dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion According to the databases explained in the previous Section 2, a demonstration of the obtained results applying segmentation techniques and performance analysis are described in this section. Firstly, a pre-processing example testing the filters and showing the usability of these processing tools are described in this Section 4. 1. Then, different examples testing the proposed segmentation algorithms are described. The purpose is to apply each segmentation technique for each one, and discuss the advantages or disadvantages comparing the obtained results.
Experimental Test After the pre-processing step (if necessary), users can select one of the segmentation algorithms at the same time. For each image, the segmented and masked images are generated and saved in a folder. In addition, all results are displayed in the application as a first preview. Often, there are long data repositories using the same acquisition system and the same protocol settings. If users can find a good procedure to segment a region of interest, it can be an excellent role to obtain many segmentation results for a long dataset. In addition, this configuration can be saved as a project file to be loaded for next time. One of the main advantages of this tool is that users can easily compare all the results of each segmentation technique. It is clear that not all segmentation techniques serve the same purpose.
Performance Analysis The robustness and quality of the proposed segmentation methods have been evaluated. A digital retinal database for vessel extraction has been used for this evaluation  [24]. This database provides true segmentation data that have been compared with our segmentation results. An example of a DSC comparison of the segmented images against a reference ground truth is depicted in Figure  10. This comparison shows the masks on top of each other. The different colours represent true positive pixels (TP, in white), true negative pixels (TN, in black), false positives (FP, in green), and false negatives (FN, in magenta). For this example, Threshold, Normalized, Intensity, and Snake segmentation results indicate a good correlation between the GT and the mask. Otherwise, Canny and Watershed are the worst algorithms for this purpose because one is based on a contour-based, and the other is region-based.
This evaluation performance demonstrates that MedicalSeg is a useful tool to generate ground truth and which allows to compare their effectiveness and select the best algorithm for a given application. Summarizing, Figure  10  represents the differences between the correct solution and our results. On the one hand, the green region indicates the difference areas compared to the ground truth. On the other hand, the purple shows just the opposite, with the two algorithms showing that the results are further away from the ground truth. The purpose is not to compare our application with other medical tools available online, such as Fiji/ImageJ, 3D slicer, Horus/Osirix-OsiriX DICOM Viewer, Matlab apps, ICY, QuPath, or CADs, among others. All of them are excellent applications and allow to perform segmentations, pre-processing tools, filters similar to the ones we propose in this work, but the big difference is that you usually have to do it manually one by one.
Currently, we use MedicalSeg to massively generate large volumes of segmented data with their respective masks. This application automatically generates a folder with all the masks of the block segmentations. In fact, one of the main advantage is the easy access and management of the results. This application represents a first step towards a robust tool to generate automatically and manual management ground truth data using medical images. In this sense, MedicalSeg can be used for artificial intelligence purposes, such as data augmentation. Furthermore, a little validation compared to ImageJ  [44]  using the same ground truth (GT) database depicted in Figure  10  is performed. ImageJ has been chosen because we consider this tool as a reference in medical applications, and also because it contains most of the segmentations techniques that are used in MedicalSeg.
In this sense, Table  1  shows the results obtained against a comparison between the GT compared with the ground truth corresponding to MedicalSeg, and GT vs. ImageJ, respectively. Finally, a last comparison between MedicalSeg vs. ImageJ was also performed. As a main conclusion, the results obtained using ImageJ and MedicalSeg are quite similar compared with the ground truth and demonstrates the quality of the proposed segmentation approaches included in our application. In order to evaluate the performance of MedicalSeg a random case is used for each database. The criteria used to do the performance analysis is based on applying all the segmentation techniques for a group of images. For each set of images, the computation time (in seconds) for each segmentation technique is obtained. Moreover, the same set of images using all the techniques together is also performed.
All the obtained results are depicted in Table  2. In this case, the parameters of each segmentation technique are fixed. In the previous section the results obtained for each experimental dataset applying all the segmentation techniques were discussed. In this section, the purpose is to obtain the elapsed time and the quantity of images generated using different experimental datasets. It is important to remark that in ThreshMAX percentage segmentation technique, the estimation of the elapsed time is performed by evaluating the method whithin a range of percentage options (40% to 80%). In the case of the snake segmentation algorithm the number of iterations was fixed to 30. Finally, Canny edge threshold for manual and automatic options were also computed. Analysing the results, the Canny edge detector and snake algorithms require more time than the others.
It is normal because both algorithms require more steps and parameters that slow down the calculation iterative process. The purpose of this section is to estimate the computational time response according to the parameters presented in this work. Consequently, if users change these parameters the elapsed time will vary according the size of the dataset used for each test and the computing resources used. Finally, the size of the selected images from the dataset were 320 × 240 cm, and the computer used to perform this test was an Intel ® Core™ i7-6700 Processor 8M Cache, up to 4. 00 GHz, 4 Cores (8 Threats) with 16 GB of RAM memory running in a Windows 10 OS.
Conclusions A new application based on different segmentation techniques using medical databases is presented. The management of MedicalSeg is developed in three main parts: (i) image segmentation development, (ii) the design of a GUI interface, and (iii) management and analysis. The image segmentation tool contains a set of nine segmentation techniques, such as threshold and normalization segmentation, Otsu, ThreshMAX percentage segmentation, manual and automatic canny edge threshold segmentation (manual and automatic), intensity threshold segmentation, active contour model (snake segmentation), Watershed and GMM. The GUI interface is described and a study case of its main functionalities is detailed. Moreover, the analysis of the results are included In order to evaluate this application, five medical datasets (mammography, prostate, retinal, ultrasound, and brain vascular) are tested to prove the usability of this tool.
A validation procedure and performance analysis are presented in this work. MedicalSeg tool demonstrates that the use of the proposed segmentation techniques are useful for medical imaging. A comparison among all the algorithms discussing the advantages and disadvantages of each dataset is performed. At this moment, it has been implemented nine segmentation techniques, but the purpose is continue working on to add more processing tools. MedicalSeg is designed as an open-source platform. In this sense, developer users can add new segmentation techniques to this application being an advantage compared with other medical applications. For this reason, users can adapt to their purposes and evolved methods offered by frameworks such as ITK, among others. The inclusion of segmentation techniques based on convolutional neural networks, such as U-net, could be a good improvement to this tool.
Data Description This data provides supporting information to the outlined article "CaV1. 3 enhanced store operated calcium promotes resistance to androgen deprivation in prostate cancer"  [1]. Raw and analysed data in excel format relating to this article is provided at Mendeley data in the same order as outlined in this article  [2]. This includes gene expression CT data for all genes outlined as well as housekeeping. CaV1. 3 protein expression data values as quantified by ImageJ. 340/380 florescence measurements overtime for all calcium traces. In addition to absorbance values of WST-1 at 440nM relating to cell proliferation and colony counts for cell survival assays. A model of disease progression during androgen deprivation therapy (ADT) was employed, representing untreated androgen sensitivity (LNCaP), early androgen resistance (LNCaP-ADT) and castrate resistant PCa (CRPC) (LNCaP-ABL).
In castrate resistant LNCaP-ABL, expression was completely lost. Note, this is preliminary data without a nucleus control so further work is require to confirm this. Androgen insensitive PC3 cell line was used to measure the role of CaV1. 3 on SOC and proliferation using siCACNA1D. PC3 cell lines displayed a significant increase in store release under Tg treatment (P < 0. 0 0 01) followed by a significant increase in SOCE (p < 0. 0 0 01) ( Fig. 8 A ). Following CACNA1D siRNA knockdown store release was decreased but did not reach significance ( Fig. 8 B ), however SOCE did (p < 0. 05, Fig. 8 C ). PC3 proliferation was significantly increased compared to androgen sensitive LNCaP (p < 0. 01, Fig. 8 D ), which was reduced by siCACNA1D (p < 0. 01, Fig. 8 D ) to levels observed with LNCaP. Bioinformatic analysis was conducted in the original paper using the samples from the Taylor et al data set  [8].
measure expression via qPCR and Western blot respectively. Store operated calcium was measured via FURA-2am. Cell proliferation was assessed with WST-1 assay. Data source location Dublin City University, National Institute of Cellular Biotechnology and the School of Nursing and Human Science, Dublin, Ireland. Data accessibility Repository name: Mendeley Data Data identification number: 10. 17632/d9vn7ygf3z. 4 Direct URL to data: https://data. mendeley. com/datasets/d9vn7ygf3z/5 Related research article For an article published in Cell Calcium. D. O'Reilly, T. Downing, S. Kouba, M. Potier-Cartereau, D. J. McKenna, C. Vandier, P. Buchanan. CaV1. 3 enhanced store operated calcium promotes resistance to androgen deprivation in prostate cancer. Cell Calcium. Volume 103, May 2022, 102554. https://doi.
(Dubovik et al. , 2021b). With support from ESA, the 11 years  (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012)  MERIS/GRASP aerosol product (V1. 1) has been released on the GRASP-OPEN website (http://www. gras p-open. com/products/meris-data-release/). There are ongoing efforts that are improving the current MERIS/GRASP product. Thus, at present GRASP can be considered as a unified approach and software platform that is fully or partially portable for processing diverse types of remote sensing observations. This study describes the development of OLCI/Sentinel-3A aerosol and surface characterization based on the GRASP algorithm. The created dataset is available publicly from GRASP-OPEN (http://www. grasp-open. com/products/olci-data -release/) and the project description is on EUMETSAT (https://www.
The OLCI observations in 2 × 2 × NT (2 by 2 pixels for NT time layers) segments data centered over these AERONET sites are adopted in the tests. In order to test over pure ocean surface, we manually moved the selected ocean/coastal sites maximum ±0. 3 • latitude or longitude to use the OLCI measurements over ocean surface. Fig. 2  shows the spatial distribution of selected AERONET sites. The selection of AERONET sites is based on several principles: a. to provide good global spatial coverage with sufficient data amount during 2018 to 2019 b. to provide good representation of various aerosol types c. to provide a coverage of different land cover types for the retrieval tests over land and a sufficient dynamic of typical wind speed for ocean tests To perform matchup of OLCI/GRASP test retrievals with AERONET, the AERONET Version 3 Level 2 direct-sun AOD  (Smirnov et al.
In this regard, in conventional satellite retrievals (i. e. MODIS,  Levy et al. , 2013) , the angular properties of BRDF are commonly assumed using the ancillary knowledge of wind speed using known empirical relationships of the surface wind speed with δ Fr and σ. Thus, the Test 1 is focused on verifying the effect of constraining the angular properties of water surface using this approach. Specifically, the following equations were used for defining δ Fr and σ  (Cox and Munk, 1954; Monahan and O'Muircheartaigh, 1980; Koepke, 1984; Frouin and Pelletier, 2015) : δ Fr = 1. 0 - ( 2. 95 × 10 -6 × w 3. 52 ) (6) 2σ 2 = 0. 003 + 0. 00512 × w (7) where w is the wind speed (unit: m/s) at the boundary layer, which is accessible in OLCI L1B data and is originally from ECMWF reanalysis dataset. Therefore, the 2 experiments were done for constraining 2nd and 3rd parameters of water surface BRDF: (i) The δ Fr and σ 2 were not retrieved but fully fixed using the Eqs.
Nonetheless, the retrieval of δ Fr and σ 2 constrained by a priori estimates seemed to be a more flexible approach. Hence, the use of a priori estimates for δ Fr and σ 2 is adopt as the outcome of Test 1 and will be used in the combination of next tests. o Test 2: Optimizing the use of spectral OLCI data Test 2 was aimed on optimizing the use of the OLCI observations at short wavelengths (412. 5, 442. 5, and 490 nm channels). In fact, the water leaving radiances in this spectral range depend on many factors (e. g. , concentrations of chlorophyll or other soluble matters in the water) and, therefore higher uncertainty in water reflectance in these channels may cause higher uncertainty in AOD retrievals. This is a reason, why the most algorithms developed for mono-and bi-viewing sensors avoid the use of measurements at wavelengths lower than 550 nm over ocean surface (e.
g. MODIS, SeaWiFS, VIIRS, etc. )  (Remer et al. , 2005; Sayer et al. , 2012 Sayer et al. , , 2018)). Therefore, the Test 2 was designed to check if the high bias in OLCI/GRASP AOD could be related with a pronounced use of 412. 5, 442. 5, and 490 nm channels in the retrieval. At the same time, it is also clear that there is significant signal of aerosol at this shortwave spectral range and complete elimination of these channels may also waken the information content in the inverted data set. Thus, the tests were done with decreased weighting for short wavelengths or fully excluded measurements at short wavelengths. Some extra tests were also done by combining the ideas used in Tests 1 and 2, as well as by attempting to increase the constraints limiting variability of aerosol type, i. e. the relative presence of aerosol components in neighboring pixels.
In baseline OLCI/GRASP AOD approach, eight values of a iso (λ) and two spectrally dependent parameters a vol and a geom are retrieved under rather strong constraints of temporal variability of these parameters. At the same time, the single viewing OLCI measurements seem to have a low sensitivity to angular features of BRDF that are described by volumetric and geometric terms in Eq. (  8 ). Moreover, realistic separation of surface and aerosol contributions in satellite observation over land surface is generally a very challenging task. Therefore, a series of tests were proposed to optimize the separation aerosol and surface information in OLCI retrieval over land. In order to conduct the tests using a priori estimate for surface reflectance, a monthly surface climatology was generated for 3 Ross-Li BRDF parameters from POLDER-3 GRASP/Optimized processing climatological dataset (https://www.
grasp-open. com/products/p older-data-release/). To adapt the climatology for OLCI wavelengths, the interpolation and exploration were performed using the nearest POLDER-3 wavelengths. Note the current GRASP/Optimized climatological dataset is available from latitude 60 • S to 60 • N. o Test 4: Constraining angular properties of land surface BRDF Based on rather positive outcome from the ocean tests, we implemented 2 modifications to optimize the OLCI/GRASP baseline land configuration before performing Test 4: (i) we introduced stronger inter-pixel constraints on aerosol type variability in spatial (X, Y) and temporal (T) dimensions (see Table  6 ); (ii) we assumed explicitly the spectrally independent volumetric and geometric BRDF parameters (a vol and a geom ) in the retrieval. Similar to the Test 1 for ocean surface, two experiments were conducted over land in Test 4 to constrain angular properties of land surface BRDF: (i) The parameters a vol and a geom were directly fixed using the POLDER-3/GRASP climatology; (ii) A priori estimates of a vol and a geom were used from the POLDER-3/ GRASP climatology with significantly high corresponding Lagrange multiplier parameters (e.
Summary of OLCI/GRASP land and ocean retrieval tests This section described the efforts on implementing a large time-series of the retrieval tests of AOD from Sentinel-3A/OLCI over land and ocean. We performed >50 tests using different configurations of surface climatology, constraints, etc. As a result, the positive evolution of OLCI/ GRASP AOD retrieval can be clearly seen through the test results from baseline Test 0 to Test 3 over ocean (Table  4 ) and Test 6 over land (Table  5 ). Thus, based on the results of the retrieval tests over land and ocean, the optimized configurations for ocean and land OLCI/GRASP retrievals are determined: • Ocean (Test 3): (i) the use of wind speed data for constraining angular ocean surface BRDF properties (using a priori estimates for δ Fr and σ 2 calculated based on wind speed from ECMWF values); (ii) the assumption of higher measurement noise at short wave channels (412.
The generated OLCI/GRASP results were validated and evaluated in this section following a similar concept as used by  Chen et al. (2020)  for evaluating POLDER-3/GRASP retrieval. Correspondingly, the OLCI/ GRASP validation results were also compared with results of validation of MODIS retrieval performed for the same time period using the same validation approach. In addition, OLCI/GRASP retrieval were compared globally with MODIS products. All comparisons were done separately for observations over land and ocean. In addition, over ocean validation was also done separately for permanent coastal/island AERONET sites and MAN ship-borne measurements obtained mainly over deep ocean. Thus, the section contains the following materials: o The generated OLCI/GRASP global aerosol products validation against the AERONET Version 3 Level 2 direct-sun AOD reference dataset o The validation of MODIS/TERRA Collection 6.
Acknowledgements The authors would like to gratefully acknowledge  EUMETSAT  for continuous support of OLCI/GRASP development within framework of the project:  Risk mitigation study for Aerosol Optical Depth  product retrieval from Sentinel-3 OLCI through the contract  EUM/CO/19/ 4600002258/JCh. We would like to thank the AERONET, AERONET/ MAN networks and MODIS mission and aerosol science team for production the data and making their data available for the community. All PIs  and their staff of AERONET stations and AERONET/MAN for maintaining the instrument are gratefully acknowledged.
CARP3 is a flagellar tip protein essential for social motility and colonization of tsetse fly salivary glands We previously identified the cAMP response protein CARP3 (Tri-TrypDB entry Tb927. 7. 5340) in an RNAi screen for cAMP resistance in bloodstream forms (BSFs) of T. brucei  32. CARP3 is a protein with no sequence homology outside Trypanosoma and unknown biochemical functions, except N-terminal myristoylation site(s) (http://lipid. biocuckoo. org/webserver. php). Its novel predicted structure (AlphaFold2  39  ) is shown in Supplementary Fig. 1a. As cAMP regulates social motility (SoMo) in procyclic forms (PCFs)  8, 40  , we asked whether CARP3 did also control the parasite's colonization of the tsetse fly vector. A homozygous CARP3 deletion mutant, its endogenous single allele CARP3 rescue, as well as a tetracycline-inducible CARP3 RNAi cell line were generated in the fully differentiation-competent pleomorphic T.
brucei AnTat 1. 1E CARP3-YFP of both BSF and PCF (Fig. 5a , b and Supplementary Fig. 8a, b ). Mass spectrometry analysis identified CARP3 and 12 AC protein groups among the 16 protein groups significantly enriched (p ≤ 0. 05, ≥ 10-fold enrichment) in BSFs (Fig. 5a , Supplementary data 1). In PCFs, CARP3 and 5 AC protein groups were among the 15 protein groups showing significant enrichment (Fig. 5b , Supplementary data 1). For independent confirmation, we selected ESAG4 (TriTrypDB entry Tb427. BES40. 13), an abundant, BSFspecific AC, for which an antibody was available  33. ESAG4 was pulled down by CARP3-YFP (Supplementary Fig. 8c ) as well as by native CARP3 (Fig. 5c ). For anti-CARP3-mediated pull-down, full-length ESAG4 was expressed as GFP fusion protein (cell line DNi-3 from Salmon et al.
Protein IDs that differ significantly (two-sided Student's t test, p value ≤0. 05, s 0 = 0. 1) in abundance are located above the significance line with their TriTrypDB entries (https://tritrypdb. org) and gene names (in green) indicated. d Analysis of ACP abundance by in-gel fluorescence (C-terminally mNeonGreen tagged ACP1/4/5) or Western blot (C-terminally Ty1 tagged ACP6) in PCF trypanosomes upon CARP3 RNAi ± Tet. CARP3 (anti-CARP3) and ACP signals were normalized to PRF-A/C and day 0 was set to 1; FRDg as loading control. e Cartoon model of the CARP3-AC complex at the membrane including the transmembrane helix and the intracellular parts of an AC dimer (ESAG4). CARP3 N-myristoylation (N-myr) is indicated. The side chains of several basic amino acids of a membraneproximal helix predicted for CARP3 are shown.
1 SP3, version 14. 0. 4. 201, 64-bit), while the follow up localization and colocalization analysis was performed via in house Fiji version 2. 1. 0 (Fiji is Just Image J  78  ) and R scripts (http://www. R-project. org/) (https:// github. com/GiacomoGiacomelli/Carp3-Co-localization-PALM). R scripts were written in RStudio (version 1. 1. 456) (RStudio_Team 2016) and run on R version 3. 6. 3 (2020-02-29) (R_Core_Team 2020). For colocalization analysis, we used the coordinate-based colocalization (CBC) method of Malkusch, et al. 43. The CBC algorithm calculates a colocalization value for each molecule while taking into account the spatial distribution of the two populations within a user determined radius (R max ). All CBC values were calculated for six different R max (50,  100. 200, 300, 400, 500 nm) and radius intervals of 5 nm (Supplementary Fig.
Beads were washed 4x with lysis buffer, followed by three washes with 50 mM Tris/Cl pH 8. Bound proteins were either eluted by boiling for 10 min with Laemmli sample buffer for SDS PAGE or beads with bound proteins were subjected to on-bead digest for mass spectrometry analysis. After on-bead digestion with trypsin, digested peptides were separated on an Ultimate 3000 RSLCnano (ThermoFisher) with a gradient from 4 to 40% acetonitrile in 0. 1% formic acid over 40 min at 300 nL/ min in a 15-cm analytical (75 µm ID home-packed with ReproSil-Pur C18-AQ 2. 4 μm from Dr. Maisch). The effluent from the HPLC was directly electrosprayed into a Q Exactive HF instrument operated in data dependent mode to automatically switch between full scan MS and MS/MS acquisition. Survey full scan MS spectra (from m/z 250-1600) were acquired with resolution R = 60000 at m/z 400 (AGC target of 3 × 10 6 ).