dataesr/scientific-paragraphs-categorization huggingface dataset:
Specific, targeted, and selective were each analyzed. Targeted and specific are lower-frequency terms that are used in a variety of contexts, not limited to gout and uric acid. Comparatively, selective occurs more frequently and appears in a pharmacologic context, its most common collocation being selective inhibitor. Success language. Despite a multimillion-word database, success terminology in gout was relatively absent. The terms control, serum uric acid target, sUA target, serum urate target, and treat to target were each evaluated. Control occurs the most frequently, appearing 9,202 times in the data set. However, it is more strongly associated with hyperuricemia than with gout. The phrase control of hyperuricemia occurs 116 times in the data set, whereas uncontrolled gout (n 5 4), controlled gout (n 5 2), control of gout (n 5 9), and gout control (n 5 3) occur very rarely.
The genus Laticauda (Reptilia: Elapidae), commonly known as sea kraits, comprises eight species of marine amphibious snakes distributed along the shores of the Western Pacific Ocean and the Eastern Indian Ocean. We review the information available on the geographic range of sea kraits and analyze their distribution patterns. Generally, we found that south and south-west of Japan, Philippines Archipelago, parts of Indonesia, and Vanuatu have the highest diversity of sea krait species. Further, we compiled the information available on sea kraits' occurrences from a variety of sources, including museum records, field surveys, and the scientific literature. The final database comprises 694 occurrence records, with L. colubrina having the highest number of records and L. schistorhyncha the lowest.
Occurrence records A database of sea krait occurrences was created using a combination of data extracted from online repositories (GBIF, HerpNet, iOBIS), from published scientific literature, and from field surveys. Most of the occurrences came from the marine environment, as data on terrestrial localities are scarce for this group. Because of philopatry, sea kraits generally avoid venturing very far from the shore line  (Lane and Shine 2011a). Occurrences without spatial data were manually georeferenced to the finest scale possible using the information provided by the source and Google Earth 7. Country taxa lists or locations that could not be georeferenced due to lack of detailed locality descriptions (e. g. , name of islands or provinces within countries;  McCarty 1986; David and Ineich 1999)  had to be excluded from our dataset, as the descriptions were too general.
Results The final database was comprised of 694 unique records of occurrence at a spatial resolution of 9 km (Suppl. material 1). The bulk of these records belonged to the yellow-banded sea krait (L. colubrina, 64. 55% of all records compiled), while the lowest number of occurrences was for L. schistorhynchus (0. 86% of all records) (Table  1 ). The EOO registered very high values for L. colubrina as a result of its wide distribution range, while L. schistorhynchus had the smallest EOO, with only 180. 99km 2 (Table  2 ). The AOO was also the smallest for L. schistorhynchus, while the largest AOOs were for L. colubrina and L. laticaudata (Table  3 ). The optimized hot spot analysis based on the whole MGRS grid identified the bulk of the range (western shores of Myanmar and Thailand, Indonesia, Malaysia, Philippines, Papua New Guinea, Solomon Islands, Vanuatu, and Fiji) as an area of high spatial clustering, with 99% confidence (z ¯ = 9.
schistorhyncha, endemic to the Island of Niue (Figure  8 ; Suppl. material 1)  (Heatwole and Cogger 2013) , and L. semifasciata, with a relatively wide but fragmented distribution range, southern Japan being its northernmost limit. Although the southern limit for the species was previously considered to be the Maluku Islands  (Heatwole and Cogger 2013) , our database indicates an extension of the known range, with new records from southern Indonesia (Figure  9 ; Suppl. material 1). With the exception of one species, all sea kraits presented a terrestrial EOO < 25%. The notable exception is L. guineai for which more than 80% of the EOO comprises terrestrial surfaces. In a recent attempt to assess the conservation status of the world's marine elapids,  Elfes et al. (2013)  have also calculated values for the extent of occurrence of several Laticauda species.
Acknowledgements We would like to express our appreciation to  Harvey Lillywhite ,  Harold Heatwole  and  Arne Rasmussen  for their useful comments that helped us improve the manuscript. The publication of this paper was funded by the  "Alexandru Ioan Cuza" University of Iași , through the project  06/3. 12. 2015 , code:  GI-2015-03 , Grant Competition for Young Researchers of UAIC, to AS. FB was funded by the  CNRS.
Data processing and sample selection The data processing and sample selection are fully detailed in  Pacaud et al. (2016, hereafter Paper II) , and is briefly summarised here. The XXL Survey contains 542 XMM pointings covering 50. 8 deg 2. After light-curve filtering (following  Pratt & Arnaud 2002) , rejection of bad pointings and exclusion of pointings with high background levels, the total XXL area spans 46. 6 deg 2 (from 454 pointings). Images, exposure maps, and detector masks were generated and processed using the Xamin pipeline  (Pacaud et al. 2006; Clerc et al. 2012). SExtractor was then run to generate a conservative source catalogue and source masks, followed by a dedicated XMM maximum likelihood fitting procedure to determine likelihood ratios to assess the detection and source extent probabilities.
3) for the bias correction. The M WL -T relation is presented in  Lieu et al. (2016, hereafter Paper IV) , based upon weak lensing cluster masses and the temperatures presented in this work. Briefly, the M WL -T relation was determined using 37 XXL-N clusters that fell within the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS) footprint, utilising the CFHTLenS shear catalogue  (Heymans et al. 2012; Erben et al. 2013)  for the mass measurements. In order to increase the statistics and the mass range covered, the XXL-100-GC is combined with 10 groups from the COSMOS survey  (Kettula et al. 2013) , and 50 massive clusters from the Canadian Cluster Comparison Project (CCCP,  Hoekstra et al. 2015). The combined M WL -T relation is fitted with a power-law of the form (assuming self-similar evolution) log 10 (M WL E(z)) = A MT + B MT log 10 (T ) (1) using the Bayesian code linmix_err  (Kelly 2007).
Sample LT Relation Figure  2  (left panel) shows the L XXL -T relation for the XXL-100-GC. For simplicity, the L XXL -T notation explicity refers to the L XXL 500,MT -T 300kpc relation. A fit to the data using a power law of the form L XXL L 0 = E(z) γ LT A LT T T 0 B LT (2) was performed, where A LT , B LT , and γ LT represent the normalisation, slope, and power of the evolution correction respectively. We note that the two clusters at low luminosity offset from the L XXL -T are the clusters XLSSC 011 and 527. These clusters are low S/N clusters at low redshift, 0. 054 and 0. 076, such that the 300 kpc region is large on the detector. The temperature measurement is likely affected by these factors. However, we still include them in the fit to the L XXL -T relation. The power law was fit to the data using the BCES orthogonal regression in base ten log space  (Akritas & Bershady 1996)   ) in C14.
Likelihood The observational data used in this study is the distribution of the XXL-100-GC clusters in L, T , and z. Our physical model assumes that the cluster population is described by a powerlaw correlation between L and T with log-normal scatter in L and evolution in L by E(z) γ LT. The XXL-100-GC data represent a subset of this population selected according to our selection function and with noisy measurements of L and T , denoted L and T. We neglect any measurement errors on z. The number density of clusters in the survey volume is taken into account by using a mass function, assumed to be described by a Tinker mass function  (Tinker et al. 2008). We then transform this to a temperature function, dn/dT , using the M WL -T relation given in Sect. 3. In the present analysis we neglect the intrinsic scatter in the M WL -T relation, and the measurement errors on the parameters of the scaling relation, and assume that its evolution is self-similar (i.
63±0. 15 and a bolometric slope of B LT =3. 08±0. 15. 3. After taking into account the selection effects, our data show that the L XXL -T relation prefers an evolution of the form E(z) 1. 64±0. 77. This is consistent with the expected "strong" self-similar evolution; however, is marginally stronger than the "weak" self-similar expectation. 4. Comparisons to clusters detected in the XMM-LSS (a precursor to XXL), which favour a negatively evolving LT relation, can be explained by the different assumptions made for the local baseline LT relation, the assumed mass-temperature relation, and the value of the core radius. 5. Investigating the impact of the assumed mass-temperature relation and the x 500 parameter, we find that they do not have a significant impact on the L XXL -T relation when using the XXL likelihood model.
Acknowledgements. XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼ 5×10 -15 erg s -1 cm -2 in the [0. 5-2. 0]keV band for point-like sources. The XXL website is http://irfu. cea. fr/xxl. Multiwavelength information and spectroscopic follow-up of the X-ray sources are obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com/. PAG, BJM, ML, GPS, TP and JD acknowledge support from the  UK Science and Technology Facilities Council. GPS acknowledges support from the  Royal Society. FP acknowledges support from the  BMBF/DLR  grant  50 OR 1117 , the  DFG  grant  RE 1462-6  and the  DFG Transregio Programme TR33.
249 g) were added, and the reaction was left to stir for 24 h. To wash out all the byproducts and unreacted monomer (APMA), the solution was poured into excess water, which led to the precipitation of a black solid. The black solid was collected by centrifugation, washed repeatedly with water, and dried under vacuum to give f-PEDOT in powder form. 1 H NMR. f-PEDOT (5. 0 mg) was dissolved in DMSO-d 6 (768. 25 mg), and its NMR spectroscopy was recorded as described above. The degree of substitution of APMA was quantified using an external reference (trioxane (10. 5 mg) in DMSO-d 6 (771. 65 mg)) and using Topspin Eretic Software. Elemental Analysis. Elemental analysis was performed at Campbell Microanalytical Laboratory, University of Otago, New Zealand. Samples were precisely weighed on a Mettler UMT2 microbalance into lightweight tin capsules and dropped into a combustion tube of the elemental analyzer (Carlo Erba EA 1108) through which a constant stream of helium is maintained.
The eight additional parameters determine the deviation from average for each physiological parameter. These parameters modify the basis functions in CIEPO06 to model individual observers. This parameterization is convenient to keep the average observer model intact. When the eight additional parameters are set to zero, the model becomes exactly same as CIEPO06. No attempt was made to modify CIEPO06 functions and improve the average observer model since it was out of scope of this study. The model output is lms-CMFs (also known as cone fundamentals), the same output as in CIEPO06. The computational procedure is equivalent to CIEPO06 except for additions of individual variability. More detailed explanations are available in the CIE publication  [2]. Note that a Matlab code for CIEPO06 is available at Munsell Color Science Laboratory's website  [13].
However, the function adopted in CIEPO06 was kept untouched at this time. The individual spectral optical density of the lens and other ocular media, D ocul (λ), is obtained by Eq (4). D ocul ðλÞ ¼ D ocul;ave ðλÞ 1 þ d lens 100 ð4Þ The spectral optical density of the macular pigment, D macula (λ), is obtained by Eqs (  5 ) and  (6). D macula ðλÞ ¼ D max;macula D relative;macula ðλÞ ð5Þ D max;macula ¼ 0:485e Àv=6:132 1 þ d macula 100 ð6Þ where D max,macula is the peak optical density of the macular pigment, and D relative,macula (λ) is the relative spectral optical density of the macular pigment. The data for D relative,macula (λ) are tabulated in Table  6. 4 in  [2]. Eq  (6)  without the inter-observer variation component was derived by Moreland and Alexander  [17]  and adopted by CIEPO06.
When λ max shift is applied in Eq  (8) , spline interpolation can be used to retrieve the average low optical density spectral absorbance at a wavelength location finer than 5nm interval. To summarize, the variability in the lens pigment is taken into account in Eq (4), the variability in the peak optical density of the macular pigment is taken into account in Eq  (6) , the variabilities in the peak optical densities of the photopigments are taken into account in Eq  (9) , and λ max shifts of photopigments are taken into account in Eq  (8). This procedure can compute the cone fundamentals for any individual observer with ten input parameters. Matlab code for the proposed model as well as an interactive demo are available at Munsell Color Science Laboratory's website  [19].
Derivation of Physiological Parameter Deviations Probability distributions are required for eight physiological parameters to perform Monte Carlo simulation. It was assumed that each physiological parameter formed a normal distribution around the mean. In addition to the model simplification, this would be a practical approach as many studies reported standard deviations. The assumption would be reasonable since a population study showed normal distributions  [20]. The standard deviations (SDs) of eight physiological parameters were determined at two steps. Note that Webster and MacLeod worked on identifying individual differences of physiological parameters by performing a factor analysis on Stiles and Burch's individual observer data  [21]. The approach in this study was different from their study in that we derived variations from numerous past studies for each physiological parameter.
g. , experimental setting, number of subjects, number of repetitions, instrument quality, etc), and determining a weight to each dataset would be arbitrary. For lens density, it is necessary to define an SD for a given age since age ranges significantly impact SDs. For this reason, the following steps were required. For each study, lens densities and ages of subjects were collected. Then, (1) an age center was set, (2) lens densities of subjects whose ages were ± five years from the age center were extracted, and (3) An SD for a given age center was taken from these lens densities. These steps were repeated until all the available age centers were selected. An SD for a given study was obtained taking an average of all the SDs at available age centers. For macular pigment, there are abundant studies.
Given that the average peak optical densities of L-and M-cone photopigments are the same in CIEPO06, it would be reasonable to treat variations in optical densities of L-and M-cone photopigments in a similar manner. For λ max shifts of L-, M-, and S-cone photopigments, the available data were extremely scarce as shown in Table  4. The study by Merbs and Nathans used genetically reconstituted pigments to measure responses  [27]. There are many studies reporting variations of λ max shifts related to genetic sequences. Although very insightful, they were not used to derive model parameters. The reasoning is explained in Complementary Insights section. Step 2 SDs obtained at step 1 were scaled to fit the variability of a target color matching dataset. The step 2 was necessary because otherwise, the model tended to overestimate variations according to the preliminary simulation.
Validation of Physiological Parameter Deviations The derived standard deviations were validated using three different color matching datasets. In all the datasets, SDs measured (or obtained) in an experiment were compared with SDs predicted by the proposed model. lms-CMFs were generated through Monte Carlo simulation using an age distribution and a field size of a target study as input. The process in the proposed model was that CIEPO06 sets the baseline CMFs using the supplied age and field size information and the eight additional physiological parameters randomly deviated the baseline CMFs. The number of CMF sets depended on each validation dataset. Then, color matching was simulated, and standard deviations were taken. This process was repeated 100 times and average SDs were taken to increase the accuracy of predicted SDs.
The other observer missed data at one wavelength that was linearly interpolated. A list of ages for 49 observers ranging from 16 to 55, and a field size of 10°were used for the model inputs. To compare the variations in the same unit, lms-CMFs were linearly transformed to rgb-CMFs. For each set of lms-CMFs, a 3 × 3 matrix was obtained such that the corresponding rgb-CMFs were normalized at three primaries' wavelengths (444. 4, 526. 3, 645. 2 nm). The measured and predicted SDs (in an absolute unit) were taken at 35 wavelengths for each RGB primary, then averaged. The second dataset was five color matches by Asano. [52]. This dataset was the same color matches as used in the derivation step 2 but included a different set of 76 observers. There were no overlapping observers between the datasets used in the derivation and the validation.
A list of ages for 76 observers ranging from 20 to 69, and a field size of 8. 5°were used for the model inputs. Color matches were simulated and the CIELAB values were predicted for each set of the CMFs from the Monte Carlo simulation. The simulation procedure for a given set of CMFs is described in Color Matching Simulation Procedure. The measured and predicted SDs [CIE-LAB unit] were obtained by averaging SDs for five color matches for both a Ã b Ã values. The third dataset was Rayleigh match employed in Oculus HMC anomaloscope by Rüfer et al. including 113 observers  [79]. For the model inputs, a list of ages was estimated from Table  1  in  [79]. Ages were ranging from 14. 9 to 65. 4. A field size was 2°. The reported peak wavelengths of red (700 nm), green (550 nm), and yellow (589 nm) primaries were used for color matching simulation.
The measured and predicted SDs were compared in Rayleigh match unit. Overall, the predictions are close to experimental data for all the validation datasets. The estimated SDs were different from experimental data between -5 to +22%. A two-sample Ftest, was performed on the Stiles and Burch's dataset and the five color matches' dataset to test if the variances of two samples are equal. The test was performed with 95% confidence level and two-tailed. F-test results did not support statistical similarities of model predictions to the experimental data perfectly. This could be because there was experimental noise present (predicted SDs were noise-free), and factors other than age (e. g. , ethnic origins, gender, genetics, etc. ), which are not incorporated in the proposed model, might affect color vision variations.
Nevertheless, the test results infer that at least for some variables, there are statistical similarities between the model predictions and experimental data. It should be pointed out that, regarding the five color matches' dataset, given that the average intra-observer variability of five color matches was 1. 4 (computed from Table  3. 5 in  [52] ), the difference between measured and predicted SDs (1. 42 CIELAB unit) in Table  7  would be perceptually small. In other words, this mathematical observer model is very useful in a practical sense even if it is not yet capable of predicting each element of observed variability perfectly in a statistical sense. To.
The variances of model input parameters were derived with two steps; standard deviations of eight physiological parameters were obtained from past studies in the first step; the obtained standard deviations were scaled down to fit color matching data in the second step. The final standard deviations are the current best estimates of inter-observer variability. The variances of model input parameters were validated using three different datasets: color matching data with 49 observers  [1] , five color matches with 76 observers  [52] , and Rayleigh matches with 113 observers  [79]. The two-sample F-test partially supported the derived standard deviations. Moreover, the model prediction would be perceptually similar to actual color matching results considering noise due to intra-observer variability.
For the Stiles and Burch's dataset, one sample was color matching responses at 35 wavelengths for the Stiles and Burch's 49 observers, and the other sample was the simulated color matching responses at 35 wavelengths for 1000 CMFs generated by Monte Carlo simulation. The test was performed for each of the 105 variables (35 wavelengths x 3 primaries). The results showed the variances were significantly different for 72 variables and were not significantly different for 33 variables. For the five color matches' dataset, one sample was the adjusted a Ã and b Ã values of five color matches for 76 observers, and the other sample was the simulated a Ã and b Ã values of five color matches for 1000 CMFs generated by Monte Carlo simulation. The test was performed for each of the 10 variables (2 values x 5 matches).
The authors declare no competing financial interest. ■ ACKNOWLEDGMENTS This work has been supported by  Universitéde Lorraine and French CNRS. M. M. is grateful to the  French National Research Agency (ANR)  for a postdoctoral contract in the framework of the  QMC=Chem  project. D. R. -S. thanks the  Spanish MINECO/FEDER  ( CTQ2014-58624-P project ,  JCI-2012-13431   Juan de la Cierva fellowship, and Mari ́a de Maetzu program / MDM-2015-0538 ) and the  Generalitat Valenciana  ( GV2015-057 ) for financial support. S. M. and L. G. acknowledge the  Austrian Science Fund (FWF)  for financial support through Project  P25827  as well as the  COST action   CM1405  (MOLIM).
Morphogenesis of the leg Within two days from the onset of cell proliferation, the amputated limb stump is transformed into an elongated leg bearing the series of podomeres typical of Parhyale legs (see leg outlines in Figure  6 ). Our live recordings and cell tracking provide the first opportunity to investigate cell behaviours during this morphogenetic process. We focused on leg elongation. During the proliferative and morphogenetic phase of leg regeneration (phase 3, Figure  3 ), the length:width ratio of the proliferating epidermis increases by more than 2-fold. Individual cell elongation does not appear to be the basis for leg elongation (data not shown), but oriented cell division and/or cell rearrangements could be involved. To address this point, we directly measured the orientation of all epidermal cell divisions in recording #04.
The coverslip bearing the animal was placed into the lid of a 50 ml Falcon tube where a large circular window had been cut out, fixed by screwing the top end of the Falcon tube and sealed with vaseline (Figure  1B ). The resulting chamber was filled with ~3 ml of artificial seawater (specific gravity 1. 018). Imaging was performed on a cLSM Zeiss 780 inverted confocal microscope using a 20x objective (EC Plan-Neofluar) at ambient temperature (20-24˚C). Z-stacks spanning the entire depth of the leg were captured at 2-5 mm steps, every 10-45 min with an image resolution of 1024 Â 1024 pixels (see Table  1 ). The images shown in Figures 1A, 4A''-A and Videos 1 and 2 were captured on a Zeiss Axio Zoom V16 microscope. The images shown in Figure  4B -B'' and Video 3 were captured on a Leica M205FA stereoscope; the still images were processed using the HeliconFocus software to combine images taken at different focal planes.