dataesr/scientific-paragraphs-categorization huggingface dataset:
Only sequences that were complete and aligned along at least 75% of the length of the query were selected. All trypanosomatid genes characterized in this study have been submitted to NCBI's GenBank; accession numbers are available in Table  S2. All endosymbiont genes analyzed here have been previously sequenced  [24] ; gene identifiers are available in Table  S3. For comparison, we used in our analyses the genome annotations of trypanosomatids (Trypanosoma brucei, T. cruzi, Leishmania major, L. infantum, L. donovani, L. mexicana, L. braziliensis)   [39]. However, care should be taken since these data may lack manual curation. As concerns information on metabolic pathways, we used KEGG  [39]  and MetaCyc  [44].
Results and Discussion We analyzed the genomes of five species of SHTs and of their TPEs for the presence/absence of genes from the metabolic pathways for essential vitamin synthesis. The genomes of two RTs, C. acanthocephali and H. muscarum, were examined in detail, however these data do not fully represent the genomic diversity of insect trypanosomatids in general. Indeed, the enormous diversity present in the Trypanosomatidae family is sometimes not fully appreciated, leading to apparent conflicts in the interpretation of metabolic data, as happened with the early studies on the nutrition of Crithidia species. Data on the nutritional requirements of C. fasciculata strongly disagreed with those obtained for C. oncopelti (recently renamed as Strigomonas oncopelti) as concerns the necessity for amino acids and vitamins which is quite different for both organisms  [5, 6].
Acknowledgments We would like to thank  Marta Campaner  and  Carmen C. Takata (USP) ,  Vladimir Lee  and  Yingping Wang  ( VCU ) for technical support, and  Carlisle G. Childress Jr. and  J. Michael Davis  ( VCU Center for High-Performance Computing ). Sequencing was performed in the Nucleic Acids Research Facilities, and analyses were performed in the  Bioinformatics Computational Core Lab  and the  Center for High Performance Computing at VCU.
The research leading to these results was funded by: the  European Research Council  under the European Community's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement nu[247073]10; the  French  project  ANR MIRI BLAN08-1335497 ;  FAPERJ grant  coordinated by  Dr. Cristina Motta  and the  FAPERJ-INRIA  project  RAMPA ; by the  Laboratoire International Associe(LIA)  LIRIO co-coordinated by  Ana Tereza R. de Vasconcelos  ( Labinfo, LNCC, Brazil ) and  Marie-France Sagot  (LBBE,  UCBL-CNRS-INRIA, France ); the  National Science Foundation [USA , grant number  NSF   DEB-0830056  to Gregory Buck]; and  Conselho Nacional de Desenvolvimento Cientı ´fico e Tecnologico (CNPq, Brazil)  and  Coordenaca ˜o de Aperfeicoamento de Pessoal de Nı ´vel Superior (CAPES, Brazil)  to  Cristina Motta ,  Marta M.
2005). The morphometric variability among the diverse populations was established by statistical treatment of data through one-way ANOVA (Tukey, P<0. 05). The homogeneity of variances was tested using the Leven's test  (Norušis 1993). Biometrical analysis of these data was performed via multivariate discriminant analysis using the statistical package SPSS for Windows, version 10. 0  (Norušis 2000).
The type strain of B. pilosicoli seems most similar to the previously sequenced porcine strain B. pilosicoli 95/ 1000 (GenBank accession no. CP002025) from Australia (13), with 98. 7% average BLAST nucleotide identity and over 93% of the genome that could be aligned to the 95/1000 genome sequence. The two strains show a high level of synteny and share approximately 1,995 orthologous genes. The majority of the genes that are found only in B. pilosicoli P43/6/78 T but not in 95/1000 are hypothetical genes, but some, such as a beta-galactosidase gene and a gene cluster putatively involved in O-antigen variation, may have important phenotypic implications. Nucleotide sequence accession number. The complete genome sequence of B. pilosicoli strain P43/6/78 T has been deposited in GenBank under the accession number CP002873.
, 1978; Hanawa and Yoritaka, 1987; Hallock and Teague, 1992; Thadathil et al. , 2002; Reseghetti et al. , 2007; Reverdin et al. , 2009; Kizu et al. , 2011; Cowley et al. , 2013]. [37] Comparison of XBT data with quasi-colocated and quasi-simultaneous reference data. In this method, the comparison is made between XBT and CTD/bottle measurements obtained from a large-scale, historical database such as the World Ocean Database (WOD)  [Boyer et al. , 2009]. These pairs are not strictly colocated and simultaneous but within a specific spatial and temporal distance (e. g. , within 1°and 1 month). In this case, the sample size available for the bias calculation is larger than if only relying on data from sideby-side studies. This compensates for the additional uncertainty arising from using data that are less closely colocated in time and position.
[41] Since then, several efforts have been made to develop XBT bias correction schemes for the global XBT data set by comparing XBT data with quasi-colocated and quasisimultaneous reference data  [Ishii and Kimoto, 2009; Cowley et al. , 2013; Levitus et al. , 2009; Gouretski and Reseghetti, 2010; Hamon et al. , 2011; Wijffels et al. , 2008]  and with global bathymetric data  [Good, 2011; Gouretski, 2012]. 3. 1. 3. Biases in the XBT Data and Their Causes 3. 1. 3. 1. Depth Bias [42] The cause of this bias is the inadequacy of the FRE in describing the actual probe depth, which depends on numerous factors including the physical characteristics of the water column, launching conditions, and the physical characteristics of the probes. The initial fall rate of the XBT (the a coefficient in the FRE) depends on launching conditions (launching height, waves, ship motion, etc.
Thermal Bias [47] The thermal bias in XBT data originates from the thermistor, wire, and data acquisition systems. The existence of this error is documented in some side-by-side studies [e. g. ,  Reseghetti et al. , 2007; Reverdin et al. , 2009]. For instance, XBTs from 23 French cruises were found to exhibit a warm bias  [Reverdin et al. , 2009]. There is a hint of temperature dependence in this bias (Figure  5 ), as also indicated by probe calibrations in the laboratory  [Gouretski and Reseghetti, 2010]. [48] Like the depth bias, the thermal bias seems to be time variable. It has predominantly positive values (i. e. , the XBT profile is too warm). One study  [Gouretski and Reseghetti, 2010]  identified a time-varying thermal bias between approximately À0. 05 and 0. 15°C, peaking in the mid-1970s for the T4/T6 types of XBTs.
Bias Correction Schemes for the Global XBT Data Set [57] XBT data are a major component of the global subsurface temperature database from the 1970s through the 1990s. There is, therefore, a lot of interest in the oceanographic community in the development of bias correction schemes in order to improve the utility of XBT data for climate applications. [58] Several correction schemes (Figure  9 ) have been proposed using different statistical methods. Wijffels et al. [2008]  contributed two sets of corrections, both of which attempt to remove bias by applying time-varying multiplicative factors to the measurement depths (effectively assuming that there are no pure temperature or depth offset adjustments). The first set (seen in  Wijffels et al. [2008, Table 1] ) spans 1968-2005, with separate corrections for "deep" (maximum depth > 550 m) and "shallow" XBTs.
This categorization scheme separates out the most common types of XBT without needing to rely on the metadata that are missing for many XBTs. The second set of corrections (seen in  Wijffels et al. [2008, Table 2] ) spans 1993-2006 (not shown in Figure  9  owing to the short time span). The method relies on the use of satellite altimetry data to indirectly relate high-quality data and XBTs, which explains the short coverage period. Separate corrections are derived for a number of specific types of XBTs, including those of unknown type separated into the depth categories described above. Ishii and Kimoto [2009]  derived corrections that are also applied to the depths only and are proportional to the length of time the XBT had been falling through the ocean when each measurement was taken. Corrections cover 1966-2006 for a variety of different types and manufacturers of XBTs.
Summary [62] During the 40+ years of studies on XBT biases, some basic features of the XBT biases were detected as presented above. These can be broadly divided into pure thermal and depth biases. The former results from problems in the thermistor, wire, and data acquisition systems, while the latter occurs due to the inadequacies in the FRE to realistically describe the motion of the XBT. The relative importance of each depends on the local temperature gradient, with pure temperature bias more prominent where the temperature gradient is low. Many correction schemes have been proposed to adjust for these biases. [63] However, many issues are still left unanswered, and uncertainties in the correction schemes persist. In the future, it will be vitally important to (1) understand and qualify each error source of the XBT bias and to determine the role each played in XBT bias history, (2) determine how best to correct the bias in global XBT data set, and (3) quantify the uncertainty in the bias adjustment schemes so that uncertainties in time series generated from the data can be properly assessed.
[64] Addressing these points requires continued investigation into XBT biases and should be facilitated by international collaborations, for example, through continuing to hold workshops where the XBT bias issues are discussed (see summary at http://www. nodc. noaa. gov/OC5/XBT_BIAS/xbt_bias. html) and through a proposed project to improve the historical ocean data record. This will allow the XBT data to be exploited fully by the climate and oceanographic communities.
Additionally, the FRE method is simple to incorporate into standard data processing procedures. [67] For the dynamic modeling technique, it is possible to incorporate changes to the drop conditions that are not reflected in the experiments during which the FRE was obtained. For instance, variations in probe mass, drop height, water temperature, or the linear density of the wire can be included in the analysis. When XBT devices are released into tropical waters or into waters that exhibit a temperature profile that differs substantially from the waters of the calibrating experiments, it is possible that a bias is incurred. That bias is related to the fact that water viscosity depends on temperature. Similarly, when drops are made from heights that differ from the recommended standard (2. 5 m), there can be an impact on the probe depth.
, 2011]. Finally, the new method allows the user to quantify the impact of various parameters, including probe mass, drop height, and linear mass density of the wire, on depth. Since the new method makes use of the temperature measurements of the probe, variations of water viscosity are naturally included in the model. Probe mass and drop height may have a significant impact on probe depths; however, a recent set of experiments suggest that the effect of drop height might be overpredicted  [Abraham et al. , 2012a]. Surface effects (such as sudden impact forces at entry, angle of impact with the ocean surface, ship motion, entrainment of air, etc. ) may negate the larger impact velocities. [71] Even though the dynamical model currently ignores these surface effects, the application of this technique to the XBT profiles stored in the world database could improve the accuracy of the estimations of the ocean heat content or provide support for experiments.
[77] 3. Obtain long time series of temperature profiles at approximately repeated locations in order to unambiguously separate temporal from spatial variability. [78] 4. Determine the space-time statistics of variability of the temperature and geostrophic shear fields, recognizing that the late of synoptic salinity profiles introduces uncertainty in the shear-temperature relationship. [79] 5. Provide appropriate in situ data (together with Argo profiling floats, tropical moorings, air-sea flux measurements, sea level, etc. ) for testing ocean and ocean-atmosphere models. [80] 6. Determine the synergy between XBT transects, satellite altimetry, Argo, and general circulation models. [81] 7. Identify permanent boundary currents and fronts and describe their persistence and recurrence and their relation to large-scale transports.
Future of the XBT Network [83] The XBT network reflects the recommendations of OceanObs99 and OceanObs09  [Goni et al. , 2010]  and includes several transects that the scientific community has added during the last 12 years (Figure  11 ). Some transects may be difficult to occupy continuously due to logistical and budgetary constraints; however, they are kept as recommendations based on the justifications given by OceanObs99, Global XBT Network, OceanObs09 Recommendations 40°E 80°E 120°E 160°E 160°W 120°W 80°W 40°W 0°8 0°S 40°S 0°4 0°N 80°N Figure  11. The current XBT network containing OceanObs99 and OceanObs09 recommendations. supported by their scientific value. Ship recruitment is an ongoing issue in implementing the XBT network, resulting in gaps or shifts in sections. Sampling histories and data along individual transects are made available through http://wwwhrx.
ucsd. edu and http://www. aoml. noaa. gov/phod/hdenxbt. [84] Thirteen years after OceanObs99, the XBT HD transects continue to increase in value, not only through the growing length of decadal time series, but also due to integrative relationships with other elements of the ocean observing system, including the following: [85] 1. The implementation of global broad-scale temperature and salinity profiling by the Argo Program underlines a need for complementary high-resolution data in boundary currents, frontal regions, and mesoscale eddies. HD transects together with Argo float data provide views of the large-scale ocean interior and small-scale features near the boundary, as well as of the relationship of the interior circulation to the boundary-to-boundary transport integrals. [86] 2. Almost 20 years of continuous global satellite altimetric sea surface heights are matched by contemporaneous HD sampling on many transects.
ACCURACY/BIASES OF ARGO FLOATS [88] The Argo Program, an array of over 3000 autonomous floats designed to return materially important oceanic climate data, is a vitally important component of the present oceanic Earth observing system and a strong complement to satellite observations. This program, designed to complement the Jason altimeter missions, provides observed climate signals that, when globally averaged, are sensitive to the presence of data bias. Thus, the Argo Program expends much effort to minimize the likelihood of measurement bias and spatial and temporal sampling bias. [89] The Argo Program has advanced the breadth, quality, and distribution of oceanographic data as compared to the broad-scale XBT network while continuing to supplement ship-based CTD programs (section 2). The autonomous profiling float introduced a high-quality CTD, reducing measurement uncertainty into the design of a broad-scale, subsurface data collection network.
Liberated from the presence of research vessels once deployed, the floats' continuous oceanic measurement over a 3-5 year lifetime greatly reduced the temporal and spatial sampling bias of the historical hydrographic data set (Figure  1 ). Nowhere is the improved temporal bias more apparent than during the winter month of August south of 30°S in the Southern Ocean  [Roemmich and Gilson, 2009]. [90] By design, the Argo array is composed of multiple autonomous float models and manufacturers, ideally utilizing different sensor models, provided by over 30 national Argo programs. The array is currently dominated by three float families, Autonomous Profiling Explorer (APEX; comprising 68%), Sounding Oceanographic Lagrangian Observer (SOLO; 23%), and PROVOR (8%) (PROVOR represents Profiler Sea in French), although the recent introduction of several new float types and additional manufacturers may realign future percentages.
The multiplicity within the array reduces the likelihood that a single failure vector or bias would render the array valueless for climate studies. However, in practice, near homogeneity in smaller regions does occur. [91] Consistent data processing over temporal and spatial dimensions and among different float providers is a high priority. Argo data are telemetered via satellite and made publicly available from the Argo Global Data Assembly Centers (GDAC) within 24 h of acquisition. The immediate distribution of data results in a two-tiered quality control system, each with distinct expectations of bias within the data. The initial release of data has undergone a series of automated checks termed "real-time quality control" (RTQC) which is performed at 1 of the 11 Argo regional Data Assembly Centers (DAC)  [Wong et al.
, 2012]. The RTQC tests are coarse. A more careful analysis termed "delayedmode quality control" (DMQC) is performed by the float provider 6-18 months after data acquisition  [Wong et al. , 2012]. The reason for the delay is to allow corrections to be made in light of the temporal behavior of the (particularly) salinity sensors. In general, data bias is identified and addressed in DMQC, unless a correction can be applied with minimal subjectivity in RTQC. This makes the DMQC data most suitable for climate-related studies. [92] What follows is a history of temperature and pressure bias identified within the Argo Program autonomous float array. Also, a presentation of past examples of float logic that led to data bias being injected into the data set will be given. These biases have already been or are presently being addressed by the Argo Program either through data adjustment or labeling.
[94] Two models of SBE CTD designed for energy-limited autonomous operation are commonly installed on Argo floats: the SBE-41 and the SBE-41CP. The former is designed to minimize energy usage by turning off the sensor pump between sparse profile measurements (spot sampling). The SBE-41CP samples continuously (1 Hz) and can return profile data averaged over pressure intervals. The SBE-41CP can also be used in spot sampling mode. The SBE-41 has historically been installed in more Argo floats. However, numbers of SBE-41CPs are increasing along with the numbers of floats using the bidirectional, higherbandwidth Iridium transmission system that can support collection of higher vertical resolution profiles 4. 2. Sensor Drift: The Causes, Identification, and Correction [95] Sensor drift is a continuous concern for instruments that are designed to obtain extended duration measurements in the climate system.
, 2010; Lyman et al. , 2010]. For example,  Ishii and Kimoto [2009]  incorporate gridded sea surface temperature (COBE-SST based on  Ishii et al. [2005] ) within the mixed layer in addition to in situ data. Levitus et al. [2012]  include in situ data from all available instrument types, whereas  Domingues et al. [2008]  only use in situ data from bottles, CTDs, XBTs, and Argo floats. Historical and Argo databases are regularly updated, so both quality and types, and amount of data used in the OHC analyses depend on the database and its version. In addition, before using the databases, research groups often apply their own quality control procedures. Consequently, the amount of apparently erroneous (delayed or real time) data removed also varies. To increase the number of available profiles for the OHC 0-700 m analyses, shallower temperature profiles are sometimes extrapolated to 700 m [e.
Despite the greatly improved open ocean coverage for the upper 700 m by the Argo array since 2005, a wide spread in interannual rates for 2005-2012 remains. Further systematic comparisons may also help to understand differences in estimates over this relatively short and well-sampled time interval. [141] Present observational estimation approaches and instrumental bias corrections are perhaps two significant sources For the latter, two standard errors are about ±2 × 10 22 J in the 1980s, decreasing to ±1 × 10 22 J in the early 1990s, but increasing in the late 1990s then decreasing substantially to about ±0. 5 × 10 22 J in the Argo era. The reference period is  1955-2006. of uncertainty in OHC estimates, and both are unlikely to be perfect. Future refinements in methodological issues and in the observational database can be made to narrow the spread in the warming rates and to strengthen the key conclusion of increasing heat storage in the upper ocean since the late twentieth century.
Improved Observational OHC Estimates and Analysis Methodologies [169] A high-quality subsurface ocean temperature database along with accurate and comprehensive metadata is an important prerequisite for advancing knowledge on instrumental biases (e. g. , XBT/MBT) and devising more accurate corrections to help further reduce uncertainties in OHC estimates. Although much has been done to retrieve original data and metadata and to improve quality control procedures, more work is required to realize the potential of the historical subsurface ocean observing system (originally designed and funded with a focus on short-term forecasting) to advance our understanding of climate change and develop improved prediction systems in order to underpin 21st century decision and policy-making assessments  [Wijffels et al.
, 2010; Levitus et al. , 2012]. Improvements to the quality of the historical ocean temperature database and metadata information for climate research purposes are currently being planned through a global coordinated effort (http://www. clivar. org/ organization/gsop/activities/clivar-gsop-coordinated-qualitycontrol-global-subsurface-ocean-climate). [170] Another large source of uncertainty in OHC estimates is the use of varying methodological approaches (e. g. , mapping practices and climatological references) by different research groups. To better understand and quantify the structural uncertainties arising from methods used in publications, a comprehensive project is underway  [Boyer et al. , 2013]. In this project, a series of systematic intercomparisons is being carried out for a number of sensitivity tests based on different parameter choices but using agreed temperature databases (e.
( GA01101 ). V. G. was supported through the  Cluster of Excellence "CLISAP"  ( EXC177 ),  University of Hamburg , funded through the  German Science Foundation. T. B. , J. M. L. , and G. C. J. were supported by the  NOAA Climate Program Office and NOAA Research. A. P. was supported by the  Inter-American Institute for Global Change Research  through the  US National Science Foundation  grant  GEO-0452325. K. E. T. and J. T. F. were sponsored by  NASA  under grant  NNX09AH89G. F. R. was supported by  EC   FP7  project  MyOcean2  and operationally supported in part by  NOAA/AOML. [175] The Editor on this paper was  Eelco Rohling. He  thanks three anonymous reviewers for their review assistance on this manuscript.
Second, regulatory feed-back loops and dosage compensation mechanisms may attenuate the dosage effect of many genes  [25] , causing them missed by correlation analysis between copy number and expression or differential expression analysis between CNA groups. Based on these observations, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. We propose a gene-wise dosage effect score to measure the prevalence of concordant copy number and expression changes across the samples in a dataset. Applied to two myeloma datasets, a genome-wide karyotype plot of the dosage effect reveals the variation of dosage effect between genes and across chromosomes.