dataesr/scientific-paragraphs-categorization huggingface dataset:
Fiber-based Raman spectroscopy in the context of in vivo biomedical application suffers from the presence of background fluorescence from the surrounding tissue that might mask the crucial but inherently weak Raman signatures. One method that has shown potential for suppressing the background to reveal the Raman spectra is shifted excitation Raman spectroscopy (SER). SER collects multiple emission spectra by shifting the excitation by small amounts and uses these spectra to computationally suppress the fluorescence background based on the principle that Raman spectrum shifts with excitation while fluorescence spectrum does not. We introduce a method that utilizes the spectral characteristics of the Raman and fluorescence spectra to estimate them more effectively, and compare this approach against existing methods on real world datasets.
A promising compromise between the two approaches is shifted excitation Raman spectroscopy (SER). The principles of SER are founded on Kasha's rule and Stokes shift. Kasha's rule states that the fluorescence emission is unaffected by a small change in excitation wavelength  [17] , while Stokes shift dictates that the Raman spectrum shifts by the same amount in wavenumber as the shift in excitation. SER collects multiple emission spectra by shifting the excitation by small amounts and uses statistical tools to separate these spectra into fluorescence and Raman components. Existing approaches for separating these two spectra, however, are sub-optimal since they do not explicitly utilize their spectral characteristics. Our objectives are to, 1) perform a literature survey to review existing methods of removing fluorescence based on statistical and machine learning tools (section II), 2) present an algorithm that utilizes the underlying structure of fluorescence and Raman signals as prior information to effectively estimate them from observed spectra (section III), 3) compare existing methods and the proposed approach on real reference datasets as well as human lung tissue data over several diverse evaluation metrics (section IV), 4) present a Python package implementing these tools to underpin reproducibility.
proposed shifted independent component analysis SICA to solve Eq. (  5 ) under normal noise model and the assumption that r m s are independent  [24]. The flexibility of these methods lie in estimating unknown shifts which can also be non-integer but this comes with additional computational complexity, and although both methods have shown to correctly identify latent spectra on synthetic data with unknown shifts, they have not been applied on real datasets. This approach can be adapted to fluorescence suppression by letting M = 2 with r 1 = r and r 2 = f , and by letting ∆n 2• = 0 to ensure fluorescence does not vary over shifted excitations while ∆n 1• s are known. To differentiate the generic formulation from this specific one, we refer to the latter as SNMF* and SICA* respectively. We observe that although SNMF* can recover latent spectra adequately the resulting Raman spectrum is not sparse, and SICA* often fails to adequately recover the Raman spectrum since it can take negative values even though independence between the two spectra is a desired assumption.
The hollow-core NCF is used to transport light to the sample whilst the surrounding multimode fibers collect the Raman scattering from the sample. The collected Raman signal is coupled via the dichroic mirror into a multimode patch cable and fiber-fed to the spectrometer (QePro Raman, Ocean Insight) which records the Raman spectra via the commercial software OceanView (Ocean Insight) within the spectral range of 840 nm to 992 nm (834 cm -1 to 2658 cm -1 in Raman shift for excitation 785 nm). We investigated two reference samples, CYCLOHEXANE and SESAME OIL, and ex vivo LUNG TISSUE from human lung. For liquid compounds, the distal end of the optical fiber was immersed in the compound but for tissue it had direct contact with the surface. 1) Cyclohexane: Cyclohexane (1. 02822, cyclohexane for spectroscopy Uvasol ® , Supelco, Merck KGaA) is a chemical compound popular in Raman spectrometer calibration due to its well known Raman spectrum and the fact that it has no fluorescence background.
D. Results 1) Effect of photobleaching: MSERS explicitly captures the effect of photobleaching where the relative intensity of the Raman spectrum compared to the fluorescence background vary over progressive measurements. Fig. 2  shows the ratio of Raman and fluorescence intensities, i. e. , (α k max n r n )/(β k max n f n ), in the order the measurements were taken for each dataset. We observe an upward trend for LUNG TISSUE indicating photobleaching while CYCLO-HEXANE and SESAME OIL do not vary. This is expected  for CYCLOHEXANE since it does not have any fluorescence. SESAME OIL shows the value indicating relatively high presence of fluorescence compared to LUNG TISSUE. 2) Changing number of excitations: Although a higher K is expected to infer better spectra, a lower K may be preferred for in vivo applications to reduce data collection time and motion artefacts, e.
g. , due to breathing. We assess if fewer measurements can provide adequate accuracy. We compare the performance of MSERS when changing the number of excitations, K. We use K ∈ {2, 4, 6, 8, 10} where the spectra are chosen to maximize the separation of the excitations (see supp. VIII). Table  II  summarizes the key metrics for all K, and Fig. 3  shows the estimated spectra. We observe that, both qualitatively and quantitatively, the estimated spectra progressively improve in signal-to-noise ratio as well as in precision and recall for increasing K, however, 1) even K = 2 provides effective fluorescence suppression, e. g. , in CYCLOHEXANE, and 2) K = 8 and K = 10 provide similar performance in precision and recall for all datasets. 3) Comparison to existing methods: We use K = 10 for comparison with other methods.
Fig. 4  shows the inferred spectra for different algorithms, and Table  III  summarizes their characteristics. We observe that qualitatively MSERS provides a 'peaky'-er Raman spectrum over zero baseline and a smoother background spectrum. This is supported quantitatively by a relatively higher sparsity and SNR compared to other approaches and a relatively more accurate location of the peaks as well as narrow peak widths (see supp. IX)). We observe that 1) AIRPLS provides a relatively noisy Raman spectrum in terms of SNR while RSERDS provides a broader Raman spectrum (see e. g. , peaks in LUNG TISSUE), and both methods result in low precision and recall. 2) PCA works well for CYCLOHEXANE but performs poorly on the other datasets. 3) SICA* provides uncorrelated spectra that can take negative values (see e.
IX. ADDITIONAL INFERRED SPECTRA EVALUATION Table V presents additional characteristics of the resulting spectra in Fig. 4  (see section IV-D. 3). We expect all the information in the observed spectra to be contained in the inferred Raman and fluorescence spectra, resulting in residuals that are 'white noise', i. e. residuals with no autocorrelation. The Durbin-Watson test statistic provides a measure of this autocorrelation in residuals, where values around 2 suggest no autocorrelation and values below (above) suggest positive (negative) autocorrelation. The test statistic was calculated for each observed spectrum and the value furthest from 2 was reported. Although all values in Fig. 4  are below 1, MSERS shows the highest across all datasets. The table also gives peak start and end positions, without the assumption of symmetry as well the relative intensities of the peaks that are not presented in Table  III.
ACKNOWLEDGMENTS Y. T. conceptualizes the idea, develops theoretical frameworks, designs computational tools, and writes the manuscript. H. D. H. and G. Z. X contribute equally to literature collection, mathematics proofreading, and manuscript revision. Z. Y. Z. contributes to technical support. P. S. contributes to idea conceptualization, manuscript writing, and project supervision. The authors are grateful for the kind help from  Aohua Cheng , a member of the  Tsien Excellence in Engineering Program at Tsinghua University ; and  Moufan Li , a student from the  Department of Computer Science at Tsinghua University. This project is supported by the  Artificial and General Intelligence Research Program of Guo Qiang Research Institute at Tsinghua University  ( 2020GQG1017 ), the  Huawei Innovation Research Program  ( TC20221109044 ), and the  Tsinghua University Initiative   Scientific Research Program.
Field Measurements A major characteristic of the River Erpe are the daily fluctuations in solutes and discharge deriving from the regularly fluctuating inflow volume of treated wastewater  (Jaeger et al. , 2019; Peralta-Maraver et al. , 2018; Schaper et al. , 2019). To capture these fluctuations we installed data loggers measuring EC, water pressure and water temperature at three locations:.
Comparison to Tracer Test Data In June 2016 several slug injections of a fluorescent tracer were performed in River Erpe along Section 1. The detailed methods and analysis of the tracer tests are described in  Romeijn et al. (2021). For the purpose of the present study, we aimed to use two of the slug tests (Injection 1 starting at hour 1,640 [before mowing] and Injection 2 starting at hour 1,760 [after mowing]) to compare the resulting parameter estimates to the results of the time series analysis of EC curves. Approximately 25 g of fluorescein were dissolved in 10 L of stream water and released into the river across the width of the channel aiming to reach a concentration of 33 μg L -1 in the river. The injection point was close to the confluence of the river and the treated wastewater, roughly 700 m upstream of Station A.
Acknowledgments The work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under  Marie Sklodowska-Curie  grant agreement No  641939  and additionally from the  Research Training Group "Urban Water Interfaces (UWI) " ( GRK 2032/1 ) funded by the  German Research Foundation (DFG). We also thank all members of the  HypoTrain  project and the  IGB Berlin laboratory technicians  for their valuable help and contributions. Open Access funding enabled and organized by  Projekt DEAL.
Extracts and bioactive bands were also analyzed using a LC-HRMS method. Data were then integrated to get insight on the substances driving the estrogenic activity of soy protein isolates. Isoflavones compounds (Table  1 ) and reference compound, 17β-estradiol (E2, CAS N • 6160-78-7) were purchased from Sigma-Aldrich, Darmstadt, Germany. QuEChERS salts (5,982-7,650) were provided by Agilent Technologies Inc. , Basel, Switzerland.
The solution was transferred into an Amicon Ultra-0. 5 Centrifugal Filter Unit 3K (Merck, Darmstadt, Germany) and ultracentrifuged at 14,000 rpm at 4 • C for 40 min. The eluate was collected and transferred in a vial before injection. The liquid chromatography high-resolution mass spectrometry (LC-HRMS) method was performed as described by Bessaire et al. (21). The analyses were conducted on a Vanquish Horizon UHPLC system coupled to a Q-Exactive HF-X HRMS instrument (Thermo Fisher Scientific) controlled by TraceFinder 4. 1 software (Thermo Fisher Scientific). The chromatographic separation was performed on a BEH C18 column (150 × 2. 1 mm (i. d. ), 1. 7 µm) (Waters, Milford, MA, USA) heated at 50 • C with a flow rate of 400 µL/min. The injection volume was 10 µL. Two chromatographic runs were considered either in alkaline or acidic conditions.
Data analysis The data obtained using the ERα CALUX assay were reported as Relative Induction (RI) by normalization of the maximum Relative Luminescence Units (RLU's) signal of the reference compound (E2) to 100%. The tested compounds were then expressed as a percentage of the maximum reference compound response. All dose-responses and dilutions were performed in technical triplicates. The HPTLC biodensitograms were evaluated using the peak height by integrating each peak with the software VisionCats 3. 0 (CAMAG). Data were analyzed using the average of triplicates performed for each pure compound and soy extract considering the dose response effect. The solvent control and reference compound (E2) were applied in each plate to confirm the validity of the test. Twenty peaks in the solvent control track were used as the mean blank sample.
The results were expressed as a percentage of the maximum activity by normalizing the maximum peak height signal obtained from the reference compound (E2) to 100%. Data graphs were produced using GraphPad Prism 9. 0. 0 (GraphPad Software LLC, San Diego, CA, USA). For the analytical data for recovered bands, differential analyses (Rf 0. 4 and Rf 0. 6 vs. silica blank) were performed with Compound Discover 3. 0 software. Results were filtered using p-value (<0. 01), area ratio between groups (>4) and identification from Arita lab Flavonoids list (containing 6,549 flavonoids). Signals were identified using in-house and web-based spectral libraries, accurate mass, and retention time.
Estrogenic activity of soy protein isolates Two soy isolates were extracted using the QuEChERS method without the cleaning step. Indeed, the cleaning step is impacting both, the sample recovery and a loss of bioactive substances [e. g. , estrogenic substances (data not shown)]. The soy extracts  were tested for estrogenic activity using CALUX assay and HPTLC p-YES. For both bioassays, dose-response curves from extracted samples were obtained in triplicate. As positive control, estradiol was used in each experiment, from 0. 03 to 27. 24 pg/well and at 5 pg/band for CALUX and p-YES assays, respectively. Transcription activation of estrogen activity was recorded in the two soy isolates with both bioassays. A clear bioactive zone with a dose-response effect was observed with p-YES (Figure  1A ).
Compound identification and quantification in bioactive bands A suspect analytical screening strategy was applied on the bioactive band recovered from the HPTLC plate to assign estrogenic activity to individual compound(s)  (8) (9) (10). The samples were analyzed by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS). Several signals potentially linked to estrogenic flavonoids were highlighted. Some signals were attributed to previously identified isoflavones such as genistein, glycitein and daidzein, known for their estrogenic activity (11). Two additional signals were tentatively identified as naringenin and formononetin based on comparison of their fragmentation data with in-house and publicly available spectral databases (Figure  2 ). Their identity was eventually confirmed by injection of reference standards.
Testing each individual substance in CALUX and p-YES bioassays identified 6 of them exhibiting measurable estrogenic activity in both tests. Variable potencies were observed according to individual substances, with some differences of ranking depending on the bioassay applied. Taken together, these data indicate that the intensity of bands visible on the p-YES plate depends on a combination of both substance-specific levels and estrogenic potencies. These two factors most likely explain the observation that a single bioactive zone is visible in the p-YES analysis of soy protein isolates. Genistein is both potent and present at high levels in the isolates, leading to the production of a very strong signal and, therefore, it was easily visible. The other isoflavones may not be visible because of either too low levels (e.
To address this need, bioassays are thought to play an important role. But to be able to interpret bioassay data from a safety perspective and, if needed, to allow for the development of management options requires understanding the origin of the activity. This is not straightforward. Based on the present work, the following strategy can be proposed to fully characterize the endocrine activity of a complex mixture: 1. Qualified sample preparation procedure (avoid cleaning step as it may remove endocrine and probably other type of bioactive substances). 2. Assess overall activity in a bioassay in liquid format. This requires using a validated bioassay. 1. In parallel, test the same sample in HPTLC-bioassay. 2. Run suspect analysis of the extracts and bioactive zones. Identify possible active substance candidates.
A new Mean Sea Surface (MSS) called DTU21MSS for referencing sea level anomalies from satellite altimetry is introduced in this paper and a suite of evaluations are performed. One of the reasons for updating the existing Mean Sea Surface is the fact, that during the last 6 years nearly three times as much data have been made available by the space agencies, resulting in more than 15 years of altimetry from Long Repeat Orbits or Geodetic Missions. This includes the two interleaved long repeat cycles of Jason-2 with a systematic cross-track distance as low as 4 km. A new processing chain with updated filtering and editing has been implemented for DTU21MSS. This way, the DTU21MSS has been computed from 2Hz altimetry in contrast to the former DTU15MSS/DTU18MSS which were computed from 1 Hz altimetry.
The new DTU21MSS is computed over the same 20-year averaging time from 1993. 01. 01 to 2012. 12. 31 with a well-specified central time of 2003. 01. 01 and is available from the following site; (https://doi. org/10. 11583/ DTU. 19383221. v1, Andersen, 2022)   Cryosat-2 employs SAR and SARin modes in a large part of the Arctic Ocean due to the presence of sea ice. For SAR and SARin mode data we applied the SAMOSA+ physical retracking in order to make it compatible with the physical retracker used for conventional Low-Resolution Mode data in other parts of the ocean.
Computation of the DTU21MSS The DTU21MSS is based on satellite altimetry data from frequently repeating Exact Repeat Missions (ERM) and in-frequently missions with long or drifting repeatcalled Geodetic Mission (GM). The MSS is determined from a sophisticated combination of the coarse ERM with the high-density GM data as described in  Andersen and Knudsen (2008). The first step is to select the averaging period and consequently the center time for the MSS. To enable evaluations the agreement within the altimetric community has been to average over 1993. 01. 01 to 2012. 12. 31. Hence the center time for this and previous DTU models will be 2003. 01. 01. Within the 66º parallels the highly accurate mean profiles derived using TOPEX/J1/J2 nearly uninterrupted observations is the back-bone of the MSS models.
The first two advances related to short wavelength improvement where one advance is related to the retracking and filtering method used to enhance the short wavelength of the MSS and the second advance is related to the computation of new 2-Hz altimetric observations. The third and fourth advances described are related to long wavelength corrections and the use of anisotropic filtering to enhance the MSS in current regions but also a new retracked Cryosat-2 dataset to enhance the Polar regions up to the 88 parallel.
For all four recent GM missions (Jason-1, Jason-2, SARAL/AltiKa, and CryoSat-2/LRM) this approach has been proved effective  (Garcia et al. 2014; Zhang and Sandwell 2017). Figure  1  illustrates the gain in range precision using the two-pass retracking. The improvement for all four LRM datasets is dependent on the SWH but is on average of the order of 1. 5 similarly to what has been shown by other authors. (Sandwell et al,, 2014; Zhang et al. , 2019). Whereas two-pass retracking is very efficient for improving the range precision for the LRM data, we did not apply the two-pass retracking for the CryoSat-2 SAR-and SARin-mode data as there is no gain in range precision from the second step of the retracking for SAR and SARin data. This was documented by  Garcia et al. , (2014).
Sentinel-3A/B SAR Altimetry The European Space Agency (ESA) launched Sentinel-3A on the 16 th of February 2016 and Sentinel-3B on 25 th April 2018. These satellites operate as SAR altimeters everywhere with the benefit of increased range precision compared with conventional LRM altimetry. Both the increased along-track resolution and more importantly the improved cross-track resolution of 35 km for the combined Sentinel 3A/B dataset would make these important contributors to the DTU21MSS. However, two problems prevented the use of these data for the time being. The first relates to the fact that mean profiles could only be computed over 5 and 3 years from Sentinel 3A and B, respectively. As the Sentinel-3 satellites operate in a 27-days repeat this resulted in as few as 66 and 40 cycles, making these mean profiles considerably noisier compared with other mean profiles.
From here it descends towards the Aleutian Trench at 57ºN, 204ºE. The standard deviation with the S3A mean profiles are 6. 1 5. 7 and 8. 1 cm respectively for the DTU15MSS, DTU21MSS, and the CLS15MSS. The missing data around latitude 90ºE is due to the crossing of the Russian island Komsomoles. The missing data around 120ºE are due to voids in CLS15MSS causing these data to be removed. The color scale ranges from -15cm to +15 cm. The increase in the S3A residuals around 190°E is associated with the transition of the Bering Strait and the in/out flow through the Strait (Woodgate and Peralta-Ferriz, 2021).
Coastal evaluation The difference between the DTU21MSS and the DTU15MSS was evaluated in the Baltic Sea as part of the BalticSeal+ project (http://balticseal. eu/). Differences are presented in Figure  9  and are ranging up to 8 cm in the coastal zone and the narrow (15 km) Danish Straits as well as the Bay of Botnia and the Swedish archipelago. In all locations we found, that the former DTU15MSS is unreasonably high near the coastline. Similarly, we found that in the Bay of Finland the DTU15MSS was too low. In all cases, we found that this is an artifact of the gridding combined with the lack of 1Hz data used for the older DTU15MSS.
Conclusions A new Mean Sea Surface (MSS) called DTU21MSS for referencing sea level anomalies from satellite altimetry has been presented along with the first evaluations. We have presented the updated processing chain with updated editing and data filtering. The updated processing filters the double retracked 20-Hz sea surface height data using the Parks-McClellan filter to derive 2-Hz sea surface anomaly. This Parks-McClellan filter has a clear advantage over the 1 Hz boxcar filter used for older DTU models in enhancing the MSS in the 10-40 km wavelength band. Similarly, the use of a the FES2014 ocean tide model improves the usage of sun-synchronous satellites in high latitudes in the new MSS. Cryosat-2 employs SAR and SARin modes in large part of the Arctic Ocean due to the presence of sea ice.
For SAR and SARin mode data we applied the SAMOSA+ physical retracking  (Dinardo et al. , 2018)  in order to make it compatible with the physical retracker used for conventional Low-Resolution Mode data in other parts of the global ocean. We initially performed global comparisons with the mean profile from various available satellite using data from the RADS data archive as these have only been used in the DTU15MSS and not any of the other MSS models. The comparison with the independent 5-and 3-year S3A and S3B mean profiles show a relatively clear improvement for the DTU21MSS. This was also expected as the S3A/B satellites employs SAR altimetry and hence should compare better with the MSS derived using the twopass altimetry due to the enhanced modeling of the 10-30 km wavelength  (Garcia et al.