dataesr/scientific-paragraphs-categorization huggingface dataset:
72), GV (P ¼ 0. 46), and GAP (P ¼ 0. 47), as well as rhesus monkey GH (P ¼ 0. 08) and GAP (P ¼ 0. 19). There appeared to be a statistically significant difference for the rhesus monkey GV (P ¼ 0. 033). Insufficient female data, with the same age distribution as for the males (17 female, 468 male), were available for assessment of cynomolgus dimensions. Male and female globe data were combined for further analyses. Although the increase is small, the data suggest that globe dimensions continue to increase throughout the period examined. This was explored by fitting the data to a two-parameter logistic growth curve using nonlinear regression. Reasonable logistic fits (R 2 ¼ 0. 43-0. 55) were obtained with the baboon and rhesus monkey data for each dimension, indicating that globe dimensions continue to increase throughout adult life toward an asymptotic maximum.
Lens Dimensions were determined for 41 baboon, 294 cynomolgus and 58 rhesus monkey lenses. They are presented in Figure  5. Male and female lens dimensions are the same for rhesus monkeys (D and T; P ¼ 0. 97 and 0. 65, respectively). This differs from the report by Fernandes et al. 7  that males have thicker lenses than females in vivo (by a fixed 0. 1017 mm, at all ages). It was not possible to compare baboon lens dimensions because of differences in the age distributions, with all male data obtained from animals younger than 105 months while most females were older than this (Figs. 5A, 5D ). However, logistic analysis of the male and female data (not shown) indicated that they could be described by the same equation. Insufficient female data were available for the cynomolgus monkeys to permit comparison.
Based on the present observations, it would appear that lens growth in the adult nonhuman primate is quite different from that in adult humans. As found for humans,  14, 18  isolated lens diameter increases with age in all three NHP species. Around the middle of the expected life span, lens diameter in the baboon, cynomolgus and rhesus monkey increases at approximately 0. 040, 0. 025, and 0. 045 mm/y, respectively. These rates are considerably faster than the 0. 013 mm/y for human lenses between ages 40 and 60 years. 18 GURE 6. Lens aspect ratio (T/D) for baboons (solid blue circles), cynomolgus (solid green circles) and rhesus monkeys (solid red circles). Male and female lens data have been combined. More notable is the observation that isolated lens thickness decreases continuously in all three NHPs, from around 5 mm early in life to 3.
Acknowledgments This work would not have been possible without the interest and support of our colleague  Professor Brien Holden , who was lost to vision science in July 2015. Apart from being a global leader in vision care and vision research, Brien was a humanitarian who did much to promote eye health care throughout the world and especially among the poor. The authors thank  Norma Kenyon  and  Dora Berman-Weinberg  of the  Diabetes Research Institute ,  Julia Zaias  and  James Geary of the Division of Veterinary Research at the University of Miami , and  Scott Heffernan  of the  Australian National Baboon Breeding Colony  for their assistance in obtaining the primate eyes. We thank the ophthalmic surgeons ( Christian Billotte ,  Viviana Fernandez ,  Joseph Stoiber ,  Ana Carolina Acosta ,  Hideo Yamamoto ,  Mohamed Gamil Metawalli Aly , and  Esdras Arrieta ) and the staff and students ( Eleut Hernandez ,  David Denham ,  Alexandre Rosen ,  Noel Ziebarth ,  David Borja ,  Mariela Aguilar ,  Andres Bernal ,  Derek Nankivil ,  Heather Durkee , and  Shawn Kelly ) of the  Ophthalmic Biophysics Center  who gave surgical and technical support during this study.
Supported in part by  NIH   Research Grants   R01EY014225 ,  R01 EY021834 ,  F31EY021444  ( NRSA Individual Predoctoral Fellowship [BMH]); Center Grant   P30EY14801 ; the  Australian Federal Government CRC Scheme  through the  Vision Cooperative Research Centre ; the  Florida Lions Eye Bank ; unrestricted funds from  Research to Prevent Blindness  and from  Karl R. Olsen  and  Martha E Hildebrandt ; and the  Henri and Flore Lesieur Foundation  (JMP). Disclosure:  R. C. Augusteyn , None;  B. Maceo Heilman , None;  A. Ho , None;  J. -M. Parel , None.
Abstract. In recent decades much attention has been given to the Arctic environment, where climate change is happening rapidly. Black carbon (BC) has been shown to be a major component of Arctic pollution that also affects the radiative balance. In the present study, we focused on how vegetation fires that occurred in northern Eurasia during the period of 2002-2013 influenced the budget of BC in the Arctic. For simulating the transport of fire emissions from northern Eurasia to the Arctic, we adopted BC fire emission estimates developed independently by GFED3 (Global Fire Emissions Database) and FEI-NE (Fire Emission Inventory -northern Eurasia). Both datasets were based on fire locations and burned areas detected by MODIS (Moderate resolution Imaging Spectroradiometer) instruments on NASA's (National Aeronautics and Space Administration) Terra and Aqua satellites.
Model set-up, BC inventories, and injection height The simulations lasted from 1 January 2002 to 31 December 2013. The model ran in a nudged mode using 6-hourly ERA Interim Re-analysis data (ECMWF, 2014) with a relaxation time of 10 days  (Hourdin and Issartel, 2000). To support the IPCC-AR5 (Intergovernmental Panel for Climate Change Assessment Report 5) and the ACCMIP (Atmospheric Chemistry and Climate Model Intercomparison Project), a historical emissions dataset was developed  (Lamarque et al. , 2010)  on a decadal basis (from 1850 to N. Evangeliou et al. :  Wildfires in northern Eurasia affect the budget of black carbon in the Arctic 2000 for the historical dataset), as well as RCP (Representation Concentration Pathway) emission scenarios for the period after the year 2000. As part of a project funded by the European Commission, MACC (Monitoring Atmospheric Composition and Climate) and CityZen (megacity Zoom for the Environment), the ACCMIP and the RCP emissions datasets were adapted and extended to the year 2013 on a yearly basis.
For anthropogenic emissions, emission data were interpolated on a yearly basis. For BB emissions, the ACCMIP dataset was extended to yearly and monthly mean calculated from modified RETRO (REanalysis of the TROposhperic chemical composition) BC emission data for the years 1980 to 1996, and from GFEDv3 carbon emission data for the years 1997 to 2013 by applying a single set of vegetation-type-specific emission factors, and the predominant vegetation map used in the GFEDv3 inventory. This extension of the ACCMIP and RCP emission dataset for the MACC and CityZEN projects is referred to as MACCity (MACC/CityZen) emission dataset  (Granier et al. , 2011). As it is explained below, emissions of MACCity (anthropogenic and BB) were used as the input source of the model worldwide, in which BB sources were derived from GFEDv3.
BC emissions from FEI-NE and MACCity For the 12-year global simulations, anthropogenic sources of BC were adopted from the MACCity emission database. With regards to BB emissions, MACCity BB emissions from GFED3 were applied for all the regions outside northern Eurasia, while within northern Eurasia anthropogenic emissions from MACCity and BB from FEI-NE  (Hao et al. , 2016)  were adopted. In summary, BC emissions from BB in northern Eurasia, excluding agricultural fires, were estimated based on the area burned, fuel loading, percentage of the fuel burned, and emission factors of BC from different vegetation types  (Hao et al. , 2016). This combined simulation is hereafter referred to as FEI-NE+MACCity. For comparison, we carried out the same simulation but using MACCity emissions alone (i.
Observation data With different types of instruments, we collected measurements of BC, which may not always be directly comparable. Following the nomenclature of  Petzold et al. (2013) , we referred to measurements based on light absorption as equivalent BC (eBC) and measurements based on thermal-optical methods as elemental carbon (EC). Aerosol light absorption data were obtained from five sites in different parts of the Arctic: Alert, Canada (62. 3 • W, 82. 5 • N; 210 m a. s. l. ), Barrow, Alaska (156. 6 • W, 71. 3 • N; 11 m a. s. l. ), Zeppelin/Ny Ålesund, Spitsbergen, Norway (11. 9 • E, 78. 9 • N; 478 m a. s. l. ), and Tiksi, Russia (128. 9 • E, 15 71. 6 • N; 1 m a. s. l. ). Different types of particle soot absorption photometers (PSAPs) were used for the measurements at Barrow and Zeppelin, and an aethalometer was used at Alert and Tiksi.
At Villum Research Station, Station Nord, Greenland, thermal-optical measurements were available. Weekly aerosol samples were analysed with a thermal-optical Lab OC / EC instrument from Sunset Laboratory Inc (Tigard, OR, USA). Punches of 2. 5 cm 2 were cut from the filters sampled at Villum and analysed according to the EUSAAR-2 protocol  (Cavalli et al. , 2010). At Alert, eBC data were available for the years 2006-2013, at Barrow for 2002-2013, at Tiksi for 2009-2013, and at Zeppelin for 2002-2013. At Villum station, EC data were available for 2008-2011. The EC and eBC data were directly compared with modelled BC concentrations for the same locations and periods. Tiksi data were not filtered for a clean air sector and may have been affected by local pollution events. Barrow and Alert data were routinely subject to data clean-ing, which removed the influence from local sources.
They also found a strong correlation between surface measured sulfate and BC concentrations in winter and spring (anthropogenic impact), which indicated that the sources contributing to sulfate and BC were similar throughout the Arctic and that the aerosols were internally mixed and undergo similar removal. Neither  Eckhardt et al. (2015)  nor Samset et   2014 ) could isolate the reason to explain why some models performed better than the others. In the present model configuration, we included the emission inventory from FEI-NE and MACCity's anthropogenic BC inside northern Eurasia and MACCity (BB and anthropogenic) outside northern Eurasia (FEI-NE+MACCity) respectively to evaluate if it produced reliable results with respect to observations. The question that stemmed from this comparison was whether or not existing datasets included all possible sources of BC emission.
Conclusions The present study focused on the impact of vegetation fires occurring in northern Eurasia on BC deposition in the Arctic. For this reason, a three-dimensional global transport model (LMDz-OR-INCA) was used to simulate fire events that took place during 2002-2013. Anthropogenic emissions were adopted from MACCity inventory, while BB emissions within northern Eurasia were from FEI-NE and beyond northern Eurasia from MACCity's GFEDv3 database. A total of 3. 0 × 10 6 km 2 was burned during the 12-year period in northern Eurasia with the majority being grassland and forest fires. Total global emissions of BC ranged from 8. 02 to 9. 48 Tg yr -1 (average: 8. 42 ± 0. 43 Tg yr -1 ) with the highest ones recorded for the years  2003, 2006, 2008, and 2012. The annual emissions from vegetation fires in northern Eurasia were estimated to be between 0.
Acknowledgements. This study was supported by the  US Forest Service ,  Rocky Mountain Research Station. We were granted access to the HPC resources of [CCRT/TGCC/CINES/IDRIS] under the allocation 2012-t2012012201 made by  GENCI  ( Grand Equipement National de Calcul Intensif ). We would also like to acknowledge the  World Data Centre for Aerosol , in which BC measurements from Arctic stations are hosted (http://ebas. nilu. no).
When dealing with multiple agents that have to take local decisions cooperatively based on limited interaction with their peers, the issue of determining whether they behave in an overall optimal fashion is an old and well known problem that is encountered in many diverse and unusual applications such as the control of multiple updates to a distributed database  [48] , the access of several processes to common data  [4] , defense against Distributed Denial of Service attacks  [40] , search by multiple agents  [23, 26] , auctions of different buyers within a shared economy  [25] , or emergency management where many evacuees have to exit a common area  [5, 8, 54]. In the paper by Bruno Gaujal and Panayotis Mertikopoulos, "A Distributed Stochastic Approximation Algorithm for Stochastic Semi-definite Programming", a distributed stochastic approximation algorithm is presented, motivated by communications between multiple agents using common resources, such as shared wireless environments with Multiple-Input-Multiple-Output.
(2) Diagnosis: The ability of the candidate to synthesize clinical, laboratory and histopathological data in order to reach the correct (differential) diagnosis. (3) Treatment: Candidates will be expected to provide a reasonable and appropriate plan for medical and surgical management of patients with the conditions depicted or described. No further details will be asked on details about surgical techniques proposed (no surgical skill evaluation is performed at the comprehensive EBOD examination). Although candidates are expected to obtain a score equal or above 6 for each of the viva (pass mark equal to 6), candidates may compensate for one lower viva score, provided they did not fail at Part 1 of the comprehensive EBOD examination, and provided they did not fail at any other of the viva, and have obtained a final EBOD score equal or higher than 6.
2013). Due to this change in precipitation, snow accumulation (the sum of precipitation, sublimation/evaporation, meltwater run-off, and blowing snow) should also increase in Antarctica  (Church et al. 2013). Abstract On average, the models in the Fifth Climate Model Intercomparison Project archive predict an increase in Antarctic precipitation from 5. 5 to  24. 5 % between 1986-2005 and 2080-2099 , depending on greenhouse gas emissions scenarios. This translates into a moderation of future sea level rise ranging from -19 to -71 mm between 2006 and 2099. However, comparison with CloudSat and ERA-Interim data show that almost all the models overestimate current Antarctic precipitation, some by more than 100 %. If only the models that agree with CloudSat data within 20 % of error are considered, larger precipitation changes (from 7.
Data and methods The CloudSat product 2C-SNOW-PROFILE  (Wood et al. 2013 ) that provides instantaneous estimates of snowfall since August 2006 was aggregated to a 1° (latitude) × 2 (longitude) grid between 63°S and 82°S as described by  Palerme et al. (2014). Due to battery problems, Cloud-Sat has not provided any data during the night since April 2011, thus the period 2007-2010 is adopted in this study to take into account the four complete years available from the CloudSat 2C-SNOW-PROFILE product. The uncertainty on this climatology can not be easily estimated, but good agreement has been found between this climatology and ERA Interim reanalysis, which is encouraging for the reliability of both datasets  (Palerme et al. 2014 ). However, precipitation events occurring below 1200 m above the surface are missed with the CloudSat 2C-SNOW-PROFILE algorithm due to ground clutter contamination  (Wood et al.
2011). It has been shown that the snow accumulation (defined as precipitation minus evaporation) simulated by ERA Interim reanalysis is close to airborne radar observations over Thwaites Glacier in West Antarctica  (Medley et al. 2013 ). Moreover, it has been suggested that ERA Interim likely offers the most realistic depiction of precipitation changes in Antarctica among various reanalysis datasets  (Bromwich et al. 2011 ). Nevertheless, similar parameterizations could be used in ERA Interim reanalysis and the CMIP5 models for simulating precipitation, which could induce similar bias in the different datasets. The Historical scenario, in which the coupled climate models are forced by observed atmospheric composition changes from the middle of the nineteenth century to the early twenty-first century  (Taylor et al.
2012) , has been used for current climate evaluation. Simulations of Antarctic snowfall in the Historical scenario have been compared to simulations from the Atmospheric Model Intercomparison Project (AMIP) experiment to investigate the influence of sea surface conditions on Antarctic precipitation. In the AMIP experiment  (Gates 1992) , the atmospheric models are constrained by realistic sea surface temperature and sea ice from 1979 to near present (2008 for most of the CMIP5 models). Because the atmospheric models are not subject to the ocean-atmosphere feedbacks, the AMIP experiment is expected to be the most reliable experiment for current precipitation simulations. For current climate evaluation, the period 1986-2005 has been adopted for all datasets, except for CloudSat observations for which the period 2007-2010 has been used.
59 if all the values are taken into account, but is really better (0. 91) without these nine values. There are issues concerning both datasets over mountainous areas. In CloudSat observations, the ground clutter is likely stronger over mountainous areas than over flat terrain, and may induce a spuriously high snowfall rate, while orographic precipitation may not be well represented in ERA Interim due to a too coarse spatial resolution. In ERA Interim, the mean snowfall rate on the Antarctic continent north of 82°S is similar during the period 1986-2005 (165 mm/year) and during the period covered by CloudSat observations (165 mm/year between 2007 and 2010). Although the interannual variability of Antarctic snowfall could be very high at regional scales, it is relatively weak at the continent scale.
However, the snowfall rate in the CMIP5 models ranges from 211 to 632 mm/year over this part of the ice sheet. The minimum snowfall rate is observed in the summer months of December-January-February (DJF). There are small differences in CloudSat data and ERA Interim reanalysis between the two seasons with the highest snowfall rate (MAM and JJA). 24 of the 40 CMIP5 models analyzed reproduce this seasonality on the continent north of 82°S, and 26 of the 40 models capture the seasonality on the peripheral regions of the ice sheet.
On average, these changes vary from -0. 81 x 10 6 km 2 for the scenario RCP2. 6 to -3. 2 x 10 6 km 2 for the scenario RCP8. 5. Simulated sea ice area changes are stronger in the models that closely reproduce the CloudSat snowfall rate for the current period (Fig. 10 ). This suggests that the stronger Antarctic precipitation increases predicted by these models could be linked with larger sea ice area changes in the southern hemisphere simulated by these models. The annual-mean sea ice areas simulated by the CMIP5 models during the period 1986-2005 have been reported in Fig. 11 , and compared to sea ice area observations in the southern hemisphere provided by the U. S. National Snow and Ice Data Center (NSIDC). These observations have been obtained from data provided by the radiometers Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Special Sensor Microwave Imager Sounder (SSMIS).
grant agreement number  603608  ( Earth2Observe  project) is gratefully acknowledged. We are grateful to the  Centre National d'Études Spatiales (CNES). Funding for this project was provided by a grant from la  Région Rhône-Alpes. J. E. K. was funded by  NASA  grant  12-CCST10-0095. Parts of this research conducted by  N. B. Wood  and  T. LEcuyer  were performed at the  University of Wisconsin-Madison  for the  Jet Propulsion Laboratory, California Institute of Technology , sponsored by  National Aeronautics and Space Administration   CloudSat Research Grant   G-3969-1. Finally, we thank the four anonymous reviewers for their comments which significantly improved the manuscript.
Though we presented only raw data in the figures (except from Supplementary Fig. 20  for demonstration purposes), a smoothing algorithm could be used to monitor smaller changes. For instance, although noticing the temperature effect on the frequency response was difficult with the raw data (Fig. 2a ), result clearly showed the correlation between the temperature and f R after smoothing. Temperature change of 0. 2 °C inside the box caused ~0. 02 Hz oscillations in f R (Supplementary Fig. 20 ). To test the effect of different solutions, we injected liquid to the channel via the inlet of the microfluidic device. The outlet was connected to a pressure pump (Elveflow AF1 Dual) for generating flow when necessary. The flow was detected with a microfluidic flow sensor (Elveflow MFS). To minimize the effect of evaporation for long experiments (Fig.
Tweezers insertion protocol. The motion of the microfluidic device (on the XYZ stage) was controlled with a PC for precise relative positioning (Supplementary Fig. 1 ). To start the insertion protocol, the PDMS rim was placed over the tweezers. (i) The first step was to detect the relative position of the PDMS layer (top of the channel) and the tweezers. The developed LabVIEW software ran the XYZ stage (0. 1 μ m steps with a speed of 100 μ m s -1 ) while monitoring f R. When the PDMS layer touched the tweezers, the lock-in-amplifier detected the sudden change in the frequency response (amplitude and phase) and stopped the XYZ stage. The microfluidic device was then moved 30 μ m away from tweezers for safe insertion between the PDMS and glass layers. (ii) After moving the microfluidic device toward the tweezers, the second step was to detect the top and bottom level position of the channel (PDMS and glass levels).
Experimental process. The developed software (using LabVIEW) was capable of controlling the XYZ stage, microfluidic pump, lock-in-amplifier, heater, temperature and humidity sensors, a camera and additional function generators (in case necessary, e. g. for DEP). As a result, the software could monitor the frequency response in real-time, position the microfluidic device, control the flow in the channel, apply DEP voltage, visualize the experiment and set the experimental conditions inside the box. The fixed stage for the tweezers included a specially designed spring pin-connection system and necessary connections to the amplifiers and function generators. After the fabrication process, tweezers were wire bonded on a printed circuit board (PCB) designed to provide the appropriate connections to the equipment.
Acknowledgements This research was supported in part by the  Ministry of Education, Culture, Sports, Science and Technology of Japan  ( Grant-in-Aid for Scientific Research  ( KAKENHI )  23710108  to M. C. T. ) and JSPS Core-to- Core Program. Y. T. thanks the  Université of Lyon 1  for financial support for working at the  University of Tokyo. Authors would like to thank  VLSI Design  and  Education Center  ( VDEC, The University of Tokyo ) for mask production and  H. Guillou  ( Neel Institute, Grenoble, France ) for his contribution on setup development.
Pluripotency markers and BCR-ABL expression For flow cytometry analysis, iPSC polyclonal stock and three clones PB32-1, PB32-4 and PB32-5 were recovered from Mitomycin-C inactivated MEF after incubation for 2 h in 1 mg/ml collagenase IV in DMEM/F12. After two cycles of washing/sedimentation with 10 ml DMEM/F12, colonies were washed once in Ca 2+ /Mg2+ free PBS (Life technologies) and then dissociated into a single cell suspension by 10 min incubation in Hank's balanced enzyme-free cell dissociation buffer (Life Technologies). For pluripotency marker analysis, 1 × 10 5 cells were incubated in 10 μl PBS containing 1 μl V450-conjugated mouse monoclonal anti-SSEA-4 and 1 μl Alexa647-conjugated mouse monoclonal antibody anti-TRA1-60 (both from BD Biosciences) or conjugated isotype control. BCR-ABL expression was performed by both Q-RT-PCR and Western blots showing expression of BCR-ABL mRNA and protein in PB32 polyclonal iPSC and three clones (data not shown).