dataesr/scientific-paragraphs-categorization huggingface dataset:
For microtubule pattern and synapses analyses, optical sections were acquired at 0. 2 mm intervals and images were treated by deconvolution with the Huygens Pro Software (Scientific Volume Imaging). Microtubule pattern analysis was performed on projection of 4 confocal sections at the cell-coverslip contact as described below. T cell morphology in synapses was analyzed as described below. Centrosome localization was estimated by measuring the distance between the anti-pericentrin stained puncta and the CTL plasma membrane at the center area of the immunological synapse.
5) Compute intermediate elliptical Fourier descriptors between the outer edge of the convex hull and its center of mass. 6) Obtain radial sections in between these intermediate contours. 7) Compute the sum of absolute dot products between the microtubule vector field and both the radial and circular vector fields. 8) Estimate the degree of radiality (DoR) by dividing the former result by the latter. The DoR was plotted versus the relative distance to the cell center of mass. Code available at https:// github. com/krentzd/fap_apc_image_analysis.
0 indicates a shape closer to a perfect circle. While the metrics are not completely unrelated, roundness is less sensitive to local irregularities being based on the area  (27)  and thus can be seen as generally providing information on the cell larger scale deviation from a round phenotype. Roughness, on the other hand, better captures variations in small scale irregularities of the cell surface. All analyses were conducted using Python 3. 10. 8 using the regionprops method from scikit-image version 1. 23. 5. The Crofton perimeter was used, which approximates the perimeter based on the Crofton method in four directions  (28). The MA is defined as the length of the major axis of an ellipse with the same normalized second central moment as the mask.
Statistics Statistical analyses were carried out as follows: Whole blood immunophenotyping data were analyzed using the Wilcoxon rank test using Prism software (GraphPad). In graphs, each point represents one subject. Pairs of FAP and healthy subjects matched in sex and age are linked by a line. Whole blood gene expression by Nanostring and secretome by Luminex were analyzed by the T-test using Qlucore software. Due to the small sample size of this rare disease cohort, we approached our analysis in a discovery-based way and did not apply FDR correction to our p values for this analysis. Migration assays were analyzed by paired T-test, using Prism software (GraphPad). Cell proliferation, differentiation and cytokine production data on purified T cells were analyzed by the Wilcoxon rank test using Prism software (GraphPad).
Killing and degranulation, and actin cytoskeleton reorganization were analyzed by two-way ANOVA test, using Prism software (GraphPad). Microtubule pattern and synapse morphology were analyzed by T-test for independent samples using Python software (version 3. 10. 8), SciPy (version 1. 10. 0) package (ttest_ind method https://docs. scipy. org/doc/scipy/reference/generated/ scipy. stats. ttest_ind. html).
B A FIGURE 4 Whole blood immune cell gene expression. Whole blood from pairs of sex and age matched FAP and healthy subjects (one pair per experiment) was added to TruCulture tubes with no stimulus, Candida albicans, SEB, or anti-CD3+anti-CD28 stimuli and incubated during 22 hours at 37°C. Cells and supernatants were then separated. Cells were lysed and mRNA was frozen. The 12 pairs of FAP and healthy subjects were then analyzed together by NanoString technology using a panel of 579 immune related genes listed in Supplementary Table  2. Cells were non-stimulated (A) or received Candida albicans stimulation (B). Data were analyzed using Qlucore software. Only pairs displaying significant differences (p<0. 05) are represented. Black round dots correspond to healthy subjects and red square dots correspond to FAP subjects.
A second set of assays was performed on PBMC or purified T cells and focused on T cell features in which we had previously observed functional effects of APC silencing (15-17). To compare APC protein expression to potential effects in immune cell responses, we carried out biochemical analysis of B A FIGURE 5 Whole blood immune cell gene expression. Samples were processed as for Figure  4. (A) SEB stimulation. (B) Anti-CD3+anti-CD28 stimulation. Data were analyzed using Qlucore software. Only pairs displaying significant differences (p<0. 05) are represented. Black round dots correspond to healthy subjects and red square dots correspond to FAP subjects. Whiskers represent minimum and maximum values, whereas boxes represent second and third quartiles framing the median. Each dot represents one subject.
n = number of pairs. The p values (t-test) are represented as follows: ***p < 0. 001, **p < 0. 01, *p < 0. 05. Data of non-responders to anti-CD3 + anti-CD28 were not included in the analysis. APC protein expressed by FAP subjects. This was challenging because of the low levels of APC protein expression in PBMC and the poor performance of available APC Abs. We therefore generated EBV transformed B cell lines allowing us to have larger amounts of cell protein extracts, and we developed a high affinity VHH Ab, allowing us to carry out immunoprecipitation to enrich APC protein from cell lysates. In this way, we could analyze APC protein expression in 10 out of 12 FAP subjects. All of them expressed a full-length protein, consistent with heterozygosity of APC mutation in blood lymphocytes. Moreover, four of them, expressed truncated forms of different molecular mass.
(A) Candida albicans stimulation. (B) SEB stimulation. (C) Anti-CD3+anti-CD28 stimulation. Data were analyzed using Qlucore software. Only pairs displaying significant differences (p<0. 05) are represented. No significant differences were observed in non-stimulated cells that secreted very low levels of these proteins. Black round dots correspond to healthy subjects and red square dots correspond to FAP subjects. Whiskers represent minimum and maximum values, whereas boxes represent second and third quartiles framing the median. Each dot represents one subject. n = number of pairs. The p values (t-test) are represented as follows: ***p < 0. 001, **p < 0. 01, *p < 0. 05. Data from non-responders to anti-CD3+anti-CD28 were not included in the analysis. most immune cell populations in FAP and healthy subjects with no major differences in the phenotype of peripheral blood immune cell populations.
Acknowledgments The authors are thankful to FAP and healthy participants for their generous engagement in the project, to  L. Arowas  for collecting clinical data from  FAP  and healthy subjects, to the ICAReB team for technical assistance with sample processing, to  Drs M. Dhooge ,  H. Hamzaoui ,  Y. Parc ,  J. C. Saurin ,  I. Sobhani  for medical advice,  Dr C. Zimmer  for scientific support and to the  Photonic Bioimaging UTechS  for technical microscopy support. Special thanks to the members of  Association Polyposes Familiales France  for their engagement all along the project.
Study population A single-centre, prospective descriptive study was conducted at a general hospital centre in France. This hospital is the main hospital for two French counties. During the first wave, 324 patients were admitted with COVID-19. Of these, 66 patients died and 42 were admitted to an intensive care unit (ICU). Subjects were eligible for inclusion if they were hospitalized in ICU or non-ICU units for COVID-19 during the first wave of the pandemic (between March 12 and April 15, 2020) and received at least 2 L/min oxygen. Patients with a previous history of PTSD were eligible for inclusion. Minors and those unable to provide consent were excluded. To identify the study subjects, we matched data provided by the medical information department, the ICU and the hospital pharmacy. Potential subjects were informed of the study, through a letter, during a routine follow-up appointment, approximately 6 months post-discharge.
Data collection and analysis We calculated the number of patients who had to be included according to the SARS-CoV1 incidence of PTSD about 50%. With a risk α at 0. 05 and β at 0. 2 (statistical power at 0. 8), the number of patients required was 64 (calculated using the library "pwr" of the R software, version 4. 2. 1). The following demographic and treatment data, recorded during hospitalization for COVID-19, were extracted from the subject's hospital records: age, sex (male/female), medical, trauma or psychological history; COVID-19 severity according to the WHO severity definitions  [22] ; corticosteroid therapy; and treatment settings (standard or intensive care hospitalization). The variables are presented in mean ± standard deviation. The normality was tested with the Shapiro-Wilk test.
We reported a low rate of PTSD (n=2, 5%) with no difference between the ICU and non-ICU groups. The mean PCL-5 score was low, but most patients presented mild to moderate symptoms as defined by the PCL-5 assessment tool. We also recorded self-reports of other psychological disorders (n=5) that lead to psychological care. Psychological symptoms such as anxiety, depression or PTSD are common in survivors of infectious disease epidemics. Earlier studies examining the prevalence of PTSD during follow-up of COVID-19 patients reported values ranging from 6. 5% to 31% at 2, 4, 8 and 12 weeks  [23] [24] [25] [26] [27] [28]. Data are summarized in Table  2. At 6 months, PTSD screening was very low in our cohort. To explain this low PTSD rate, we formulated several hypotheses. Firstly, as supposed by McNally et al, "the majority of people exposed to trauma will experience stress reactions that remit within 3 months of the traumatic event"  [29].
We involve covariates varying in space in the model, such as sources of crowd (industry, market, and worship place) and population density to link their spatial information with the COVID-19 transmission risk, and to study the spatial and temporal trends. In particular, the mark takes a role of temporal effect measured according to the different phases of COVID-19 in Surabaya-Sidoarjo, so the model enables investigating how the effect of such covariates evolves in different (temporal) phases. The resulting information is expected to help the government to detect areas that may need more tailored intervention for surveillance and vaccination program, as well as provide a brief evaluation of the large-scale social distancing (PSBB). The reminder of the paper is organized as follows. The dataset motivating this paper is presented in Sect.
Dataset The detailed locations (longitude and latitude) of the home addresses of all 8532 confirmed cases in the Indonesian regions of Surabaya and Sidoarjo during the period 17 March to 9 July 2020 are provided by the East Java COVID-19 Response Acceleration Task Force. To study the COVID-19 transmission in different phases, the cases are divided into six periods according to PSBB (large-scale social distancing or lockdown) regulated by the government (Covid 19 2021) concerning the enforcement of PSBB (see Table  1 ). Figure  1  presents the distribution of overall COVID-19 cases, while Fig. 5a  depicts its distribution per temporal period. In general, most confirmed cases are located in the northern part of Surabaya-Sidoarjo. In particular, they are centered in the northeastern part of the region where the Surabaya city center is located.
The cases then spread to the western and southern parts with a more dispersed pattern with respect to different phases. The number of new cases per period shows an increasing trend over time, except for PSBB 3 (see row 4 of Fig. 5a  and Table  1 ). We observe rare cases in the southeastern part of the region as it is a conservation region (mangrove forest) with a few number of inhabitants. The spatial covariates represent the sources of crowds density (enterprises, markets, places of worship) and population density. To obtain the density of sources of crowds, we first collect all of their coordinate locations. In more detail, the address of enterprises in Surabaya and Sidoarjo are obtained from the website of the Ministry of Industry, Republic of Indonesia (https://kemenperin. go. id), and they are converted into latitude and longitude coordinates with the help of Geocode by Awesome Table  feature   and places of worship are collected using Geopy Phyton library with the Nominatim Geocoding service (https:// pypi.
org/project/geopy), from keyword search results on the OpenStreetMap website. The keywords are:''pasar''(market),''mall''(shopping mall) for the markets, and''masjid''(mosque),''gereja''(church) for the places of worship. Data cleaning is conducted to ensure that all sources of crowds are located within the Surabaya and Sidoarjo region (within coordinates ½112:45938; 112:87361 Â ½À7:574321; À7:193056). When we obtain all the three coordinates of the crowd data (enterprise, market, and place of worship), we transform them into density maps by kernel intensity estimation  (Baddeley et al. 2015). Population density for each subdistrict is obtained from the Central Bureau of Statistics Indonesia in 1000 population per km 2 and transformed into population density maps by Smooth:im of the spatstat R package.
Parameter estimation and model assessment To estimate the parameter b, we maximize the corresponding log-likelihood function  (Baddeley et   4 ) is equivalent to the weighted likelihood function of Poisson generalized linear models (GLMs), and it is efficiently implemented in e. g. ppm function of the spatstat package  (Baddeley et al. 2015). We denote the resulting estimator obtained from (4) by b (and denote the estimator of a by â). To handle the multicollinearity problems (See Table  2  and Sect. 2), we perform model selection based on the Bayesian Information Criterion  (Choiruddin et al. 2021 ). To perform model validation, we consider simulation envelopes. Envelopes define the critical bounds of the statistical test of a summary function, such as K-function or cross-K-function, which validates suitability point pattern data to point process model  (Baddeley et al.
Acknowledgements The study is supported by  Institut Teknologi Sepuluh Nopember  under Grant No  967/PKS/ITS/2022. A. Choiruddin  and  J. Mateu  thank the  Indonesian Ministry of Education, Culture, Research, and Technology  for the support of visiting professor through the  World Class Professor 2022 program. We thank the two reviewers and the associate editor for their thoughtful and constructive comments. Funding The study is supported by  Institut Teknologi Sepuluh Nopember  under Grant No  967/PKS/ITS/2022.
Data and Models We explore our hypothesis using a combination of in situ and satellite-derived observations, and ocean and ice sheet modelling. We take advantage of several existing GNSS records from Ross Ice Shelf (RIS) collected during various field campaigns (Sec. 2. 1), focusing on the ones that are sufficiently long to identify intra-annual velocity variations. We combine these records with estimates of intra-annual variations in SSH fields for the open ocean in front of the ice shelves from an existing satellite altimetry data set and from ocean models that include ice shelves (Sec. 2. 2). We then compare the GNSS records to an ice flow model forced with the varying SSH from the ocean models (Sec. 2. 3).
Model summary, and initialisation We used the open-source ice sheet and ice flow model Elmer/Ice  (Gagliardini et al. , 2013) , the glaciological extension of the Elmer finite element software developed at the Center for Science in Finland (CSC-IT). The modelling framework is similar to that described by  Klein et al. (2020). We added variability of SSH in both time and space, relative to the initial static sea level, focusing on SSH output from the and ice streams where vertical shear stresses are negligible relative to other stresses acting on the ice. In addition, we used a linear Weertman friction law  (Weertman, 1973)  and a non-linear constitutive relationship between strain rates and deviatoric stresses, classically used in ice flow modelling and known as Glen's flow law  (Glen, 1958).
Acknowledgement. This research uses the data services provided by the  UNAVCO Facility  with support from the  National Science Foundation  (NSF) and  National Aeronautics and Space Administration (NASA)  under  NSF  Cooperative Agreement  EAR-0735156  ( GZ19 ) and  EAR-1724794  (BATG). CM, LP and HF were supported by  NASA  grants  80NSSC20K0977 ,  NNX17AG63G , and  NNX17AI03G  and by  NSF  grants  1443677  and  1443498. LP was also supported by  NSF  grant  1744789. PB was supported by  NSF  grants  PLR-1246151  and  OPP-1744856. GNSS data for GZ19 can be accessed at the UNAVCO data center (https://www. unavco. org/data/doi/doi: 10. 7283/T53R0RPD ). The IBE data were generated using Copernicus Climate Change Service Information [2020]. The modelling in this work used the  Extreme Science and Engineering Discovery Environment (XSEDE) , which is supported by  NSF  grant no.
Code and data availability. Elmer/Ice code is publicly available through GitHub (https://github. com/ElmerCSC/elmerfem; Gagliardini and others, 2013). All the simulations were performed with version 8. 3 (Rev: b213b0c8) of Elmer/Ice. All Python 3 scripts used for simulations and post-treatment as well as model output are available upon request from authors. The data used are listed in the references.
Model fits were assessed using residual diagnostics using the 'appraise' function from the 'gratia' R package  [61]. To test for a significant effect of infestation on shell temperature or wet weight, parametric F-tests were computed (p < 0. 05) using the 'anova. gam' function from the 'mgcv' R package  [62]. Wald'slike tests were used to assess whether the non-linear relationship between infestation and shell temperature over time (i. e. , heating rates) differed between infested vs. non-infested infauna and whether the non-linear relationship between infestation and wet weight of invertebrates over time (i. e. , desiccation rates) differed between infaunal species  [63].
Online application The "Jupiter Probability Tool" application has been designed following the requirements presented in Section 2. The tools cited in Section 3 are already implementing part of project requirements. Hence, our application's graphical user interface implements some interface features found in existing tools (e. g. , the location of Jupiter on the Phase-CML map, similarly to the Radio Jupiter Pro tool, as shown in Figure  2 ). However, since the previous tools are not open source, none of their code have been reused. 4 Jupiter Radio Map: http://jupiter. kochi-ct. jp/jrm/ The application is using the SPICE kernel system  (Acton et al. , 2018)  for computing observational and planetary ephemerides. It also displays contextual data, when available, such as observational data (e.
g. , from the NDA database) and pre-computed modeled data from the ExPRES modeling tool. The application has been developed as a joint project between the MASER (Measuring, Analysing and Modeling of Emissions in the Radio range) service  (Cecconi et al. , 2020) , and the NDA team  (Lamy et al. , 2017) , with support of PADC (Paris Astronomical Data Centre).
Development details The application is developed in python, using the flask 5  web development library  (Grinberg, 2018). The Solar System bodies and spacecraft ephemerides are retrieved using the python-webgeocalc 6  library, accessing a dedicated WebGeoCalc server  (Acton et al. , 2018)  installed at the Observatoire de Paris. That server is configured to serve locally SPICE kernels for NASA, ESA and JAXA space mission. The NDA Jupiter data are retrieved using the das2  (Piker, 2017)  protocol, connecting to a das2 server implemented and maintained by the Nançay Data Centre (CDN), at the Nançay Radio Observatory (ORN). The ExPRES simulation runs are retrieved from a local FIGURE 3 Screenshot of the online application. The numbers are referring to the numbered items in Table  1. server as CDF 7 files, and are accessed using the spacepy.
Acknowledgments The authors acknowledge support from  Observatoire de Paris ,  CNES (Centre National d'Etudes Spatiale)  and  CNRS (Centre National de la Recherche Scientifique)  through two of its funding programmes: PNP ( Programme National de Planétologie)  and  PNST (Programme National des relations Soleil-Terre ). Support from  Paris Astronomical Data Centre (PADC)  is acknowledged.
Data availability statement The code of the application is currently not open (except the lines presented in the paper). The data used in this application are all openly available: ExPRES collection  (Louis et al. , 2020) , NDA Jupiter Routine collection  (Lamy et al. , 2021) , Phase-CML maps (Lamy et al. , 2022), and the Juno SPICE kernel dataset  (Semenov et al. , 2017).
Stream Gauging and Catchment Delineation We installed a stream gauging station at the outlet culvert. The gauging station consisted of two perforated PVC tubes fixed to the upstream and downstream headwalls of the culvert and armed with pressure transducers (Solinst Leveloggers®). Barometric compensation was performed using barometric pressure data from Solinst Barologgers® deployed nearby. Streamflow at the culvert was directly measured with a digital current meter at the time of installation and the observed flow was used to calibrate a value of Manning's n. The continuous record of water level slope and water depth, along with the culvert geometry and Manning's n coefficient were used to calculate flow according to Manning's equation  (Dingman, 2002). To determine the drainage area of the canal, we used lidar data obtained from the Brunei Survey Department and analyzed in GRASS GIS.
We first filtered the lidar point cloud data to last-return points (using libLAS version 1. 8. 1, http://liblas. org). We then removed outliers among last-return points using two methods: (a) within each cell of a Cartesian grid, we removed outliers more than three times the interquartile range below the lower quartile (Tukey's fence) and recorded the value and location of the lowest among the remaining points; and (b) we removed points with large deviations from a bicubic spline surface through the points (using v. outlier, https://grass. osgeo. org). The remaining local minima were used to construct a gridded digital terrain model (DTM) for each of these sites by inverse-distance weighted interpolation using a geospatial translator library SOMERS ET AL.
Model Fitting We estimated six parameters by fitting the four governing equations (Equations 12-15) to the observed canal data for concentration and 𝐴𝐴 𝐴𝐴 13 C of CH 4 and CO 2 using the Matlab multi-parameter non-linear regression function. By fitting all the different data types simultaneously, we achieve better estimates of parameters than we would if we fit only one data type. In particular, the isotope ratio measurements provide additional constraints beyond just concentrations while only introducing one additional parameter, the fractionation factor. The six parameters are: the shape parameter for lateral groundwater velocities, D, the isotopic fractionation factor, 𝐴𝐴 𝐴𝐴 , and coefficients used to calculate the rates of methane oxidation (k mic ), degassing (s and V 0 ), and DOC oxidation (k DOC ).
All the fitting parameters were assumed to be the same for the January and August datasets except D (how quickly groundwater flow diminishes with depth) and k DOC (rate coefficient for DOC oxidation). D may vary because the contribution of deep groundwater may change with hydrologic conditions. k DOC is allowed to vary because we expect that DOC entering the canal with porewater will change depending on hydrologic conditions, and oxidation rates to vary with flow conditions and solar radiation (photo-oxidation) in ways that are still poorly defined in the literature and our model is not equipped to calculate. From the best-fit parameters, we calculate the amounts of CO 2 and CH 4 that were advected into the canal, oxidized, degassed or remain dissolved in the canal water at the outflow.