dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients of low weight should have hemoglobin of at least 12 g/dL or should be transfused with RBCs to reach this level  [98]. Similarly, when severe thrombocytopenia is present, platelet transfusion to above 40 Â 10 9 /L is recommended to prevent bleeding complications. In children who weigh less than 20 kg, the apheresis machine should be primed with RBCs and/or human albumin to lower the extracorporeal volume  [19, 99]. Question 3: Are there special considerations for obese patients? There are little data to address growth factor dosing in obese patients. One retrospective study of 86 patients reviewed outcomes after 2 different G-CSF mobilization regimens: either single daily dose (14 mg/kg/day) or split dose (2 Â 7 mg/kg/day). Patients were stratified according to body mass index (BMI; 25 or >25).
ACKNOWLEDGMENTS Conflict of interest statement:  H. K. D. has received research support from  Sanofi  and  Celgene  and is on the  Speaker's Bureau for Sanofi and Celgene. W. B. has received research support from  Amgen  and has consulted for Amgen. S. D. has received research support from  Sanofi. M. M. has received honoraria and research support from  Amgen ,  Chugai , and  Sanofi , whose products are discussed in this work. L. J. C. has received research support for Onyx,  Celgene ,  BMS , and  Novartis ; has consulted for Onyx and Sanofi; and is on the  Speaker's Bureau for Sanofi. M. A. P. has consulted for Sanofi. P. S. is on Speaker's Bureau and has served on  Advisory Board  for Sanofi and is on Speaker's  Bureau for Millenium. Financial disclosure: The authors have nothing to disclose.
Given the restrictions posed by chip interconnect and packaging technologies, this is obviously not a feasible approach. However, VLSI technology does offer a workaround. Leveraging the five orders of magnitude or more of difference in bandwidth between a neuron (typically spiking at rates between 10 and 1000 Hz) and a digital bus enables engineers to replace thousands of dedicated point-topoint connections with a few metal wires and lots of switches, and to time multiplex the traffic over these wires using a packet-based or ''event-based'' data protocol called address-event representation (AER). AER was originally proposed more than 20 years ago in Mead's Caltech research lab  [26] ,  [28] ,  [29]. For over ten years, AER sensory systems were reported by only a handful of research groups, examples being Lazzaro et al.
Fig. 2 (a) illustrates an AER interface servicing an array of spiking pixels. The on-chip AER periphery contains address encoders, bus arbiters, and handshake circuitry, implementing a four-phase handshake with the data receiver [Fig. 2(b) ]  [17]. The AER bus is a multibit (either parallel, serial, or mixed) bus which transmits the addresses of the emitting neurons. Typical delays for transmitting address events between AER chips range from about 30 ns  [38]  to 1 s  [48]  per event for parallel AER, and have been reported down to 16 ns per event for serial AER  [39]. 2  Addresses are received, read, and decoded by the receiver chip (or module) and sent to the corresponding destination neuron or neurons. The use of AER splitters and mergers  [40]  allows extension to one-to-many, manyto-one, or many-to-many AER links.
These sensors' output is compressed at the sensor level, without the need of external processors, optimizing data transfer, storage, and processing, hence increasing power efficiency and compactness of the vision system. The dynamic vision sensor (DVS) is the first practically usable device of this class and has triggered a plethora of research in eventbased vision. The asynchronous time-based image sensor (ATIS) continues the maturing and push to real-world applicability of bioinspired vision and derives its unique characteristics from combining the advantages of eventdriven acquisition and time-domain spiking encoding of image information. The DAVIS sensor proposes a hybrid between frame-based and frame-free sensor technology. The speed advantage of the reported interchip serial AER interface is due to the use of low-voltage differential signaling (LVDS) transmission over pairs of impedance matched microstrip lines that can be driven near the physical limit of several gigabits per second.
Similarly, O'Connor et al. [69]  have shown handwritten character recognition using event-driven stochastic encoding learning and processing. No complete package such as OpenCV for event-driven vision processing exists to date, but there is a growing opensource Java-based environment for controlling AER-type hardware as well as performing basic processing like optic flow, spatial filtering, ego-motion suppression, etc. [70]. Future developments in bioinspired vision will focus on continued sensor improvements, together with developments of algorithms and hardware architectures for processing the sensor outputs. These approaches should further improve performance in artificial vision systems, e. g. , for wide DR high-speed imaging, while at the same time decreasing computational cost and latency by taking advantage of the sparsity and the precise timing of the event-based visual information encoding.
g. , K-means) cluster analyses. Hierarchical cluster analysis is based on the idea that patients who share similarities on a set of data can be grouped together. In the agglomerative techniques (the most widely used), the results are shown as a dendrogram in which each horizontal line represents an individual subject and the length of horizontal lines represents the degree of similarity between subjects  [30, 41]. The number of clusters is determined according to the results of the analysis (see below). In Kmeans cluster analysis, the number of clusters () is determined before the analysis and the algorithms find the cluster center and assign the objects to the nearest cluster center  [30, 41]. Drawbacks of the K-means analysis are the necessity of choosing the  number of clusters and the fact that the algorithms usually prefer clusters of approximately similar size, which may lead to ignoring a smaller, yet important, group of subjects  [30, 41].
Self-organizing maps (SOM) are an alternative neural network-based nonhierarchical clustering approach, which has been used for analysis of gene arrays  [42]  and has also been used recently to examine comorbidities in COPD patients  [40]. The choice of variables included in a cluster analysis is a very important aspect of the analysis; cluster analysis detects structures within selected variables, but cannot determine whether some of the selected variables are irrelevant for phenotyping. The choice of variable is dictated by practical considerations (e. g. , the type of data available in the cohort), underscoring the need for well-characterized cohorts. Although some investigators performed cluster analysis with as many variables as available in their database  [37] , we believe that selection of a limited number of variables considered important in defining the disease process may be preferable.
For example, when looking for phenotypes associated with different risk of mortality, our cluster analyses were based on data previously associated with death in COPD patients  [34, 43]. This strategy is very similar to what has been performed for analysis of multiple genes using gene arrays: although it is possible to set up arrays using very large numbers of gene, it is also possible to use gene sets containing smaller numbers of genes that are defined based on prior biological knowledge, for example, published information about biochemical pathways  [44]. The encoding of variables (e. g. , using raw data, categorized data, or -scores  [37] ) may also affect the results of a cluster analysis  [45] , but this aspect has not been formally explored in the COPD literature. Further, the ways to handle missing data, which are present in any large dataset of observational data, have been different among studies; it has resulted in exclusion of patients from the analysis in some studies  [34, 35] , whereas other investigators suggested the usefulness of multiple imputation for missing data, to avoid excluding patients from the analyses  [46].
If the piece is placed on its correct emplacement, a visual feedback is shown (a "flash") and the piece can no longer be moved. If the user touches a placed piece, the "flash" effect appears as a confirmation of its validation. When the 12 pieces are correctly placed on the grid, a congratulation message is displayed and the experimenter selects another game. The puzzle games were built on HTML5, CSS3, Javascript and Php and available on two touchscreen devices through Mozilla Firefox mobile web browser. Targets sizes (a puzzle piece) are 19x19mm on the smartphone (85 pixels width) and 35x35mm (195 pixels width) on the tablet. In order to compensate the lack of spacing between the targets, they are contoured by a 1mm dark border. The system starts counting the number of attempts once a piece is already placed on its right emplacement.
All data have been anonymized and kept confidential on a secure server. Participants 24 older adults aged of 65 years or more (65-86, mean 74. 25, SD 5. 8), 8 men (mean 73. 12, SD 5. 66) and 16 women (mean 74. 81, SD 5. 96), participated of the experiment. The number of subjects on each age range is represented on Figure  4. All of them had visual impairments. 3 of them didn't wear glasses. 2 had no or little sight on one eye due to injuries but sufficient correction for the healthy eye. Sight control before the experiment shows that none of them had problems to distinguish colors or a grid displayed on the screen placed near them. On the other hand, contrast sensitivity test shows that 2 persons had insufficient sight correction. They were all right handed, 3 women were ambidextrous but used the right hand to interact.
Age effects The collected data was divided according to the different age ranges. Results of Friedman tests show significant effect of age (chi-squared= 16. 35, df= 3, p-value= 0. 0009612). Figure  7  and Figure  8  describe SNA during interaction on smartphone and tablet, respectively, according to the age range. Because of the aging effects on sensorial and cognitive skills  [2]  it would be expected that younger subjects interact more accurately, executing less SNA. However, when analyzing each condition separately, subjects aged 80 years old or older sometimes performed better than younger subjects. For better understanding these results, potential interaction techniques effects have been searched. Results of the Wilcoxon Signed Rank test for subjects aged 80 years old or older showed significant effects for interaction techniques ( Z= 1.
, 2012). The additional in situ observations provided by Concordiasi constitute a reference dataset which was used to compare in situ data with satellite retrievals  (Wang et al. , 2013)  and numerical forecasts  (Cohn et al. , 2013)  in order to document shortcomings in models and data assimilation. This knowledge can then be used to improve forecasting and assimilation and lead to more accurate real-time analyses as well as improved re-analyses. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. The additional observations were mainly intended to complement routine observations, and to match with satellite overpasses.
A major exception is observed for the region of Victoria Land, including the area around Dome C and the Concordia radiosonde station where the statictime variance is large. Apparently, during this time period, the additional Concordia radiosonde profiles were not sufficient in number to offset large variance in analysed 500 hPa height caused by factors not simply related to the number of observations. Such factors include relatively large uncertainty in background forecasts for data assimilation over this region, due to the particular difficulty in forecasting the extreme weather over the Antarctic plateau, with strong thermal inversions and strong coupling with the snow-covered surface. As explained in  Cohn et al. (2013) , all models fail to represent the strong thermal inversion with the right intensity.
Observation impacts were computed at T107 resolution based on a dry energy norm from the surface to the top of the model using Eq. (  2 ), with g replaced by g = M T a P T CP(x f b -x t ) + M T b P T CP(x f a -x t ). (4) Note that Eq. (  4 ) is similar to Eq. (  3 ) except that the adjoint of the forecast model linearized about the forecast started from x a is applied to the error measure evaluated for the forecast started from x b , and vice versa. This corresponds to a second-order approximation of δe in terms of d  (Errico, 2007) , but for the measure Eq. (  1 ) provides essentially the same accuracy as the third-order form Eq. (3)  (Gelaro et al. , 2007). The GMAO produced a global simulation and observation impacts including Concordiasi dropsonde observations using a reduced-resolution version of the Goddard Earth Observing System 5 (GEOS-5) atmospheric data assimilation system  (Rienecker et al.
If we look more closely to the ranking, AMSU-A contribute the most, followed by IASI, radiosondes, AMVs from geostationary or polar satellites, aircraft, buoys, and radio-occultation data based on the global positioning system (GPS-RO). This is not a specific result for this assimilation and forecast system. Although the impact of any one data type depends on the mix of other data types, there is a broad consensus amongst the global numerical weather prediction (NWP) centres that these observation types are the biggest contributors to forecast skill, as noted in the final report of the fifth WMO workshop on the impact of various observing systems on numerical weather prediction (WMO, 2012). If we focus now on the Antarctic region, we can see different features. In situ measurements are rare due to the hostile environment for both manual and automatic systems.
Surprisingly AMVs, which are one of the biggest contributors in the global context, degrade (slightly) the Météo-France forecast in the polar context during this study period. Atmospheric InfraRed Sounder (AIRS) is not assimilated over sea-ice, nor over land for the moment (even though some stratospheric and upper tropospheric channels could be assimilated). Considering High-resolution Infrared Radiation Sounder (HIRS) observations over the South Pole area (land), the Météo-France system assimilates data from only a single water vapour channel if the altitude is less than 1500 m. Over sea, observations too far from the guess for one surface channel are discarded. On the contrary, Global Positioning System Radio Occultation (GPS-RO) measurements are independent of the surface type, cloud or particles in the air and so bring a contribution commensurate with their number.
Similarly, Figure  11  shows observation impact results obtained in the NRL system. Here, in a global context, AMVs have more impact than other observation types. This may be explained largely by the greater number of AMV data assimilated, compared to other systems. Another explanation may be more-optimal assimilation of AMVs using the NRL super-observation procedure. In the polar domain, AMVs wind data (MODIS) still provide the largest forecast error reduction, followed by satellite radiances, including AMSU-A and IASI, and in situ measurements such as radiosondes, land-surface observations and dropsonde profiles.
The results obtained for each system reflect different assimilation strategies. For GMAO, Météo-France and ECMWF, 75-79% of the total forecast error reduction comes from three instruments, which are not the same for each system. For the NRL system, there are more observation sources, leaving a more evenly-spaced distribution among six of the sources. AMVs in the NRL system have the largest impact on forecast error reduction in the south polar region due to the assimilation of a larger number of MODIS and AVHRR wind data in this particular system and an elaborate pre-processing and data selection. The NRL system uses nearly 39 000 AMV observations per day, which is a little more than in the GMAO system but four times more than in the Météo-France or ECMWF system. This case notwithstanding, microwave radiances of AMSU-A and infrared radiances of IASI have a good impact in all systems, representing around 60% of the total contribution (except for NRL, for which it is 33%).
8 and 12. 2% of the error reduction respectively. This is also true in the NRL system where this group of observations explains nearly 8% of the forecast error reduction. Measurements made by radiosondes contribute between 6 and 10% to the total in all systems except in the ECMWF system where they surprisingly contribute only 1. 3%. In contrast, AIRS infrared data play an important part in the ECMWF system as they contribute more than 22% of the total. This is the only system where they have such a large impact. GPS radio-occultation measurements play a very important role in the GMAO system. Additionally, they contribute nearly 18% of the total impact. Although this value is less important in NRL (4. 7%) and ECMWF (6. 4%), this type of observation has a good impact. Météo-France is the only system less responsive to these observations with only 2.
Dropsonde impact in the southern polar region During the 2010 campaign, an important effort was made to evaluate the impact of dropsonde observations. In this section, we detail the forecast improvements that dropsondes brought as part of the observing system. The impact of temperature, humidity and wind measurements are investigated as well as the impact of the location of these data. Although the dropsonde impact is relatively small compared to satellite data such as AMSU-A, IASI or GPS-RO, it is interesting to evaluate the impact of this relatively uniform source of atmospheric profiles in the region and to compare it with the impact provided by radiosondes over Antarctica. Figure  13  shows the dropsonde impact for each measured parameter at low and high levels (below and above 400 hPa).
Instruments that have the largest impact are AMSU, IASI, AIRS, GPS-RO, RAOB, surface and AMV data; however, the exact ranking varies among centres due to their data assimilation system specifications. The NRL shows a particularly strong impact of AMV data, as this centre uses more of these observations than the other centres or over-weights AMVs. Another explanation may be that radiance assimilation is not as optimum as that at other centres because of bias in the NRL forecast model. The Météo-France assimilation exhibits a large impact of AMSU-B data, following some research developments to be able to use these data over sea-ice. Compared to the global context, there is generally more impact of satellite data and less impact of conventional observations. This would be slightly different over the northern polar area, where radiosondes and aircraft play a more prominent role (not shown).
Acknowledgements Concordiasi is an international project, supported by the following agencies:  Météo-France ,  CNES ,  CNRS/INSU ,  NSF ,  NCAR ,  University of Wyoming, Purdue University, University of Colorado , the  Alfred Wegener Institute , the  Met Office and ECMWF. Concordiasi also benefits from logistic or financial support of the operational polar agencies IPEV, PNRA,  USAP  and  BAS , and from BSRN measurements at  Concordia. Concordiasi  is part of the THORPEX-IPY cluster within the International Polar Year effort. The authors would like to acknowledge  Cihan Sahin  ( ECMWF ) for providing the singular vectors optimised over the southern polar area during the Concordiasi experiment.
Expression of the AngioMatrix signature correlates with angiogenesis markers, tumor progression and poor prognosis for CRC, low grade glioma and GBM patients To address the potential relevance of the AngioMatrix signature for cancer patients, we analyzed transcriptomic datasets, as this strategy enables investigating large and independent patient cohorts. Since insulinoma is rare and mostly benign (and no dataset could be retrieved), we focused on colorectal cancer and glioma, as their incidence is higher, angiogenesis is known to drive their progression and several independent datasets could be retrieved for CRC  [26] [27] [28] [29] [30]  and glioma  [31] [32] [33]. We addressed whether expression of the AngioMatrix signature correlates with surrogate markers of blood vessels and angiogenesis in CRC.
Langlois et al. In CRC metastasis, while no difference is observed in the lung (Supplementary Fig. S4E ), AngioMatrix expression is significantly up-regulated in liver metastasis compared to normal tissue (Fig. 5F ). The recurrent link between increased AngioMatrix expression and CRC progression prompted us to test a potential correlation with CRC patient survival. We used datasets from two independent cohorts of patients  [28, 29] , which were stratified using cut-off values into AngioMatrix low or high groups and survival analysis was performed to compare the outcome of these groups. A high expression of the AngioMatrix signature significantly correlated with a shorter relapse-free survival in the two CRC cohorts (Fig. 5 , G and H, and Supplementary Figure  S4 , F and G). We analyzed AngioMatrix expression in independent glioma datasets and observed again a significant correlation between AngioMatrix expression and the EC markers PECAM1 (Fig.
Functionally, we demonstrated that TNC ablation impairs the RIP1-Tag2 angiogenic switch, in line with our macroscopical characterization of the two islet classes  [25]. These data again support the notion that components of the AngioMatrix signature promote the RIP1-Tag2 angiogenic switch. www. impactjournals. com/oncotarget To evaluate the potential translational relevance of the AngioMatrix signature for cancer patients, we showed that AngioMatrix expression significantly correlated with EC markers in human CRC and glioma, supporting the notion that this signature also correlates with the angiogenesis status within human tumors. During CRC progression, AngioMatrix expression is increased at the adenoma-carcinoma transition, in partial agreement with previous studies showing that the angiogenic switch occurs early along the adenoma-carcinoma sequence  [41, 42].
Genome-wide gene expression profiling and data mining Pools of angiogenic and non angiogenic pancreatic islets were sorted as described  [25]  and RNA was extracted for labeling and hybridization (Affymetrix arrays). Data are deposited in the Gene Expression Omnibus (NCBI, GSE51637). Significantly deregulated genes were selected using the BRB-ArrayTools software (NCI, USA). The matrisome  [23, 24]  was used to compare the overlap with the AngioSwitch signature and define the AngioMatrix signature. Enrichments of TBRS from specific stromal cell types  [22] , the matrisome and its divisions  [23]  in the profiling dataset of RIP1-Tag2.
Breastfeeding is universally acknowledged to be the best way of providing ideal food for the healthy growth and development of infants. The World Health Organization recommends that infants should be exclusively breastfed for the first 6 months of life in order to achieve optimal growth, development, and health. After this, infants should receive nutritionally adequate and safe complementary foods while breastfeeding continues for up to 2 years of age or beyond. Exclusive breastfeeding has been shown to be the best method to prevent allergy. Data from the German Infant Nutrition Intervention (GINI) study Pediatr Gastroenterol Hepatol Nutr showed that exclusive breastfeeding had a significant protective effect against atopic dermatitis when compared with conventional cow's milk formula (CMF)  [15].