dataesr/scientific-paragraphs-categorization huggingface dataset:
| Fertilized egg to postyolk absorption Egg fertilization took place 10-14 hours after collection. Each wild female's eggs were subdivided into 2-7 lots; each lot was mixed with sperm from a different wild male of the same population. The 2010 crosses experienced 100% mortality due to an equipment malfunction during winter; however, 2010 data for wild female body size, fecundity, egg diameter and gonadosomatic index (GSI) were included in population comparisons to increase statistical power. Crossing yielded 389 and 265 families (half-and full-sibs) in 2011 and 2014, respectively, or 43. 1/33. 1 families per population per year (Supporting Information Table  S1 ). Mean family size was 20. 0 eggs ± 8. 0 SD as Cape Race females are small with low fecundity relative to other salmonids and brook trout populations (e.
| Body size to age 12 months, 2011 crosses Fork length (mm) was measured at 0, 3, 7 and 12 months and mass (±0. 1 g) at 3, 7 and 12 months. A random sampling of a range of averages of 48 to 93 fish per tank was measured across time periods. Length/mass data were analysed using linear mixedeffects models with the nlme package (Pinheiro, Bates, DebRoy, Sarkar, & RC Team-R software, 2017) in R (R Core Team 2016). Population and time period were included as, respectively, a categorical fixed effect and a continuous fixed covariate. Inclusion of a population × time period interaction term permitted calculation of population-level growth rates; tank and tank × time period were also included as random effects. Mass was ln-transformed; the slope of mass over time represented specific growth rate. Accounting for time-associated heteroscedasticity and modelling separate residual variances for each population and time period provided significantly improved model fit (likelihood ratio tests (LRTs); χ 2 = 4920.
| Female reproductive investment, 2011 crosses We quantified fecundity, egg diameter and GSI for mature captiveborn females in November 2013. Females were anesthetized prior to removing eggs; digital photographs were taken of eggs in Petri dishes with known size standards. Fecundity (total number of eggs) per female was counted using ImageJ software  (v. 1. 48v; Rasband, 2014). Mean egg diameter of 15 randomly selected eggs per female was measured as a proxy for egg size. GSI was calculated as the ratio of egg mass to body mass. The mean number of captiveborn females per population processed was 44 (total = 354 across populations, 71. 4% of all females). This suite of traits associated with female reproductive investment measured on captive-born females was also analysed on wild females; the analysis of these traits is described below.
| Population trait differentiation between wild and captive environments As a measure of among-population plasticity in wild and captive environments, we compared female (and male) size-at-maturity, fecundity, egg size and GSI between captive-born females in 2013 and wild females used to derive crosses in 2010, 2011 and 2014 (mean number of wild females/males per population = 38/52). Wild trait data were collected using the same procedures described as captive-born females. Analyses of variance (ANOVAs) were employed to quantify the effect of population, environment (captive or wild) and the population × environment interaction for each trait except for male and female body mass (analysed using generalized linear models); between-population comparison p-values for each trait were independently adjusted to control for type-I error using a Bonferroni correction.
Type 2 Diabetes and Associated Glycemic Traits Loci To understand to what extent genetic determination of DR might reflect enrichment for type 2 diabetes or glycemic   control genes, we computed a correlation between case status in the any DR analysis and the sum of the b*risk allele (for quantitative glycemic traits) or logOR*risk allele (for type 2 diabetes) of the trait-associated SNPs for each cohort and each trait (see Supplementary Data for details).
DAPPLE Results: Protein-Protein Interactions One protein network from the African American PDR analysis was significant (P = 0. 0009) for average binding degree within the network (Fig. 2 ). The aforementioned top-ranked SNP (rs115523882) could not be included in the DAPPLE analysis because its nearby gene (GOLIM4) is not in the protein database. The significant protein network includes genes with primary roles in inflammation including IFNG, IL22RA1, CFH, and SELL. IFNG encodes interferon-g, which is highly expressed in ocular tissues from patients with PDR  (43). IL22RA1 encodes the IL-22 receptor, and CFH encodes complement factor H; both proteins are suspected to play a role in PDR  (44, 45). SELL encodes L-selectin, which is expressed at higher levels in lymphocytes from patients with DR and associated with increased endothelial adhesion  (46).
Multiple pathways including inflammatory pathways are implicated by this network. To confirm biological significance, these results will need to be followed up with functional in vitro studies. The DAPPLE results were corroborated by the MAGENTA GSEA in the African American PDR and extremes of DR analyses. This network of genes, however, was not enriched for in Europeans. This could either be due to technical differences (e. g. , the number of African American cases is approximately threefold larger than the number of European cases) or due to biological reasons. For example, we found that the allele frequencies of the most significant variant per gene for 40% of these protein-interacting genes are rare in Europeans (MAF ,0. 2%), whereas they are common in African Americans (MAF. 1%), according to the Genome Aggregation Database (see Web Resources in the Supplemental Data).
Acknowledgments. The authors thank the staff at the  Icelandic Heart Association  and the  Age, Gene/Environment Susceptibility -Reykjavik Study (AGES ) participants who volunteered time and allowed the authors to contribute data to this international project; the staff and participants of the  Atherosclerosis Risk in Communities (ARIC)  study for important contributions; and all of the patients recruited in the  Genetics of Diabetes and Audit Research Tayside Study (GoDARTS)  and other European and African American cohorts. The authors especially thank the  Health Informatics Centre in the School of Medicine, University of Dundee , for help with data access; the staff and participants of the  Jackson Heart Study (JHS) ; and the other investigators, staff, and participants of the  Multiethnic Study of Atherosclerosis (MESA) study for valuable contributions  (a full list of participating MESA investigators and institutions can be found at https://www.
Subjects included in the present analysis consented to the use of their genetic information. This work was supported by  Tenovus Scotland  ( 2015 T15/40  to W. M. ). GoDARTS was supported by  Chief Scientist Office Scotland and Diabetes UK. The genotyping costs were granted by the  Wellcome Trust  for WTCCC2 samples and by the  Innovative Medicines Initiative  for SUMMIT (SUrrogate markers for Micro-and Macrovascular hard endpoints for Innovative diabetes Tools) samples. The  SUMMIT consortium  was supported by the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) for the  Innovative Medicine Initiative  under grant agreement  IMI/115006  (to the  SUMMIT consortium). FinnDiane was supported by grants from the  Folkhälsan Research Foundation , the  Wilhelm and Else Stockmann Foundation , the  Liv och Hälsa Foundation ,  Helsinki University Central Hospital Research Funds (EVO) , the  Novo Nordisk Foundation  ( NNF14SA0003 ), and the  Academy of Finland  ( 134379 ,  275614 , and  299200 ).
Bartter Award ,  M01-RR-01346. Computing resources were provided, in part, by the  Wake Forest School of Medicine Center for Public Health Genomics. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. M. I. M. is a  Wellcome Senior Investigator  supported by  Wellcome  awards  090532 ,  106130 ,  098381 , and  203141  and a  National Institute for Health Research Senior Investigator. The views expressed in this article are those of the authors and not necessarily those of the  National Health Service, National Institute for Health Research , or the  Department of Health. Duality of Interest. J. Z. K. is employed by  Sun Pharmaceutical Industries, Inc. P. -H. G. has received investigator-initiated research grants from  Eli Lilly and Company and Roche , is an advisory board member for  AbbVie, AstraZeneca ,  Boehringer Ingelheim ,  Cebix ,  Eli Lilly and Company ,  Janssen ,  Medscape ,  Merck Sharp & Dohme ,  Novartis ,  Novo Nordisk , and  Sanofi , and has received lecture fees from  AstraZeneca ,  Boehringer Ingelheim ,  Eli Lilly ,  ELO Water ,  Genzyme ,  Merck Sharp & Dohme ,  Medscape ,  Novo Nordisk , and  Sanofi.
5 A-C ). To track glucose uptake in vivo directly, we injected the fluorescent glucose analog 2-NBDG into tumor-bearing mice. In line with previous data  (17, 18) , Tconvs showed a massive defect in glucose uptake in tumor compared with spleen; conversely, Tregs displayed a relatively low glucose absorption in spleen that was maintained in tumor, resulting in a significant rescue of the Treg/Tconv relative ratio of glucose uptake at the tumor site (Fig. 5 D-F ). Then, we analyzed the expression of selected glycolysis-related genes (Hk2, Gapdh, and Eno1) in Tregs and Tconvs through the PrimeFlow RNA assay. All three genes were significantly more represented in TUM-Tregs than in TUM-Tconvs (Fig. 5G ). Interestingly, in both Tregs and Tconvs, Hk2 and Eno1 [the latter being a master regulator of human Treg metabolism and development  (11) ] were repressed in tumors compared with spleens, in line with a general contraction of glycolysis in the tumor context; however, their expression remained higher in Tregs in both compartments, pointing to a preferential preservation of glycolytic activity in Tregs in the TME (Fig.
For all the metabolic indexes analyzed, Tregs showed a relative advantage over Tconvs specifically at the tumor site, as evidenced by higher Treg/Tconv ratios (Fig. 6 D  and E ). These data demonstrated that tumor-associated Tregs engage both glycolysis and OxPhos for their expansion. To assess whether these metabolic routes are differentially required for Treg and Tconv proliferation, we performed the Treg/Tconv coculture assay described above in the presence of 2-deoxyglucose (2DG) or etomoxir (Etx), selective inhibitors of glycolysis or FAO, respectively. In line with previous results with human Tregs (  14 ), 2DG profoundly restrained proliferation and lipid accumulation to a similar extent in both Tconvs and Tregs, suggesting that both cell types require glycolysis to support cell division and FAS.
Data were acquired on an LSR Fortessa cell analyzer (Becton Dickinson) and were analyzed with FlowJo software (version 10. 1r5; Tree Star Inc. ). In Vitro Proliferation Assays. CD4 + CD25 + Tregs and CD4 + CD25 -Tconvs were magnetically purified from splenocytes of wild-type C57BL/6 mice using the CD4 + CD25 + Regulatory T Cell Isolation Kit (Miltenyi Biotec), labeled with 10 μM of the cell proliferation dye eFluor670 (eBioscience) by incubation for 15 min at 37 °C in complete RPMI Dutch-modified medium, and cultured or cocultured at 1:1 ratios in the presence of equal numbers of irradiated (3,500 rad) autologous splenocytes plus soluble anti-CD3 (1 μg/mL; eBioscience) and IL-2 (100 IU/mL; Roche), with or without TOFA (Sigma), Etx (Sigma), or 2-DG (Sigma) at different concentrations. After 4 d of culture, analysis of eFluor670 dilution and Bodipy incorporation was performed in gated Foxp3 + Tregs or Foxp3 -Tconvs by flow cytometry.
Pellets were then resuspended in 250 μL methanol/acetonitrile 1:1 containing [U- 13 C 6 ]-Glucose 1 ng/μL (internal standard; 389374; Sigma Aldrich) and lysed by TissueLyser (Qiagen) for 3 min at the highest frequency. Lysates were spun at 20,000 × g for 5 min at 4 °C. Supernatants were then passed through a regenerated cellulose filter, dried, and resuspended in 100 μL of MeOH for subsequent analysis. Metabolomic data were obtained on an API-4000 triple quadrupole mass spectrometer (AB Sciex) coupled with an HPLC system (Agilent) and a CTC PAL HTS autosampler (PAL System). The identity of all metabolites was confirmed using pure standards. Quantification of different metabolites was performed with an LC-MS/MS method using a cyano-phase LUNA column (50 mm × 4. 6 mm × 5 μm; Phenomenex). Methanolic samples were analyzed by a 5-min run in negative ion mode with 30 multiple reaction monitoring (MRM) transitions.
The mobile phase A was 5 mM ammonium acetate (pH 7. 00) in MeOH. The gradient was 100% A for all the analysis with a flow rate of 500 μL/min. MultiQuant software (version 3. 0. 2; SCIEX) was used for data analysis and peak review of chromatograms. Quantitative evaluation of all metabolites was performed based on calibration curves with pure standards; then data were normalized on numbers of total purified cells. XTT Cytotoxicity Assay. To assess the cytotoxic effects of TOFA and Etx on tumor cells in vitro, MCA38 cells were seeded at a concentration of 5 × 10 3 cells per well into 200 μL of complete DMEM in a flat-bottomed 96-well plate in the presence of different concentrations of TOFA and Etx or DMSO and water as respective controls. Conditions without cells and cell treatment with 0. 05% saponin (Sigma-Aldrich) were used as negative and positive controls, respectively.
For each sample 100 ng of total RNA were reverse transcribed according to the Illumina TotalPrep RNA Amplification kit (Ambion), and biotinylated cRNA was generated by in vitro transcription. Washing, staining, and hybridization were performed according to the standard Illumina protocol: Briefly, 750 ng of cRNA of each sample in a final volume of 15 μL were hybridized onto Illumina HumanHT-12 v4 Expression BeadChip arrays. Hybridization and scanning were performed according to the manufacturer's indications on an Illumina iScan System, and data were processed with BeadStudio v. 3 (Illumina). Arrays were quantile normalized, with no background subtraction, and average signals were calculated on gene-level data for genes whose detection P value was lower than 0. 001 in at least one sample. Statistical Analysis.
ACKNOWLEDGMENTS. We thank  Massimo Rossi ,  Nicola Guglielmo , and  Gian Luca Grazi  for liver specimens;  Mario P. Colombo  for providing mouse tumor cell lines;  Natalia Pediconi  and  Massimo Levrero  for discussions and sharing reagents;  Alfonso Grimaldi  for collaboration in the revision process;  Giovanni Bernardini  for sharing reagents; and  Hervé Lerat  for providing HCVTg colony founders. This work was supported by  Associazione Italiana per la Ricerca sul Cancro  Grants  IG-2014 15199  and IG-2017 19939 (to V. B. ) and IG-2017 19784 (to S. P. );  Ministry of Education, University and Research (MIUR)  Grants  RF-2010-2310438  and  RF 2010-2318269 ;  Fondazione Italiana Sclerosi Multipla Grants  code  2015/R/04  (to V. B. ), code  2013/R/9  (to E. M. C. ), code  2016/R/18  (to G.
M. ), and code  2014/R/19  (to M. S. );  MIUR Progetti di Ricerca di Interesse Nazionale  Grant  2010-2011  protocol  2010LC747T_004 ;  MIUR Fondo per gli Investimenti della Ricerca di Base   2011/13  Grant no. RBAP10TPXK ;  Istituto Pasteur Italia-Fondazione Cenci Bolognetti Grant   2014-2016 ;  International Network Institut Pasteur Programmes Transversaux de Recherche Grant   20-16 ;  Fondazione Roma Grant for Biomedical Research   NCDS-2013-000000345 ; the  European Union 's  Seventh Framework Program  ( FP7)  under Grant Agreement 259743 (MODHEP consortium) (to Y. W. );  Fondazione Cariplo  Grant  2016-0852  (to G. D. N. );  Ministero della Salute  Grants  GR-2011-02346974  (to G. D. N. ) and  GR-2013-02355011  (to F. B. ); and  European Union IDEAS Programme European Research Council   Starting Grant  menTORingTregs  310496  and  Telethon Grant   GGP17086  (to G.
, x n ) (1) Rule 1: if x is A 1 and y is B 1 THEN f1= p1x + q1y + r1 (2) Rule 2: if x is A2 and y is B2 THEN f2= p2x + q2y + r2 (3) The general structure of ANFIS method is illustrated in Fig. 2. Layer 1: Every node is an Adaptive to a function parameter. O1, i= µAi(x),. for. i= 1, 2,… or (4) O1, i=µBi-1 (y),. for. i= 3, 4 Ai, Bi-1: Linguistic label which belongs to Node i. O1, i: Membership grade. Layer 2: Nodes of layer 2 are supposed as node II, the output of which is calculated as Eq. (  6 ): 2 , 1 1 2. , 1, 2 i wi O w i w w     (6) Layer 3: Layer 3 nodes are nonadaptive and are labeled as N. These nodes are calculated from the ratio between the i th laws firing strength and the sum of all rules' firing strengths. 3 , 1 1 2. , 1,2 i wi O w i w w     (7) Layer 4: Each "i" node in layer 4 is an adaptive node to an output: 4 , 1 ( ) i i i i i i O w f w px qy r     (8) 5 , 1 i i i i i i i i w f O wf w      (9) The ANFIS approaches were implemented using MATLAB in this study.
CONCLUSION In the current research, capability of the ANFIS method was assessed for estimating hydraulic jump characteristics (i. e. sequent depth ratio and jump's length) in channels with different boundary conditions. ANFIS was applied for different datasets in channels with smooth bed, rough bed, with a step and central block. The obtained results revealed that in predicting the sequent depth ratio the model including Fr1 and y1/B as input variables performed more successful than other models. The superior performance for length of hydraulic jump was obtained for the model with input parameters of Fr1 and (y2-y1)/y1. Comparison between the results of channels with different shapes showed that the developed models for the case of channel with a central block led to more accurate outcome.
They are not, so far, commonly used in rehabilitation. Software development began mainly with 2D environments. It is today conducted in 3D environments due to fast developing 3D design software and the use of more robust computers. This is particularly the case of the simulator ViEW (Virtual Electrical Wheelchair) developed by our research team and used in the present study  [5, 6]. It has several objectives: safe driving training  [6] , testing control skills relative to user-specific wheelchair prescriptions  [5] , providing aid for PW parametrization, settings and testing new features  [7]. A major interest of simulation is to be able to measure driving performance indices in a simple way: duration of a mobility task, number of movements on the joystick  [2] , spectral analysis of movements  [8] , velocity, average deviation compared to a reference  [9]  or compared to an optimal trajectory  [8] , the length of a path or the number of collisions  [4, 10].
The ViEW simulator The ViEW simulator (Virtual Electrical Wheelchair) is designed in an entire software way, i. e. without a mechanical platform. This choice is justified by the need for a system easy to duplicate and to transport to various rehabilitation centres. The target population of the simulator is indeed made up of people with severe motor disabilities. This implies that the experiments in a research laboratory will be very difficult or even impossible to carry out. The information feedback towards the user is consequently only visual via a single screen. This choice was made to facilitate the spreading of the simulator. The use of a head mounted display is technically possible but is rejected in most practical situations: the user is by assumption a person with a severe motor deficiency and has therefore generally many difficulties to wear this kind of equipment.
People with disabilities The second experiment took place in the Centre of Physical Medicine and Rehabilitation for Children of Flavigny sur Moselle. Eight children with different types of disabilities (praxis and visuospatial disorders, learning disabilities) were selected. During the experiment, the subjects used the same virtual environment and the same wheelchair architecture as those used by the healthy subjects. For the measure of their D p , we used the same eye tracker described in Section 3. 2. A trial period between 5 to 10 minutes was given to discover the simulator. Each of them made at least three tests at each speed from 0. 82 to 2. 02 m/s. The visual impairment of 3 subjects led to unusable data and were then excluded from the study. The eye tracking device was, in fact, not able to detect their gaze.
D p to differentiate the driving behaviours For all the following results the data normality has been successfully checked using the Shapiro-Wilk statistical test. Table  2  sums up the results of the normality test for healthy people. We analysed the variations of the preview distance D p for all 15 healthy subjects on the test course according to the type of driving situation. Fig. 7  shows the box plots of D p average for all 15 subjects at 0. 82 and 1. 32 m/s. In zone 2, we see that the users D p range is less than D p ranges in zone 1 and zone 3, which means that the users paid more attention in this zone. In zone 1, we see that the D p range is lower than in zone 3, which may be explained by the constraints this region features. We therefore suggest three driving behaviours depending on D p values at different areas of the trajectory.
For that, we calculate the averages of the preview distances D p by user group for each speed on the 3 zones of the course. Fig. 12  shows greater values of D p for the accustomed group on each of the 3 zones. This observation is confirmed by a Student's t-test by gathering the data obtained at various speeds. Table  5  summarizes the results and shows clearly (p far below the threshold α = 5%) that a significant difference exists between the accustomed and the novices groups.
This work describes the modeling, control and development of a low cost Micro Autonomous Underwater Vehicle (µ-AUV), named AR2D2. The main objective of this work is to make the vehicle to detect and follow an object with defined color by means of the readings of a depth sensor and the information provided by an artificial vision system. A nonlinear PD (Proportional-Derivative) controller is implemented on the vehicle in order to stabilize the heave and surge movements. A formal stability proof of the closed-loop system using Lyapunov's theory is given. Furthermore, the performance of the µ-AUV is validated through numerical simulations in MatLab and real-time experiments.
Embedded System The hardware architecture of the prototype consists of an embedded system that includes a Raspberry Pi 2B, which has a quad-core ARM (Advanced RISC Machines) Cortex-A7 CPU of 900 MHz, 1 GB RAM memory, camera interface (CSI), display interface (DSI), four USB ports, Ethernet port, Full HDMI port, 40 GPIO pins, and a slot to use a micro SD card for an operative system based on a Linux platform that allows for programming the Middleware. This embedded system also includes a compass sensor (CMPS10), an ultrasonic sensor with the driver SRM400, a depth sensor (BMP085) and a Raspberry Pi camera for artificial vision. The Raspberry processes the information from the sensors, computes de control laws and sends signals to the actuators (DC motors) controlled by Pulse Width Modulation using a micro maestro device (six-channel USB) in order to manage four thrusters through the Robbe Rookie 25A ESC (Electronic Speed Control) motor drivers.
Computer Vision The GNSS (Global Navigation Satellite System) signals cannot go through water, acoustic communication throughput is limited, odometry is impossible. However, vision is available. The camera provides rich data about the environment. Moreover, in the case of a Remote Operated Vehicle (ROV), it shows to the the user a lot of information to help him drive the robot. Computer Vision is a way to process these data autonomously, in order to implement obstacle avoidance, object following recognition and following, localization and mapping, without any human intervention. However, as other sensors, underwater vision poses some particular issues to be solved with respect to usual air vision. The main problem is about picture quality, depending on the water (clearness, suspended particles, etc.