responsible for the separation between C1 and D1 organisms (Fig.
4 ). The quality of these models was assessed using bootstrap resampling. The R 2 X, R 2 Y, Q 2 Y, and RMSEE values obtained from the PLS-DA models are presented in the supplementary data (Table  S4 ). These values were used to determine the optimal number of principal components (PCs). Evaluation of the model performance using two PCs revealed R 2 Y values ranging from 0. 71 to 0. 94 and corresponding Q 2 values ranging from 0. 42 to 0. 90 (Table  S4 ). Clear separation between the C1 and D1 stages was observed in all four datasets, with the separation being more pronounced and reliable in the HILIC datasets. VIP scores derived from the PLS-DA analysis were used to select the variables responsible for stage separation. VIP scores greater than 1. 5 for HILIC(+), 1. 2 for HILIC(-), 1. 5 for RPLC(+), and 1.
1 for RPLC(-) within each dataset were used as selection criteria (listed in Table  1A -B). This criterion resulted in a list of 32 features (each represented by the pair, RT_m/z), including 10 features for HILIC(+), 2 for HILIC(-),  16  for RPLC(+), and 4 for RPLC(-). These 32 significant features, along with their MS-DIAL identification number, VIP score, RT, precursor mass, identification (if available), and p-value from the Wilcoxon test, are listed in Table  1A -B.
5. Lipid structure refinement using EAD. To go deeper into structural determination of the molecules identified as features in positive ion mode, Electron-Activated Dissociation (EAD) was used. Among the 16 most relevant features in the RPLC(+) dataset, only 5 compounds were confidently identified through CID MS/MS database matching, while the rest remain unknown. Interestingly, out of those 6 lipids, the phospholipid PC 36:3 was identified and highlighted as an important feature in both RPLC(+) and RPLC(-) datasets. This lipid was identified at the same RT in positive and negative ion mode, and the obtained trends are identical, increasing the confidence in the results (Fig. 8 ). In CID, the prominent and intense fragment observed is the loss of a phosphocholine fragment at m/z 184. 07, which is a common fragment (polar head group) shared between phosphatidylcholines (PC) and sphingomyelins (SM).
Concerning the other relevant features, those partially identified lipids were further confirmed and elucidated using EAD. Specifically, PC O-34:3 was identified as PC O-16:0/18:3  (9, 12, 15) , PC O-36:5 as PC O-16:0/20:5  (5, 8, 11, 14, 17) , PC O-36:1 as PC O-18:0/18:1(9), and TG 45:0 as TG 14:0/16:0/15:0. However, the unknown features that did not match any CID MS/MS spectra in MS-DIAL remained unidentified even after MS/MS EAD spectral database, that can be attributed to the limited comprehensiveness of the database.
7. Database-free approach by SIRIUS for structure investigation using EAD To go further into the structural elucidation of the metabolites and lipids features in gammarids we combined EAD and an in-silico free database approach with SIRIUS  [21] [22] [23] [24] [25] [26] [27]. Currently, most of the databases used for the structural elucidation of metabolites are based on CID MS/MS spectra. Since EAD fragmentation is more recent, only MS-DIAL version 5. 1 software integrates EAD for lipid identification. Nevertheless, this database remains partial, and potential identifications are limited to 'known unknowns,' meaning the compounds in the database. However, database-free solutions that provide structural propositions through the investigation or prediction of fragmentation spectra are an engaging alternative.