dataesr/scientific-paragraphs-categorization huggingface dataset:
The threshold value was determined for each TopoChip replica separately and equalled the 93rd percentile of all ICAM-1 median intensities per replica. This adaptive threshold strategy allowed us to take a variation of median intensities between TopoChip replicas into account (Supplementary Figure  5 ). To evaluate the correlation between ICAM-1 expression and surface design parameters, we trained a machine-learning model with 10-fold cross-validation with 75% of the data by employing a random forest classification algorithm. We analyzed design parameters of 72 low-scoring units and 112 high-scoring units that had a significant difference in frequency of ICAM-1-positive cells. The accuracy of the obtained model was assessed on a held-out data set that was not used for model training (25% of the data).
The accuracy of the obtained model was assessed on a held-out data set, which was randomly selected from 1/3 of the original data, and not used for model training. To ensure robustness of the model, we performed model training 100 times with random splitting of the data into training and testing data sets. The average accuracy of the 100 simulations validated on the held-out data set was 86%, which means that from 100 surfaces, the algorithm was able to accurately predict expression of ICAM-1 on 86 of them (Figure  6A ). In addition, we also quantified the importance of surface design parameters for separation of defined classes according to pattern recognition algorithm. The parameters that correlate to ICAM-1 expression based on average importance across all simulations (Figure  6B ) include pattern area, space area, and the WN0.
To investigate whether upregulated ICAM-1 expression in TSCs on selected topographies correlates to co-expression of other FRC markers, we evaluated the expression of some FRCspecific genes using qPCR analysis. On ICAM-1 High surface and ICAM-1 Low surface ICAM-1 expression was significantly different with more than 2 -fold change in comparison with cells on the flat surface (Figure  7C ). Interestingly, ICAM-1 gene expression level on the ICAM High surface was the lowest of the three surfaces despite having the highest ICAM-1 expression at the protein level. None of the other markers of the FRC phenotype that we evaluated, such as VCAM-1, IL7, or CCL5, were differentially expressed in cells cultured on ICAM-1 High or ICAM-1 Low surfaces (data not shown). However, we did observe an increase of LTBR gene expression on all four surfaces containing topography.
ACKNOWLEDGMENTS We thank  Céline Monvoisin  and  Marion Guirriec  ( University of Rennes, Rennes, France ) and  Natalie Fekete (Materiomics)  for technical support. FM and KT received funding from the  Ligue Nationale Contre le Cancer (Equipe labellisée). This research is supported by funding from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no  289720. AV, NB, and JdB acknowledge the financial contribution of the  Province of Limburg.
Background: The University of Maine Stormwater Management and Research Team (SMART) program began in 2014 with the goal of creating a diverse science-technology-engineering-math (STEM) pathway with community water research. The program engages female and underrepresented minority high school students in locally relevant STEM research. It focuses on creating educational experiences that are active and relevant to students that build confidence, connect knowledge and skills directly to solving problems in local communities, and support student cultural identities. The core tools of the SMART program are resources and relationships: universitydesigned or commercial water data collection equipment, data loggers and chemistry supplies, on-campus science and engineering training for teacher-mentors and students, and a community mentor network.
g. , temperature, conductivity, pH) into a common online database. Students entered the program already attracted to STEM, with 70 to 58% of students indicating that they were interested or very interested in the various STEM areas-particularly biology, chemistry, environmental sciences, and engineering-in order of importance. Nevertheless, interest in these disciplines did not align with student knowledge of these disciplines, as reflected by many students indicating they knew nothing or almost nothing regarding many of these fields. The top five stormwaterrelated engineering and science practices, among 16 different project activities, with the greatest increases in participation rate are given in Table  3  for male and female students and in Table  4  for white and URM students. The top activities listed are related to collecting data via wireless sensors and probes and using them in real world applications.
STEM education through community engagement (objective 3) A key component in stimulating interest in the program and consequently a STEM career is the combination of a global problem in a community environment. Collected summative data highlight the importance students place on finding solutions and feeling empowered to address these problems by connecting with their community (Fig. 7 ). Without projects such as SMART, few students have opportunities to work on real world problems in their communities. Prior to participation, nearly half of students (48. 6%) indicated that they had never used data to help solve a local environmental problem, and three fourths (74. 3%) indicated that they had never worked with environmental officials. SMART provides students with many hands-on activities to work on the local environmental issue of stormwater across a full academic year and beyond, if desired.
Local water districts have supported the SMART program by providing technical expertise, presentations, and identifying appropriate watershed locations for water sampling and installation of wireless sensors. Water districts are obligated to perform community outreach and provide water quality data to the Environmental Protection Agency (EPA) on a regular basis. Therefore, the districts gain from the program by engaging in mutually beneficial activities with a local SMART group, and also by having access to a trained workforce for internships or future permanent jobs, even beginning at the high school level. The combination of educating Maine students in STEM while also addressing locally relevant issues provides a powerful incentive for the engagement of community organizations. Organizations that have supported the UMaine SMART program include IDEXX Corporation, Emera Maine (an electric power utility), non-profit Maine Community foundation, and Bangor Savings Bank.
Further, the collaborative is developing an integrated assessment process for both the broadening participation outcomes and the scale-up process, to improve the availability of longitudinal data and allow multi-state longitudinal data collection and evaluation. This assessment method will extend to post-secondary to document the longitudinal effectiveness of the program. The SMART program has also posed challenges that are being addressed in the current program. One of these challenges is the attrition of the SMART students, often attributed to the lack of available after-school time for SMART activities due to sports, other extracurricular activities, or part-time jobs. This could be remedied in some schools by including SMART activities as part of existing courses in the high school curriculum or creating a new course.
On the technical aspects of SMART activities, the greatest challenge was the loss of wireless sensor data acquisition systems, due to vandalism, when left unattended in a watershed over a long period of time over. This was resolved by employing secure locations, controlled by water districts, or collecting intermittent data before or after a rainstorm. Furthermore, in places where WiFi (the communication protocol for our wireless sensor data acquisition system) access was limited, real-time data could not be collected. Finally, lack of teachers' experience with teaching of engineering and hands-on science and mentoring students could be detrimental to the success of the program. This has led the project team to plan for teacher-only professional development in collaborative inquiry through cognitive apprenticeship, as a part of the summer institute.
35 analyzed to estimate a reproducibility, which mainly represents the sample heterogeneity. The reproducibility estimated from the Standard Deviation (SD) of the replicates for the total mass fluxes determination (0. 12%) is generally lower than the accuracy, except for  13 C. In addition, daily satellite ASCAT wind (Fig. 4c-d   2004 ), in phase with the 0. 2°C and 0. 8°C criteria. Finally, in situ pH sw (Fig. S3 ) and calcite saturation state (Ω calcite ) were calculated with the CO2SYS program  (Lewis and Wallace; 1998) , using discrete Dissolved Inorganic Carbon measured using a LiCOR 7000 and 5 potentiometric pH sw measurements (25°C). The dissociation constants proposed by  Lueker et al. (2002)  were used for this purpose. This resulted in a precision of ± 0. 04 units for pH sw in situ and ± 0.
10 The analysis accuracy for the sediment trap samples has been directly indicated in Fig. 2 , and estimated by propagation of the accuracy of each parameter from a logarithmic expansion for the molar ratios (C:P, N:P, N:P, Si:C, Si:N and Si:P) and all the calculated vertical transfer efficiencies as T eff for POC fluxes (cf. Eq. 1). Standard deviation (SD) between samples representing the variability over the total dataset (AMOP1+AMOP2) or a given subset of data (e. g. corresponding to high, intermediate or low T eff ranges) has also been indicated in Tables  1, 2, 3  and S1  The transfer of particles through the Peruvian OMZ is studied using data collected at the two fixed sediment traps, one located in the oxycline/upper core (34 m) and the second in the lower core (149 m). Seasonally, at 34 m, the mass fluxes during summer are about 60% lower than during winter-spring (AMOP2: 986 mg.
15 T eff values present a strong temporal variability and, while the mean transfer efficiency appears to be relatively similar for both datasets (T eff ~45%), the transfer appears to be more than 3 times more variable for AMOP2 than for AMOP1 (Fig. 2 ; Tab. 1). T eff can be higher than 100%, meaning particles accumulation between both traps, potentially attributable to advection of particles or to primary or secondary production between traps. T eff is never higher than 100% during AMOP1 but it is 3 times during AMOP2 (AMOP2-S1, -S3, -S6; T eff = 135, 106 and 149%, respectively). Due to the potential bias affecting these values 20 not linked to the influence of the OMZ, T eff > 100% will be discarded. Except these very high T eff , transfer efficiency varies between 1 and 71% and three ranges of variation can be defined: high, intermediate and low.
g. detritus due to excretion and mortality), which can also induce active vertical transport of particles not directly considered here. Zooplankton production should be pointed out for AMOP1-S1, characterized by an oxygen event (>10 µmol. kg -  , Fig. 4a ), but with an unexpected high T eff (71%) suggesting 15 preferentially OM preservation. However, this sample is different from the others previously mentioned, with a number of swimmers collected 3 times higher than the average number for the AMOP1 dataset, especially in the lower trap and raising the influence of the active transfer between both traps and potentially secondary production. In this study, we assume that a) similar particles are collected in the upper and lower traps for each sampling period; b) all particles collected in the deep trap are, for each sampling period, subject to a comparable time in the OMZ layer.
Note that all 20 the AMOP1 samples in summer and the first six AMOP2 samples (from AMOP2-S1 to -S6) in winter appear to be subject to a comparable trophic level effect as samples are composed of around 60-70% of faecal pellets, spending therefore a homogeneous time in the OMZ. However, for the six last AMOP2 samples (from AMOP2-S7 to -S12) in late winter/early spring, the proportion of faecal pellets and marine snow inverts. For AMOP2 dataset, low T eff (from AMOP2-S7 to -S12) can be explained by the higher proportion of small particles (as marine snow) potentially easily degradable.
30 Qualitatively, the evolution of elemental fluxes at 34 m should be taken into account to investigate whether the composition of a more or less labile OM can affect the transfer efficiency (T eff ) in addition to the OM availability. For both datasets, POC and PON fluxes show a strong linear correlation with the total particles mass fluxes at the upper trap as well as for the lower trap (R² = 0. 98 for both traps Fig. S2a, b ).
3c) and about 5 times higher (20%) of the total mass flux) than the average for the entire dataset (Tab. S5). A difference of phytoplankton community could be at the origin of this distinction. Indeed, at the beginning of the AMOP2 dataset and up to this sample, the MLD is relatively stable and no strong deepening is observed as a consequence of wind intensification. As the surface productivity ismainly due to diatoms, this long period without strong mixing event can 15 induce silica depletion in surface, limiting diatoms growth. This hypothesis is supported by the analysis of the phytoplankton functional types around the mooring location using MODIS data and the algorithm developed by  Hirata et al. (2011). For AMOP2-S4, the prymnesiophyceae becomes the most influent phytoplanktonic group.
T eff15N remains higher than 100% for all main T eff ranges, in agreement with a particulate δ 15 N decrease with depth from the oxycline to the OMZ core in 5 response to remineralisation mechanisms reported previously off Peru  (Libes and Deuser, 1988). Thus, in addition to the particulate δ 15 N alteration due to the microbial activities and the chemical and isotopic PON compositions associated with different metabolic pathways of OM synthesis, the depth and oxygenation of the oxycline should play an important role to explain the differences between low, intermediate and high T eff ranges. Actually, the highest T eff15N (156%) occurs for the intermediate T eff range, whereas T eff15N are lower (107 and 113%) for the low and high T eff ranges, respectively. However one 10 should be cautious with these results, as the variability in δ 15 N on the entire dataset does not allow to strictly discriminate the ranges.
, S2, S3, S4 and S5). The different relative SD 15 present values always higher than the Total Uncertainties (TU = accuracy + reproducibility) for all considered parameters. Data are available at different time resolutions: 15 min (O 2 , density from temperature and salinity); 30 min (fluorescence); 1 day (satellite ASCAT wind); 7 or 11 days for AMOP1 and AMOP2 datasets, respectively (sediment trap fluxes, including the percentage of Polychaetes relatively to all other collected swimmers, noted %Poly). All fluxes (for the total mass of particles, POC, PON, POP, BSi, CaCO 3 ,  13 C and  15 N, as well as %Poly), corresponding to a period of collection of 7 and 11 days for 20 AMOP1 and AMOP2 periods, respectively, have been normalized and expressed per day. Hereafter, we will use 7days as the nominal weekly period.
The b coefficient represents the attenuation of the curve and therefore an index for OM respiration during its sinking. Fitting b on the AMOP dataset reveals temporally-varying b values (between 0. 23. and 2. 97, Tab. 1), with small and large b values, indicating a slow or rapid OM decay, respectively. Even if this transfer layer (between both traps) is relatively shallow and Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-103 Manuscript under review for journal Biogeosciences Discussion started: 28 March 2018 c Author(s) 2018. CC BY 4. 0 License. covers a short distance (115 m) compared to other studies (e. g. , between 200 and 1,000 m, Miquel et al. , 2011; Heimburger et al. , 2013), b values are in the same range than previous estimates. The largest b values are estimated from September 2 (AMOP2-S7) to November 6 (AMOP2-S12) with a maximum of 2.
Acknowledgements We would like to thank the crew of the  German R/V Meteor  for the deployment as part of the DFG-funded SFB754 fieldwork, the crew of the  Peruvian R/V Olaya  for the visit and the crew of the  French R/V Atalante  for the recovery of the fixed AMOP mooring. We would like to thank  Stefan Sommer  and  Marcus Dengler  for providing the METEOR wind dataset. We would also like to thank  Miriam Soto ,  Anne Royer  and  Emmanuel De Saint Léger  for general logistics and administrative support. We are.
35 The b coefficient represents the attenuation of the curve and therefore an index for OM respiration during its sinking. Fitting b on the AMOP dataset reveals temporally-varying b values (between 0. 23. and 2. 97, Tab. 1), with small and large b values, indicating a slow or rapid OM decay, respectively. Even if this transfer layer (between both traps) is relatively shallow and Biogeosciences Discuss. , https://doi. org/10. 5194/bg-2018-103 Manuscript under review for journal Biogeosciences Discussion started: 28 March 2018 c Author(s) 2018. CC BY 4. 0 License. covers a short distance (115 m) compared to other studies (e. g. , between 200 and 1,000 m, Miquel et al. , 2011; Heimburger et al. , 2013), b values are in the same range than previous estimates. The largest b values are estimated from September 2 (AMOP2-S7) to November 6 (AMOP2-S12) with a maximum of 2.
Setting and Sample A non-experimental, cross-sectional design was used. This research was conducted in Tabuk City in Saudi Arabia. Tabuk is one of the 13 major regions in Saudi Arabia located in the north western area of the Kingdom with a total population of around 571,000  [17]. Data were collected at the big major malls to ensure the representation of the population. A sample of 432 participants was collected. The sample size was estimated by using the Slovin's formula based on a power of. 80, level of significant. 05, population size (n) 571,000 and a medium effect size. The required sample size based on that was at least 400 participants. In the study, 432 participants were included to overcome any attrition during data collection process  [18]. Inclusion criteria were all people who live in Tabuk City and with an age ≥18 years old.
Methods A self-administered structured questionnaire was used in this study. Validity and reliability were tested in a previous study based on a sample size of 120. The questionnaire was made from multiple-choice questions that needed about 10 min to finish. The questionnaire included demographic variables like age, gender, nationality, marital status, educational level, occupation and income. Anthropometric measures including blood pressure, Random Blood Sugar (RBS), height, weight and BMI, were also recorded. Data were collected by the authors and trained nursing and medical students in their internship in October 2016. The participants were voluntarily involved in this study after explaining the objectives, significance, confidential use of data, and utilization of the outcomes. The respondents were advised about their right to either agree to participate, withdraw, or decline without any risk or benefits.
Data Analysis The Statistical Package for the Social Sciences (SPSS) (version 23) was used to analyse the results. Descriptive statistics were used to analyse demographic data, whereas inferential statistics (t-test and Spearman's rho) were utilized to assess the relationships between demographics and the dependent variables. The Pearson product-moment correlation coefficient (Pearson's correlation, for short) was used to measure the strength and direction of relationship between BMI, HTN and DM.
The standard sample contained an aqueous solution of albumin at 0. 25 g/L and the working reagent. The control sample containing distilled water and the working reagent was prepared by mixing a solution of bromophenol blue at 1. 2 g/L and a buffered solution (pH 3. 0) containing 320 mmol/L citric acid and 160 mmol/L sodium phosphate at the ratio of 2:23. The contents of each sample were mixed and incubated for 10 min. The optical densities of the test and standard samples were determined at a wavelength of 620 nm and related to the control sample. The statistical analysis of the obtained data was performed using the Statistica 10. 0 (StatSoft) and package R (version 3. 2. 3) software. The graphs representing the experimental results were developed using the ggplot2 (version 2. 0. 0) package.
8 (Pixmeo, Bernex, Switzerland) and registered for segmentation in the same orientation. Color mapping to reveal cortical and trabecular thickness was obtained from µCT data and measured in millimeters, at the proximal femur. Flow cytometry. Axin2 CreERT2/+ ;R26 mTmG/+ mice received a daily IP injection of 100 µL tamoxifen suspended in corn oil/10% ethanol for 5 days. The control group received corn oil injections without tamoxifen. On post-injection day 7, mice (N = 5) were sacrificed, tibiae and femurs were collected, and marrow was collected by gentle scraping and/or flushing (using 2 mM EDTA in 2% FBS). Cells were filtered through a 40-μm nylon mesh, pelleted by centrifugation (200 g at 4 °C), washed twice, re-suspended in flow cytometry staining buffer (2% FBS in PBS) and kept on ice for subsequent flow cytometry analyses.
Aliquots of the mineralized fraction were gently crushed by mortar and pestle then digested in 0. 25% collagenase (StemCell Technologies, Canada) for 45 min at 37 °C under constant agitation, then filtered through a 40-μm nylon mesh, pelleted by centrifugation (200 g at 4 °C), washed twice, re-suspended in flow cytometry staining buffer, and kept on ice until subjected to flow cytometry. Flow cytometry experiments were performed in triplicate, using a FACS AriaII (BD Biosciences); data were analyzed using FlowJo Software (Tree Star, Inc, Eugene, OR). Histology. Aniline blue staining was performed. Slides were de-paraffinized in Citrosolv then hydrated through a graded ethanol series to distilled water. Slides were then stained in a saturated solution of picric acid followed by a 5% Phosphotungstic acid solution and staining in 1% Aniline blue.
Histomorphometric analyses. Histomorphometry was performed in Image J software (1. 50 b). At least 4 slides from each sample were used for quantification. For apoptosis detection, the number of terminal deoxynucleotidyl transferase dUTP nick end labeling TUNEL-positive (TUNEL +ve ) cells were counted, and expressed as a percent of the total DAPI +ve viable cells. Aniline blue staining was used for quantification of new bone formation. The area of new bone was used as numerator and the total graft area was used as denominator.
Methods A pilot study was performed in the South of France PACA region, where data from the six local SAMU structures are centralized into an interconnected database. A minimal set of variables required for health monitoring (administrative and medical items) and modalities for data extraction and transmission to Santé publique France were defined. SAMU data were transmitted daily to Santé Publique France and the PACA regional team developed a Microsoft Access® application to import decrypted data, request database and analyze indicators. Retrospective part of the study was performed over a 2-year period (2013-2014) and the prospective part during 2015 was based on daily data collection. Completeness and quality of variables were analyzed. SAMU indicators including several level of specificity were built and compared to existing SurSaUD® indicators in different situations (for detection, seasonal follow-up and health impact assessment) using Spearman coefficient correlation.