dataesr/scientific-paragraphs-categorization huggingface dataset:
At the macroscale, dynamical databases for rats and primates such as BAMS (bams1. org), neurocircuitry for modeling drug effects  [12] , the connectome of basal ganglia  [13] [14] [15] , and CoCoMax (cocomac. g-node. org, [16] ) currently provide valuable resources for the analysis of brain connectivity and the modeling of brain dynamics. Yet, the lack of integration of neurochemical data, the low density of these networks (e. g. , BAMS2 database with a maximum coverage of 11. 2%), as well as the separation of meso-(i. e. , intraregional connections) and macroconnectomes (i. e. , neuronal projections between distinct brain regions) limit the applicability of these databases. Thus, a cohesive and comprehensive understanding of the rat neurochemical connectome is still missing. ChemNetDB aims to address this knowledge gap by integrating neuroanatomical track tracing and neurochemical data from 36,464 rats into a terminologically accurate database of the multiscale, neurochemical connectome of the rat brain.
Data selection process and overall estimates Automatic keyword-based search (1,750 keywords) and manual grey search on electronic databases revealed 124,694 abstracts, titles, or both identified as original publications. Out of these, 4,517 studies were relevant for data mining and data from 1,560 original research articles with 36,464 rats were selected for the connectome identification based on the inclusion criteria. A flow diagram of the study selection process is represented in supplementary information (S1 Fig). According to our stringent inclusion and exclusion criteria, these selected studies represent the relevant published outcome of 55 years of neuroanatomical research. All selected studies were performed in outbred rats with no specific genotype or phenotype. Furthermore, animals did not receive any pharmacological treatment or behavioral training.
Brain-wide connectivity database In total, the systematic literature search identified 281 interconnected nuclei (4,585 axonal projections) of cerebral regions, cerebellum, and components of medulla oblongata. The connectivity matrices associated with this raw database are presented in the supplementary information (S7, S8 and S9 Tables) along with Bregma levels from rostral to caudal. The nomenclature-consistent, brain-wide (cerebral) core of the chemical connectivity database (ChemNetDB) comprises by 188 cortical and subcortical morphologically distinct regions with 3,712 connections. The database references are provided within S1 Text. This database considerably differs from the existing macroscopic connectomes  [17, 18]  of the rat central nervous system. In contrast to ChemNetDB, which is a multiscale connectome database, these databases are often restricted to a single spatial scale.
Furthermore, with the exception of a few brain regions such as amygdala, the bed nucleus of stria terminalis (BNST), and cerebral cortex, they are only sparsely connected, and do not provide edge-complete connectivity. Databases lacking the edge-completeness property include brain regions that are either not connected to any other region, or receive only afferents from other regions, or send only efferents to other brain areas. None of these cases are biologically or physically feasible within the central nervous system. Thus, these databases effectively present cerebral subnetworks of the full connectome. In addition, concerns regarding nomenclature and data integration (such as combining data from neonatal and adult animals) and other inconsistencies make these platforms much more difficult to use.
A multiscale connectome Divisive hierarchical algorithms with edge-completeness constraints further reduced the dimension of the network and integrated the extracted cerebral core into a multilayer cerebral neurochemical connectome of the rat brain. Neurochemical connections within and between brain regions were mapped into a 3-dimensional space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and cell-type-specific projections. The connectome consists of 125 scale-consistent cerebral nuclei and 2,931 multiscale, multichemical connections (Fig  1a ). Thereby, 632 bidirectional, 1,642 unidirectional connections, and 25 loops utilize 25 different neurochemical compounds, such as amino acids, monoamines, cannabinoids,-opioids, and several other neurotransmitters.
Modeling the rat connectome with an EDR network model Consistent, cortex-wide retrograde tracer injections in several species (macaque  [20, 33] ), mouse and microcebus  [19]  have shown that the distribution of the lengths of white matter (WM) axons follow an exponential decay, called exponential distance rule (EDR), with a species-dependent decay rate (λ). Naturally, 2 questions arise: Is the rat large-scale connectome also described well by the EDR network model? And (2) Does the agreement with the EDR model break with increasing resolution? The second question could not be answered for the other species, as there is no (nearly) edge-complete database available at higher resolutions for them, and thus ChemNetDB is especially valuable in this regard. By using ChemNetDB, we next attempt to answer both questions by modeling the connectome with the EDR network model at the G 19×19 and G 125×125 levels, respectively.
Discussion Most studies in neurobiology rely on a precise understanding of the neuronal connectivity and its neurochemical actors. Yet, investigators commonly face massive data that require enormous resources to be processed for their demands. By utilizing advanced neuroinformatics, our study resolves this problem and integrates over 50 years of neuroanatomy research on rat brains into a consistent multiscale, multilayer neurochemical cerebral connectome. Supervised machine learning was applied to resolve nomenclature issues resulting in an extensive standardized database, which combines the state-of-the-art knowledge of connectomics and neurochemistry. Establishing the neurochemical connectivity database (ChemNetDB) is a novel approach towards topological mapping of the brain that takes the neuroconnectomics well beyond the binary constructs and paves the way for more advanced and accurate investigations of healthy and disease states of rat brains.
We have investigated, as examples of analytic studies that could be done on the database, several network measures and their relationships with similar analyses in macaque and the mouse. Analysis of the structural properties of the resulting network reflect and confirm the key functional roles of deep subcortical brain areas such as lateral hypothalamus, BNST, and DRN. These regions are known to be responsible for primitive yet essential and evolutionary conserved functions, such as regulation of sleep and circadian rhythms, reward, anxiety, aggression, and fear. The importance of these brain areas for survival is also associated with an early formation in the developmental stages as reflected in the connectome by their extremal values of various graph connectivity measures such as b. c. and/or node degree.
However, the actual biological interpretation of driver nodes is not yet clear and the role of the identified brain regions in controlling brain activity requires further investigations and experimental validations. Note that the outcomes of the example network analyses presented here might change with the addition of new data; however, the main observations, such as the strong core-periphery structure and network measures expressed as fractions (including density, the fraction of 3-motifs, etc. ), are expected to be robust against changes due to the high density of the networks. ChemNetDB provides the first, whole-brain, large-scale and consistently collated rat connectome database that also includes neurochemical specificity. This will provide researchers with a tool to gain insights into the fundamental relationships between connectome architecture, information processing, and brain function, with potential for advancing preclinical research and clinical applications such as those related to substance abuse and depression.
Standardized data mining and database creation Search strategy. The online portal of the National Library of Medicine (http://www. ncbi. nlm. nih. gov/pubmed/) including PubMed, PubMed Central, and MEDLINE was used as the platform for literature research. A systematic screening of the original research articles published until February 2015 was performed based on 1,750 keywords (see S2 Text). In addition, the reference sections of identified papers as well as review and meta-analysis articles were screened for further relevant citations. For hippocampal formation, the search strategy was further extended to include the hippocampome  [10]. Study selection. Reviewers, in pairs, independently screened titles and abstracts of articles and reviewed the full text of any title or abstract deemed potentially eligible by either reviewer.
Reviewers resolved disagreements by discussion. Among these studies, only peer-reviewed original research articles in English language were chosen for data mining if they provided consistent information on chemoarchitecture, cytoarchitecture, cerebral intra-or interregional connectivities, mRNA expression levels, and/or receptor distribution densities. Thereby, only studies conducted on rats not associated with any disease-model, geno-or phenotype were included, which used anterograde tracing with radioactive amino acids, and autoradiographic fiber tracing methods, as well as anterograde and retrograde tracing with horseradish peroxidase, in situ hybridization, immunohistochemistry or immunocytochemistry as applied technique. Tracking tracing and immunohistochemical studies in animals other than rats or in cell cultures were excluded.
Functional connectivity as measured by magnetic resonance imaging or electrical stimulation was excluded as the correspondence between anatomical projections and functional connectivity is still an open question. Optogenetics is most likely the right compromise. This modern technique, however, lacks standardized experimental and reporting protocols and has only rarely been used to investigate the rat neuroanatomy under basal conditions. Thus, there is an insufficient number of studies to conduct a robust systematic review of the literature and therefore observations based on this valuable technique are not included in the present version of the database. Abstracts and unpublished studies were not included. Authors were contacted if critical information was missing or only partially provided in their articles.
In order to avoid artificial bias in data mining due to differences in nomenclature, the reviewers did not use any a priori parceling or data filtering techniques. If a research article fulfilled the inclusion criteria, then the 4 categories of variables, including the definition of the brain area as provided by the authors, were extracted and implemented into a standardized template. Category I-Variables concerning cytoarchitecture and morphology: types of neurons; percentage share of different neurons; number of neurons; given nomenclature of brain region components. Category II-Variables concerning topology: efferent connections; afferent connections. Category III-Variables concerning neurotransmitter and receptors: local receptor distribution density; mRNA expression levels; neurotransmitter systems.
Category IV-Variables concerning utilized methods and animals: utilized tracer-technique; tracing markers; strain, age, weight, gender, and number of rats. Quality assessment. Two factors may significantly influence the quality of the dataset: (1) differences in the nomenclature and parceling schemes and (2) the accuracy, reliability, and completeness of pathway tracing data. Neuroinformatics tools provide appropriate strategy to investigate inferences due to different parceling schemes and/or insufficient data. Here, we used divisive hierarchical algorithms with constraints for the preprocessing of the multivariate datasets to reduce the dimension of the network and compare the structural differences with respect to different parceling schemes. Edge-completeness of the network was the defining constraint.
It excludes regions from clustering which only receive or only send neural projections. An exception to this rule is given if a network node belongs to a well-defined functional unit. The anterolateral nucleus of thalamus represents the only case that the exception was made. There were 93 nomenclature inconsistencies. Here, we used a supervised machine learning technique to identify synonymous brain areas with respect to cytoarchitecture. Thereby, a given morphology is associated with one well-defined equivalence class for brain regions of different denotations. These equivalence classes were used as the input data for the structural preprocessing of the database. For algorithmic realization, the "LPBOOST" and "RUSBoost" (combined with "AdaBoostM2") ensemble learning method "fitensemble" in MATLAB were used, which allow for the melding of results from many weak learners into one high-quality ensemble predictor, particularly appropriate for classification of skewed data (many more observations of one class).
If data on the existence of the pathways were not complete, then the related brain regions and connectivity patterns were treated as temporarily unreliable for network analysis and were excluded from the connectome. Nonetheless, all data are preserved within the database and each efferent and afferent matrix contains a section "other regions" that includes this additional connectivity data. This section not only contains the data from different parceling schemes but also data on cerebellum, nerve fibers, and components of medulla. Furthermore, it includes data on connectivity of cerebral areas for which not enough information is available at the current stage (auditory cortex, Barrington nucleus, caudal temporal cortex, central linear nucleus of raphe, [anterior and posterior] cingulate cortex, claustrum, cuneiform nucleus, dorsal peduncular cortex, dorsal/lateral and dorsolateral tegmental nucleus, ectorhinal cortex, Edinger Westphal Nucleus, endopiriform cortex, Forel fields, frontal cortex, frontal polar cortex, frontal motor cortex, gustatory cortex, infraradiata cortex, interpeduncular nucleus, lateral terminal nucleus of the accessory optic system, lateral/medial/ventral orbital cortex, lateral/ vental occipital cortex, lateral precentral cortex, median eminence, mesencephalic reticular nucleus, nucleus ambiguus, motor trigeminal nucleus, nucleus basalis of meynert, nucleus incertus, nucleus of Darkschetwisch/Cajal, olfactory tubercle, orbitofrontal cortex, parasubthalamic nucleus, parastrial nucleus, parietal cortex, pedunculopontine nucleus, periamygdaloid cortex, periaqueductal gray, piriform cortex, pretectal area, primary motor cortex, primary somatosensory cortex, primary visual cortex, red nucleus, reticular formation, retrosplenial cortex, secondary somatosensory cortex, sensorimotor cortex, somatic motor cortex, subfornical organ, superior colliculus, temporal cortex, trigeminal motor nucleus, visual cortex, zona incerta).
This procedure ensures the high quality of the network in a quantitative hypothesisfree manner. Integration of data into a network structure. The remaining brain regions and their neurochemical efferents and afferents were compiled into a cerebral neurochemical connectome of the rat brain including 125 brain areas. Micro-, meso-and macroscale connections within and between brain regions were mapped into a 3-dimensional space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and celltype-specific projections. Weights of connections only by means of the transmitters they utilized. The length of each connection was calculated as the Euclidian distance between the geometric centers of the brain regions as defined by Paxinos atlas  [35]. Recursive coarsening and network partitioning.
4) We repeat (1) through (3) until we reach the same binary connectivity density as in the data network. This results in a directed, spatially embedded and weighted random network, an EDR network. By using this procedure, we create many realizations of these EDR networks and then we average the graph measures over these realizations (ensemble average). Given a graph measure, or property P, we compute its ensemble average as function of the decay rate λ and we set the value of λ such that the ensemble average of P is as close as possible to its value in the connectome. We then record this λ P value obtained by graph property matching. If there is a direct way of measuring λ, e. g. , from retrograde tracing, then its value can be compared with λ P. If the 2 are close, then the EDR is a strong determinant of that property in the connectome.
AA T in the model and data. (e) The average local clustering coefficient in the undirected version of the graph. Dashed lines in (a) and (e) indicate values measured on the dataset. (f,g) Comparison of 3-motif counts between the dataset, EDR model with λ = 0. 6, and CDR model (λ = 0). (g) Log-ratios between motif-counts. (h) Number of cliques with different sizes in the data and then the EDR and CDR network models. (i) The eigenspectrum of the co-occurrence matrix AA T. https://doi. org/10. 1371/journal. pbio. 2002612. g006.
89±0. 20 PgC over Eurasia. Basu et al. , 2014 , studied seasonal variations of CO 2 fluxes during 2009-2011 over Tropical Asia using GOSAT, CONTRAIL and IASI data. They found an enhanced source for 2010 and concluded that this is likely due to the biosphere response to aboveaverage temperatures in 2010 and unlikely due to biomass burning emissions. Parazoo et al. , 2013, used  GOSAT XCO 2 and solar induced chlorophyll fluorescence (SIF) retrievals to better understand the carbon balance of southern Amazonia. Ross et al. , 2013, used  GOSAT data to obtain information on wildfire CH 4 :CO 2 emission ratios. For flux inversions not only the retrieved greenhouse gas values are relevant but also their error statistics, in particular the reported uncertainties. Chevallier and O'Dell, 2013, analyzed  this aspect in the context of CO 2 flux inversions using GOSAT XCO 2 retrievals.
Detmers et al. , 2015, analyzed  GOSAT XCO 2 to detect and quantify anomalously large climate-related carbon uptake in Australia during the time period end of 2010 to early 2012. Furthermore, a number of publications focused on improving retrieval algorithms including data processing and comparisons with ground-based observations and global models (e. g. Heymann et al. , 2012a Heymann et al. , , 2012b) )  or on applying existing algorithms to other sensors (e. g. ,  Heymann et al. , 2015). Satellite XCO 2 retrievals are also used, for example by the European Centre for Medium-Range Weather Forecasts (ECMWF), to characterize atmospheric CO 2 at large and synoptic scales and for CO 2 forecasting  (Massart et al. , 2016). Last but not least and despite the fact that none of the existing satellite missions has been optimized to obtain information on anthropogenic CO 2 emissions (in contrast to other proposed future missions, in particular CarbonSat  (Bovensmann et al.
For Europe their results imply that Europe is a much stronger carbon sink than current knowledge suggests. Chevallier et al. , 2014a , used an ensemble of inversion methods and GOSAT XCO 2 retrievals to also derive regional (sub-continental) CO 2 surface fluxes. They also found a significantly larger European carbon sink. They conclude that the derived sink is unrealistically large and they argue that this may be due to modelling issues related to long-range transport modelling and/or biases of the satellite retrievals. In particular they argue that errors of the satellite data outside of Europe may adversely influence the European results. To further investigate this European carbon sink issue in detail,  Reuter et al. , 2014a, used  an ensemble of SCIAMACHY and GOSAT XCO 2 data products and an inversion method which is not, or at least significantly less, sensitive to the potential issues discussed in  Chevallier et al.
, 2016). They report a European sink strength of 0. 75±0. 63 GtC/year for 2008-2009, which temporally overlaps with the range reported by  Reuter et al. , 2014a , and is significantly larger compared to their reference inversions without these new Siberian observations. On the other hand, based on simultaneous CO 2 and CH 4 flux inversions using GOSAT-retrieved ratios of total column CH 4 and CO 2 for 2009 and 2010,  Pandey et al. , 2016, obtain  European terrestrial CO 2 fluxes close to zero, in contrast to the results discussed above. Apparently, more research is needed to answer this important European carbon sink question with confidence. For satellite XCH 4 retrievals and the interpretation of these data sets the situation is similar as for XCO 2. SCIAMACHY data have already been extensively used to improve our knowledge on atmospheric methane and regional methane emissions prior to the start of the GHG-CCI project (e.
, 2014; Wecht et al. , 2014). The SCIAMACHY XCH 4 retrievals have also been used to compare with and to improve chemistry-climate models  (Shindell et al. , 2014 , Hayman et al. , 2014). Despite this quite large number of publications it is clear that still much more has to be learned about the various (and changing) sources and sinks of CO 2 and CH 4. It is obvious that the more accurate and precise the observations are and the longer and denser the observational time series is, the larger their information content. Within the GHG-CCI project a continuous algorithm improvement, re-processing and data product analysis cycle is carried out every year with the goal to deliver each year an improved data set of satellite-derived atmospheric CO 2 and CH 4 information. The latest data set is called CRDP3.
Overview data set CRDP3 The GHG-CCI latest data set called Climate Research Data Package No. 3 (CRDP3) consists of several satellite-derived atmospheric CO 2 and CH 4 data products. These data products are classified as (i) GHG-CCI project core products, generated with so-called ECV Core Algorithms (ECAs), and (ii) additional products, generated with so-called Additional Constraints Algorithms (ACAs). The ECA products are XCO 2 and XCH 4 (see Tabs. 1 and 2) retrieved from satellite nadir mode radiance spectra in the near-infrared / shortwave-infrared (NIR/SWIR) spectral region using appropriate retrieval algorithms. These retrieval algorithms are all based on modelling the observed radiance spectra using a radiative transfer model and corresponding parameters (e. g. , vertical profiles of atmospheric CO 2 , CH 4 , temperature, aerosols, etc.
GHG-CCI ECV Currently, the corresponding GHG-CCI ECA products are derived from SCIAMACHY/ENVISAT and TANSO/GOSAT. In this publication we focus on the GHG-CCI CRDP3 ECA products. An overview on the additional GHG-CCI ACA products is given in  Buchwitz et al. , 2015, and details are  given on the GHG-CCI website (http://www. esa-ghg-cci. org/), in particular in the corresponding ACA product tables as given on the GHG-CCI main data products website (http://www. esa-ghgcci. org/sites/default/files/documents/public/documents/GHG-CCI_DATA. html). In short, ACA products are not (or typically not) sensitive to near-surface GHG variations but to variations in upper atmospheric layers, i. e. , layers above the planetary boundary layer. They therefore provide complementary additional information (compared to ECAs) on atmospheric CO 2 and CH 4.