dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection and Identification of DEGs in Response to Tsetse Infection by Trypanosomes We compared 12 tsetse fly (G. p. gambiensis) transcriptome profiles to better understand their pathosystem at the transcriptome level (S3 vs. NS3; I10 vs. NI10; I20 vs. NI 20 tsetse flies). We observed significantly differentially expressed genes (Figures  5, 6 ; p < 0. 05) between S3 and NS3 flies (1373 genes), I10 and NI10 flies (52 genes), and I20 and NI20 flies (1025 genes) (Supplementary Tables  S2-S4 ). Among the DEGs identified for 3-day samples, names could be assigned to 797 contigs with reference to the T. brucei database, and to 435 contigs with reference to the insect database; 141 contigs remained "hypothetical. " Among the DEGs identified for 10-day samples, names could be assigned to 39 contigs with reference to the insect database, and 13 remained "hypothetical.
" Finally, among the DEGs identified for the 20-day samples, names could be assigned to 866 contigs with reference to the T. brucei database, and 112 contigs with reference to the insect database; 47 remained "hypothetical. " When comparing day three sampled flies that ingested a non-infected bloodmeal (NS3 flies) with flies that ingested an infected bloodmeal (S3 flies), 208 transcripts showed an upregulated expression (Fold Change > 1) in non-stimulated flies, whereas 1165 transcripts were down-regulated (Fold change < 1) (Supplementary Table  S2 ). In self-cured flies sampled 10 days after ingesting an infected bloodmeal (NI10 flies), 19 transcripts were up-regulated and 33 were down-regulated when compared to the corresponding genes of infected flies (I10 flies) (Supplementary Table  S3 ).
p. gambiensis and T. b. gambiense. These SNPs overlap genes that exhibit both up-and down-regulation of homologous transcripts from different insect and parasite species. The SNP genetic sites identified in our dataset will provide useful marker resources for fine mapping experiments and marker-assisted G. p. gambiensis control programs. These results will have immediate applications for exploring G. p. gambiensis genome diversity and co-expression networks involved in tsetse infection by trypanosomes, as well as the development of stochastic and metabolic networks. In addition, these resources can be used to identify novel genes, transcript models and eQTLs, and to study trypanosome adaptation to diverse fly tissue environments. These findings will also be useful when undertaking comparative studies with the G.
ACKNOWLEDGMENTS The authors thank the " Région Languedoc-Roussillon-Appel d'Offre Chercheur d'Avenir 2011 , " the  "Service de Coopération et d'Action Culturelle de l'Ambassade de France au Niger " and the  "Institut de Recherche pour le Développement"  for their financial support. IH is a PhD student supported by the  French Embassy  to  Niger, Service de Coopération et d'Action Culturelle (SCAC). We acknowledge the support of  Laboratoire d'Excellence (Labex) Parafrap  N •  ANR-11-LABX-0024.
Results indicate that the performance of the semi-analytical models varies depending on the product and wavelength of interest. For chlorophyll retrieval, empirical algorithms perform better than semi-analytical models, in general. The performance of these empirical models reflect either their immunity to scale errors or instrument noise in R rs data, or simply that data used for model parameterisation were not independent of NOMAD. Nonetheless, uncertainty in the classification suggest the performance of some semi-analytical algorithms at retrieving chlorophyll were comparable with the empirical algorithms. For phytoplankton absorption at 443 nm, some semi-analytical models also performed with similar accuracy to an empirical model. We discuss the potential biases, limitations and uncertainty in the approach, as well as additional qualitative considerations for algorithm selection for climate change studies.
g. detrital and inorganic matter) alters the spectral quality of the underwater light field and thus influences phytoplankton photosynthesis. The concentration of particulate material in the water is also important in coastal regions and has implications for coastal protection, shipping and recreational activities. These are some of the reasons why the systematic monitoring of ocean colour is considered a requirement for climate research by GCOS  (GCOS, 2011)  and why it is a component of the Climate Change Initiative (CCI) of the European Space Agency (ESA). The CCI programme was launched to realise the full potential of long-term, global, Earth Observation archives that ESA as well as its member states have established over the past 30-years, and to contribute to the Essential Climate Variable databases required by United Nations Framework Convention on Climate Change (UNFCCC).
A database was developed with simultaneous measurements of in situ chlorophyll and in situ measurements of remote-sensing reflectance just above the surface (R rs (λ)). Based on the results from the workshop, an empirical blue-green band ratio algorithm, labelled the Ocean-Chlorophyll-2 (OC2) algorithm, was chosen as the operational algorithm for SeaWiFS. This was later updated to the Ocean-Chlorophyll-4 (OC4) algorithm  (O'Reilly et al. , 2000). In Case-1 waters  (Morel and Prieur, 1977)  typically encountered in the open ocean, where variations in ocean-colour are driven primarily by the abundance of phytoplankton, with a co-varying influence from particulate matter and CDOM, empirical blue-green band-ratio algorithms were generally found to perform with reasonable accuracy. However, in more optically-complex waters (Case-2 waters according to  Morel and Prieur, 1977) , often encountered in coastal regions, where the concentrations of particulate matter and CDOM do not covary in a predictable manner with the abundance of phytoplankton, empirical blue-green band-ratio algorithms can give spurious results (e.
, 2013). In contrast to the aims of the NASA GIOP workshop, but making use of progress made as a result of the workshop, and building on the report of the IOCCG working group on the topic  (IOCCG, 2006) , this paper aims to establish an objective methodology for algorithm selection for climate-change studies, and then to use the method to compare and rank a variety of algorithms. Both qualitative and quantitative considerations are examined. Qualitative considerations relate to the suitability of the algorithms for use in climate change studies and the quantitative considerations relate to algorithm performance. Qualitative algorithm considerations include the ability of the algorithm to: • Create a long-term, consistent, error-characterised time series of oceancolour products for use in climate-change studies; • Generate products that best suit the requirements of the user community; • Facilitate seamless merging of Case-1 (open-ocean) and Case-2 (coastal optically-complex) waters; • Quantify a variety of properties of the marine ecosystem that are relevant to climate studies and accessible from satellite ocean-colour data and; • Be robust against potential modifications in the marine ecosystem in a changing climate.
Ideally, the most suitable algorithm would meet all these requirements and compare well in statistical tests of performance. Using a suite of statistical tests, and an in situ database of chlorophyll (C), the diffuse attenuation coefficient at 489 nm (K d (489)), IOPs and R rs (λ), we evaluate the quantitative performance of a number of empirical and semi-analytical in-water bio-optical models. The limitations of the approach are discussed and additional challenges regarding the selection of in-water algorithms for climate studies are highlighted.
Data To test in-water bio-opticals models, we made use of the publicly-available NASA bio-Optical Marine Algorithm Data set (NOMAD,  Werdell and Bailey, 2005). NOMAD Version 2. 0 ALPHA was compiled on 18 July 2008 by the NASA Ocean Biology Processing Group and source data is available online (http://seabass. gsfc. nasa. gov/seabasscgi/nomad. cgi), as is documentation related to IOPs  (Werdell, 2005). The NOMAD database provides global in situ measurements of above-water spectral water-leaving radiance (L w (λ)) and spectral surface irradiance (E s (λ)), from which remote-sensing reflectance can be computed (R rs (λ) = L w (λ)/E s (λ)), and coincident measurements of water constituents such as the chlorophyll-a concentration, IOPs and K d (489) (diffuse attenuation coefficient at 489 nm). The solar sun-zenith angle (θ) was computed for each data point using information on time and location.
The common band set used included six bands compatible with MERIS and Sea-WiFS and five bands compatible with MODIS. Co-located in situ measurements of R rs (λ) were used as input to the models, as opposed to satellite-derived R rs (λ), to minimise mis-matches in spatial scales between input and output variables. To maximise the number of b b (λ) samples, 670 nm was used where reflectance data at 665 nm were unavailable. Note that b b (λ), and the slope of b b (λ), denoted as γ (Table  1)  Remote sensing reflectance data, at various wavelengths, and solar-zenith angles were used as input to in-water algorithms to estimate IOPs, C and K d (489) (Table  1, 2  and 3 ). Estimated variables using the models were then compared with in situ values in NOMAD, to determine the performance of the algorithms.
Model C Model C refers to the ocean-colour model of  Devred et al. (2011)  with some simplifications. This model is designed to derive in-water optical properties and water constituents from spectral water-leaving radiances, using non-linear optimisation procedures. The method makes use of a three-component model of phytoplankton absorption coupled to the reflectance model of  Sathyendranath and Platt (1997). The model retrieves b bp (555) (assuming the slope of b bp = 1. 03 following  Maritorena et al. (2002) ), a dg (443) and S dg from R rs , initially assuming that a ph can be expressed as the sum of the absorption coefficient of three phytoplankton size classes (pico-, nano-and micro-phytoplankton), each with its particular specific absorption spectrum (a * ph , phytoplankton absorption normalised by chlorophyll concentration) derived from the NOMAD dataset.
Chlorophyll (C) models Chlorophyll (C) algorithms incorporated into the comparison are described in the following section. For semi-analytical Models C, G, H, I, and K, chlorophyll is an output from the models. For semi-analytical Models A, B, D, E, and F, chlorophyll is not an output. For the purposes of the comparison, we estimated chlorophyll as a function of a ph (443) using a power-law relationship (e. g. Bricaud et al. , 1995) , such that C = a ph (443) A 1 B , (1) where, A and B are positive empirical parameters. The empirical parameters A and B were computed using the in situ NOMAD database (1042 samples), and set to A = 0. 0497 and B = 0. 7575. For semi-analytical Models A, B, D, E, and F, a ph (443) was first computed, then chlorophyll was computed using Eq. (1). It is worth noting that the empirical conversion from a ph (443) to chlorophyll is merely introduced to facilitate the comparison, it is not a feature of the original algorithms.
Statistical Tests To test the performance of the in-water algorithms the following univariate statistical tests were adopted that are commonly used in comparisons between modelled and in situ data (e. g. Doney et al. , 2009; Friedrichs et al. , 2009). 4. 1. 1. Pearson correlation coefficient (r) The correlation coefficient r (also called Pearson's product moment correlation) is calculated according to r = 1 N -1 N i=1           X M i -1 N N j=1 X M j { 1 N-1 N k=1 X M k -1 N N l=1 X M l 2 } 1/2                     X E i -1 N N m=1 X E m { 1 N-1 N n=1 X E n -1 N N o=1 X E o 2 } 1/2           (8) where, X is the variable and N is the number of samples. The superscript E denotes the estimated variable (from the model) and the superscript M denotes the measured variable (from NOMAD).
Slope (S ) and Intercept (I) of a Type-2 regression The performance of a model with respect to in situ data can be tested using linear regression between the estimated variable (from the model) and the measured variable (in situ data), such that X E = X M S + I. (12) A slope (S ) close to one and an intercept (I) close to zero is an indication that the model compares well with the in situ data. Type-1 regression typically assumes the dependent variable (in situ data) is known infinitely well, when in reality the in situ data are also affected by uncertainties (e. g. problems with in situ data sampling techniques) that are difficult to quantify. Therefore, we adopted Type-2 regression  (Glover et al. , 2011 , MATLAB function lsqfitma. m), which instead of minimising the vertical distance between independent data and linear fit (as in Type-1 regression), minimises the perpendicular distance between independent data and linear fit.
Quantitative statistical methodology As with the OC-CCI comparison of atmospheric correction algorithms  (Müller et al. , Submitted) , a points scoring classification was used in the in-water comparison to rank objectively the performance of the algorithms. Each variable was tested independently in the points scoring classification. For each variable, R rs (λ) values in the database were used as input to the algorithm to estimate the variable, the estimated variable was then compared with the corresponding in situ value using each statistical test and a score was assigned for each test ranging from zero to two. These tests are described in the following sections. If the algorithm was not capable of estimating the variable, it was given zero points for that test. In addition, a chi-square test was also performed separately on a selection of the semi-analytical models.
This involved using sampling with replacement to randomly re-sample the in situ data (1000 times) creating 1000 new datasets the same size as the original dataset but not identical. The quantitative statistical methodology was then re-run for each new dataset (Monte-Carlo approach) and from the resulting distribution of scores, a mean score for each model was computed. Additionally, a 2. 5% and a 97. 5% interval on the bootstrap distribution was taken and assumed to be the error-bars or confidence limits on the mean score for each model, rather than standard deviations on the bootstrap distribution, to avoid misinterpretation of results should the bootstrap distribution not follow a normal distribution or be skewed, for instance from the presence of outliers in the data.
Chi-square test In addition to the tests described above, a chi-square (χ 2 ) test was also used to compare performance of a selection of semi-analytical models. For each semianalytical model tested, a reconstructed reflectance spectrum was produced in forward mode and compared with the in situ reflectance data. This was conducted on 1713 samples (K d (489) database) representative of a broad range of oceanic environments inclusive of the major ocean basins (see Fig. 1 ). The test is designed to examine how well each semi-analytical model performed at reproducing the observations. The results from this test are not incorporated into the points classification, as some semi-analytical models in the comparison are algebraic (e. g. Models A, B, D and E) thus their χ 2 values equal zero. However, the information is useful to evaluate the performance of those semi-analytical algorithms that are not algebraic (Models C, F, G, H, I, J and K).
2). When combining the scores of all these variables, regardless of approach (i-iv above), it is evident that Models D to J have higher scores than Models A, B, C and K. It is important to note that despite this, Models A, B, C and K do, in some cases, have higher or comparable scores to Models D to J for particular variables (Fig. 6 ). Regarding Models D to J, it is very difficult to objectively nature should be performed using a database entirely independent of any data used to parameterise the models. In the intercomparison carried out here, it has been difficult to evaluate the impact of the NOMAD dataset on algorithm performance, because most algorithms are influenced to some degree by the dataset (see Table  3 ). The limited availability of in situ observations on R rs (λ) and inwater variables, coupled with the need for a large database to implement our objective classification has meant that some data used in the comparison are not independent of those used to parameterise many of the models.
This was partly addressed using the bootstrap method which allowed for some investigation into the performance of the algorithms in the context of the range of variability in the dataset. However, the work highlights the need for an independent dataset to be developed and used to evaluate algorithms further, to ascertain the extent to which the results are influenced by this issue. Whereas NOMAD is the most extensive global database of in situ R rs (λ) and in-water variables (IOPs, C and K d (489)), the distribution of measurements in NOMAD is not equivalent to the distribution in the global ocean. Eutrophic waters are over-represented in NOMAD and oligotrophic waters under-represented  (Werdell and Bailey, 2005). Ideally, when comparing global bio-optical algorithms, a dataset should be used that corresponds approximately to the distribution of measurements in the global ocean, highlighting the need for continued on-going in situ campaigns that focus on the areas of the ocean that are under-represented in in situ databases, such as the oligotrophic gyres.
The performance of the algorithms tested may differ when used with data containing higher levels of noise. The tolerance of the bio-optical models to errors in R rs will need to be evaluated further to reflect realistic satellite measurement conditions. This could be done using simulated datasets (e. g. Lee et al. , 2010)  or satellite and in situ match-ups (e. g. Mélin et al. , 2005; Bailey and Werdell, 2006; Maritorena et al. , 2010). A global database of satellite and in situ match-ups would also allow for a thorough investigation into the suitability of coupling different in-water bio-optical models with atmospheric correction models. For example, atmospheric-correction models that focus on estimating the spectral-shape of R rs accurately, with low bias, maybe better suited to bandratio in-water models.
Summary An objective classification has been presented designed to rank the quantitative performance of a suite of bio-optical models based on a variety of univariate statistics. Eleven semi-analytical models, as well as five empirical chlorophyll al-    # Wavelengths used that are available in the comparison. $ Qualitative assessment of algorithm independence to NOMAD: 1 = NOMAD dataset has a small influence on model parameterisation; 2 = NOMAD dataset has some influence on model parameterisation; 3 = NOMAD dataset has a large influence on model parameterisation. * CI refers to a colour index defined as the difference between R rs in the green region of the visible spectrum and a reference formed linearly between R rs in the blue and red region of the visible spectrum. Table  4 : Performance of semi-analytical models in the objective classification.
The GSM model retrieves simultaneous estimates of chlorophyll (C), a dg (443) and b bp (443) from R rs (λ), assuming an underlying bio-optical model and using non-linear optimisation. Global parameters of the bio-optical model were initially assigned based on simulated annealing on a global quasi-real dataset, which are then used in the non-linear optimisation routine. These include a fixed chlorophyll-specific phytoplankton absorption coefficient (a * ph ), S dg and the slope of b bp. The chlorophyll (C), a dg (443) and b bp (443) are first retrieved by fitting the bio-optical model to the observed R rs (λ). IOPs at any wavelengths are then obtained using C, a dg (443) and b bp (443).
Figures 6 and 10 show results of the quantitative intercomparison on b b (λ). Results indicate that it is difficult to separate the performance of the semianalytical models at determining b b (λ), as indexed by large error bars on the mean score of the bootstrap distribution. These larger error bars are in part a consequence of a lower number of in situ samples in the b b (λ) dataset, as compared with the other IOPs. Models A and B display a positive bias (Fig. 10), indicating.
gorithms and an empirical diffuse attenuation coefficient algorithm, were ranked for some 29 variables using the NASA NOMAD dataset. Uncertainty in the using the General Search and Cruise Search utilities to facilitate communication, collaboration, and acknowledgement. NASA should be commended for the development of the NOMAD database and for on-going in situ activities. This work is a contribution to the Ocean Colour Climate Change Initiative of the European Space Agency and was supported by the UK National Centre for Earth Observation.
5. 6. 6 Figures 6 and 10 show results of the quantitative intercomparison on b b (λ). Results indicate that it is difficult to separate the performance of the semianalytical models at determining b b (λ), as indexed by large error bars on the mean score of the bootstrap distribution. These larger error bars are in part a consequence of a lower number of in situ samples in the b b (λ) dataset, as compared with the other IOPs. Models A and B display a positive bias (Fig. 10), indicating.
, 2012a; Ekici et al. , 2014; Paquin and Sushama, 2014). This paper demonstrates the impact of adding new permafrost-related processes into JULES (Joint UK Land Environment Simulator;  Best et al. , 2011; Clark et al. , 2011) , the land-surface scheme used in the UK Earth system model (UKESM). Although the scarcity and uncertainty of global data on permafrost limit the detail with which it can be represented in a large-scale model like JULES, it is possible to capture the broad spatial patterns of permafrost and active layer thickness (ALT) and to realistically simulate presentday conditions. Chadburn et al. (2015)  describe in detail the relevant developments within JULES. These include the effects of organic matter, moss, a deeper soil column and a modification to the snow scheme. Chadburn et al.
Standard model description JULES is the stand-alone version of the land-surface scheme in the Hadley Centre climate models  (Best et al. , 2011; Clark et al. , 2011)  and was originally based on the Met Office Surface Exchange Scheme (MOSES)  (Cox et al. , 1999; Essery et al. , 2003). It combines a complex energy and water balance model with a dynamic vegetation model. JULES is a community model and is publically available from http://www. jchmr. org/jules. The work discussed here uses a JULES version 3. 4. 1 augmented with improved physical processes. JULES represents the physical, biophysical and biochemical processes that control the exchange of radiation, heat, water and carbon between the land surface and the atmosphere. It can be applied at a point or over a grid and requires temporally continuous atmospheric forcing data at frequencies of 3 h or greater.
Global organic matter distribution The organic fractions were calculated from a combination of the Northern Circumpolar Soil Carbon Database (NCSCD)  (Hugelius et al. , 2013)  where available and the Harmonized World Soil Database (HWSD) (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012) for the rest of the land surface. These databases include some rather limited information about the vertical distribution of soil carbon. Using this, an approximate vertical profile of soil carbon was prescribed for each grid cell (see Eq. 1) and the soil properties calculated accordingly for each model level. Organic carbon quantities can be obtained from both the NCSCD and HWSD data sets for the top 30 cm (C 30 ) and top 1 m (C 100 ). The profile of soil carbon was assumed to be a constant plus an exponential term. The total for the top 1 m adds up to the observed amount.