dataesr/scientific-paragraphs-categorization huggingface dataset:
Gibbs sampling is performed as usual to access such complicated joint f (•) making use of its marginals: f (� z m |Z (-m) , � x, � θ) where Z (-m) is Z lacking the m-th column relative to the mass peak assignment � z m. These probabily distributions, by Bayes rule, are proportional to: (-m) , � x (-m) , � θ). L(•) and p(•) represent the likelihood and prior functions, respectively, and � x (-m) means � x lacking x m. L(x m |Z, � x (-m) , � θ) • p(� z m |Z As pioneered by  (Rogers et al. , 2009) , we still use a straightforward Dirichlet-categorical prior p(•) on the chemical reaction graph to enhance the odds of more connected compounds over less connected ones. However, we integrate this model to real biochemical networks/pathways databases such as KEGG or BioCyc  (Altman et al. , 2013)  in a sensible way in order to retain biologically meaningful priors.
Although this integration is made by web-services provided by public databases, ProbMetab allows in-house (possibly curated or specific) pathway databases to be used to inform the prior component of this Bayesian model. In a subsequent review,  (Rogers et al. , 2010)  briefly suggested how their previous method could be extended to incorporate additional experimental information and metadata since the original formulation considered only a measurement noise model. We followed these leads expanding the likelihood function L(•) in multiplicative independent terms to account for additional orthogonal information sources: L(x m |Z, � x (-m) , � θ) ∝ L N (x m |Z, � θ N ) • L rt (x m |Z, � θ rt ) • L iso (x m |Z, � θ iso ) (1) where L N (•) is the measurement noise model; L rt (•) is the retention time error model and; L iso (•) is the isotope profile error model.
� θ ⋆ 's are their respective appropriate parameters subset of � θ. We implemented computationally this improved likelihood function in ProbMetab with modularity in mind. This is important because depending on particular dataset, equipment setup, experimental conditions and so on, a given multiplicative term in L(•) can be dropped, or a new one included. For example, there are isotope profile error models proposed for Orbitrap (Thermo Fisher) mass spectrometry platform that can take advantage of known natural relative abundance of carbons 12 C and 13 C. Taking into account measured peak intensities would improve the odds of a given compound over others if it fulfills an expected ratio  (Weber et al. , 2011). Additionally, one may include a retention time error model to take advantage of known differences in liquid chromatography elution time of chemically distinct compounds with similar mass.
, 2009). In order to overcome this limitation, we replaced L N (•) following the ideas put forward by  (Böcker et al. , 2009)  when modeling isotope and noise error simultaneously but, here, considering only the noise part: L N (x m |Z, � y, w) ∝ 1 ω � 1 -Φ � 1 σ ω (|x m /y c -1| -µ ω ) �� (2) where 1 ω is the indicator function for the set ω composed by all j candidate compounds in the database for which |x m -y j | ≤ w; µ ω and σ ω are the average and standard deviation of all masses in � y listed in ω; Φ is the normal cumulative distribution function; and w is a researcher-defined mass difference limit. The set ω, defined by the threshold w, is an improvement that inhibits the unwanted effect of letting highly connected compounds (hubs in the metabolic networks) overinfluence the posterior when using the originally proposed Dirichlet-categorical prior.
Results and Conclusion The platform chosen for implementation of these ideas was the well-known and established R programming environment (R Development Core Team, 2010), which incorporates a wide range of analyzes including successful tools that perform preprocessing of spectral data required for metabolite annotation (Fig. 1 )  (Smith et al. , 2006; Kuhl et al. , 2011). Figure  1 : Schematic representation of ProbMetab package scope in spectral data analysis, showing the integration in a programming environment that provides different solutions for analysis and information integration associated with the model. The main product of a probabilistic annotation is a list of compound candidates ranked by their probabilities (Fig. 2 ). In order to easily navigate over ProbMetab's results, we display tabular and dynamic network outputs along with supporting information, which assists practitioners to ultimately decide on most parsimonious annotations instead of forcing them to simplistically rely on the top probability assignment.
All mass peaks are viewed as graph's nodes. Edges between two nodes are drawn if any candidate compound assigned to the outgoing node can be metabolized to any candidate compound assigned to the incoming node by means of a known biochemical reaction (Fig. 3 ). ProbMetab is capable of producing reaction graphs and export them as standard Cytoscape input files or broadcasting the necessary graph data and attributes (color, shapes, etc) directly to Cytoscape Desktop  (Shannon et al. , 2013). This information can be easily overlaid with other widely used systems biology strategies such as correlation or partial correlation networks. If a mass spectra time-series or biological replicates are available, ProbMetab uses third party packages integrated downstream to export correlation or partial correlation graphs, along with their intersection/difference with the reaction graph.
Alternatively, a biologist can visualize ProbMetab's results in a simplified searchable web interface. Our package has a function that is responsible to consume an online web-service, which checks and renders the broadcast results as a web-page. The visualization approach was developed taking advantage of the cytoscape. js library  (Lopes et al. , 2010)  and its dependencies and can be easily integrated or embedded into any html5 web application. ProbMetab's documentation brings two detailed case studies in which all its features are explored. Moreover, to highlight integration with downstream and upstream third party R packages, data analysis examples mentioned are carried out from raw data, following through preprocessing until it reaches Prob-Metab's specific point of action. We used publicly available data from Trypanosoma brucei, causative agent of sleeping sickness, and an original dataset from Sacarum oficinarum (sugarcane), an important biofuel source, to illustrate several points in typical metabolomics analysis sections.
The T brucei dataset, obtained from the mzMatch. R project website, was chosen because it presents a set of metabolites identified with the aid of internal control standard compounds, being specially suited for performance evaluation. With this validation dataset we compare the MetSamp (http://www. dcs. gla. ac. uk/inference/metsamp/) implementation from  (Rogers et al. , 2009)  with ProbMetab's implementation and show that, the efficient R/c++ integrated function  (Eddelbuettel and François, 2011)  had a 3 fold time improvement over the MATLAB implementation. For both implementations the higher probability candidate was the true identity in up to 60% of the metabolites. However, instead of reporting only the higher probability candidate identity as proposed by  (Rogers et al. , 2009)  we show that exporting the complete ranking in summarized visualizations, up to 90% of metabolites identities are among the top-three higher probabilities.
The full or filtered ranking allow the experimenter to associate the candidates with additional information present in this outputs and attribute the correct identity. The sugarcane dataset was chosen to exemplify differential expression of annotated metabolites in contrasting environmental perturbation. We successfully recovered changes in a known stress response pathway (flavone and flavonol biosynthesis), showing the importance of a network-centric visualization for metabolite annotation in order to track metabolism changes. The benchmark dataset confirms, as preconceived by  (Rogers et al. , 2010) , that a probabilistic model using orthogonal data and metadata yields better automatic mass peak annotation. The perturbation dataset shows that probabilistic annotation can produce otherwise impossible interpretation for differential network connectivity.
Acknowledgement We thank  Bruno M Della Vecchia  ( USP ),  Dr Oscar Yanes 's team ( Rovira i Virgili University ) and  Paul Shannon  ( Fred Hutchinson Cancer Research Center ) for critical discussions. We thank  EBI/EMBO 's courses for catalyzing cooperation among coauthors. Funding Microsoft Research and  FAPESP  grant  2009/53161-6. FAPESP fellowships (2010/14926-4 ,  2012/05392-1, 2010/15417-6 ). We thank the  Programa de Pos-graduação em Genética FMRP-USP  for CAPES travel grants and initial funding awarded to RRS.
, 2013; Ward et al. , 2009; Zemlak et al. , 2009) ; although in some cases it has been shown to need additional DNA data  (Dasmahapatra et al. , 2010; Taylor and Harris, 2012). In 2014, Kowasupat et al. reconstructed a phylogenetic tree for the bubble-nesting betta fishes in Thailand by using mitochondrial COI gene and nuclear ITS1 (internal transcribed spacer 1) gene. The ITS1 gene, albeit showing only small differences among species, is still useful in identifying species of the bubble-nesting bettas and is also capable of yielding genetic markers. The combined COI and ITS1 phylogenetic tree shows the relationship of six clearly distinguishable species, viz B. splendens, B. mahachaiensis, B. siamorientalis, B. imbellis, B. smaragdina, and B. stiktos. Close morphological similarities between B.
Sequence submission Fighting fish sequence data were submitted to GenBank (www. ncbi. nlm. nih. gov). Of the total 707 COI sequences, 306 were from  Kowasupat et al. , 2014, 249  of the bubble-nest builders were from Sriwattanarothai et al. , 2010, plus 152 new COI sequences of the mouth brooders with accession numbers KM485311 to KM485462. The percentage of base-pair differences from the total of 652 bp in the COI sequence was used for intra-specific and inter-specific sequence comparison. Of the total 192 ITS1 sequences, 102 were from  Kowasupat et al. (2014). All the new 90 ITS1 sequences in this study have accession numbers KM485463 to KM485552.
Phylogenetic analysis In addition to the COI and ITS1 datasets, a combined dataset was created by joining the two DNA regions of the same specimen. All repeating sequences in the each dataset were eliminated so that the sequence of each specimen was unique. Phylogenetic analyses were performed on the three datasets using MrBayes version 3. 2. 2 (Huelsenbeck and Ronquist, 2001; Ronquist and Huelsenbeck, 2003; Ronquist et al. , 2012). First, preliminary Bayesian inference was carried out for each of the dataset using the most extensive single-nucleotide model for that dataset. Metropolis-coupled Markov chain Monte Carlo (MC 3 ) was used to sample from the posterior distribution in order to observe the characteristics of the run using Tracer version 1. 5  (Rambaut and Drummond, 2003). The posterior distribution was used to decide which evolutionary models, and in which order, should be inspected for their marginal likelihoods so that their fitness for the given dataset could be compared using Bayes factors.
36 and 0. 79 respectively in the COI tree and the specimens of B. pallida could not even form a clade in the combined tree (Figs. 2, 3 ). Moreover, the COI sequences of the two differed by only 5. 87 bp Fig. 2. The phylogenetic tree reconstructed from the alignment of 83 COI and ITS1 sequences using Bayesian inference with Metropoliscoupled Markov chain Monte Carlo (MC 3 ): the posterior probabilities are indicated next to the nodes. The dataset was divided into 4 partitions: one for each codon position and another for ITS1. An isosceles triangle's depth (along the tree) corresponds to the depth of the most divergent specimen in the clade while its width represents the number of varieties. Each terminating line represents a single specimen. Fig. 3. The phylogenetic tree reconstructed from the alignment of 93 COI sequences using Bayesian inference with Metropolis-coupled Markov chain Monte Carlo (MC 3 ): the posterior probabilities are indicated next to the nodes.
The dataset was divided into 3 partitions according to the positions in the codons. An isosceles triangle's depth (along the tree) corresponds to the depth of the most divergent specimen in the clade while its width represents the number of varieties. Each terminating line represents a single specimen. (0. 9%) on average. Even though B. prima seemed to be genetically indistinguishable from B. pallida, 5 specimens of the former formed a subgroup within this clade (B. sp. (cf. prima)) with posterior probability of 0. 98 in the combined tree. The average difference among the COI sequences in this subgroup was 2. 5 bp (0. 38%) while the sequences differed from those in the main group by 15. 5 bp (2. 38%) on average. The COI genes of the other two brooders, B. apollon and B. ferox, were so nearly identical that the specimens of B.
Phylogenetic relationship The phylogenetic trees inferred from the three datasets (COI, ITS1, and COI + ITS1) are shown in Figs. 2 3 4. A preliminary inference from the COI (652 bp) dataset using Trichopsis vittata as the outgroup revealed that B. macrostoma should be the root of the trees (result not shown) which was also the conclusion of  Rüber et al. (2004). In addition, Table  3  shows that the COI sequence of B. macrostoma differed the most from those of other specimens. Thus, B. macrostoma was used to root the three inferred trees. Generally, the phylogenetic relationships among the taxa were similar to those in  Rüber et al. (2004). As indicated by the inferred combined and COI trees, B. macrostoma should not be a member of B. unimaculata group. In Rüber et al. , even though the two taxa speciated from other taxa consecutively, the former could not form a clade.
Phylogenetic analysis The aforementioned phylogenetic relationships were obtained after following the procedures described in section 2. 5. 2. After many SS runs, the 2-parameter nucleotide substitution model was selected for the second COI codon partition while the reversible-jump (mixed) nucleotide substitution model  (Huelsenbeck et al. , 2004)  was selected for the third COI codon and the ITS1 partitions. Interestingly, the model choices for the first COI codon partitions of the combined and COI datasets were different: the choice for the combined dataset was the general time-reversible model while that for the COI dataset was the mixed model. Regarding the choices for rate variations across sites, +G + I was best for the second COI codon partition while + G was best for the third COI codon and the ITS1 partitions.
Once again, the choices for the first COI codon partitions of the two datasets were different: +G for the combined dataset and + G + I for the COI dataset. The inconsistency of the model choices for the first COI codon partitions of the two datasets indicated that each Bayesian analysis had to be performed independently: one should not employ the model choice from a dataset in the analysis of another dataset. not too far apart (about 50 km) are genetically different enough to be considered different species. The genetic evidence for a new species of B. simplex was very strong; the COI sequences of the two subgroups differed by more than 10 times the difference within each subgroup and the posterior probabilities for both clades were 1 in the combined and COI trees. However, upon careful inspection of fish specimens (N = 25) from the two catch sites the caudal transverse bars could distinguish the great majority of the type-locality fish (mostly absent) from that of the other fish (mostly present) caught at the other site.
Acknowledgments This work would not have been possible without the help of such aquarist specialists ,  A. Phumchoosri ,  J. Attavichitchanyarak , and  H. Linke. We appreciate  K. Pinkaew ,  N. Buafai ,  A. Nuallaong ,  H. Saelim , and  K. Tongpijit  for their laboratory assistance. We are indebted to  T. Jeenthong ,  Dr. B. Nuangsaeng ,  Dr. N. Sriwattanarothai ,  Dr. A. Monvises ,  S. Somadee , and  N. Jairuksa  for their assistance in sample collections. We thank  A. Blanc ,  R. Blanc ,  Y. Chaisuntornkitti ,  P. Soisook , and  W. Pipatmekin  for providing us with some mouth-brooding bettas,  N. Panitvong  for giving two B. pugnax and  T. Utapong  for one B. prima in Thailand, and  U. T. Nhản ,  T. Vịnh , and  Z. Pinitpolnikorn  for some B. prima from Vietnam. We are also indebted to several other people, not mentioned here, who helped us at betta collecting sites.
Spectrum sensing has been identified as the key step of the cognition cycle and the most important function for the establishment of cognitive radio. In this paper, a blind cyclostationary feature detector, which is based on the symmetry property of cyclic autocorrelation function (SP-CAF), is implemented and tested using universal software radio peripheral platform and GNU Radio open-source software development toolkit. Performance of the SP-CAF is compared to the classical energy detector via various tests conducted in real scenarios where both detection algorithms are employed to blindly sense the spectrum for opportunistic access. This study shows that the blind cyclostationary feature detector outperforms the classical energy detector while guaranteeing acceptable complexity and low sensing time.
Firstly, theoretical work tested with simulations is not sufficient in CR scenarios and needs to be tested under real physical limitations of the hardware. For http://jwcn. eurasipjournals. com/content/2014/1/26 that, our blind spectrum sensing detector based on the symmetry property of the CAF (SP-CAF), is implemented to exploit its high performance and low sensing time. The complexity of this algorithm, which influences its sensing time, was monitored throughout the experiments and reduced by applying code optimization techniques. A different set of parameters is chosen in each experiment to describe the algorithm sensitivity. Tests are performed using Universal Software Radio Peripheral (USRP N210, Ettus Research, Santa Clara, CA, USA)  [7] , which is a low-cost Software Defined Radio (SDR) transceiver platform, and the GNU Radio free software  [8] , which is a framework for building real-time signal processing systems.
Experimental implementation and code optimization In this section, we present the main components of our test-bed. The test-bed can be divided into two main parts, i. e. , software and hardware parts. The hardware part is in charge of the RF and sampling processing while the radio transceiver's physical layer is software driven, running on a computer. In this paper, our software part comprises GNU Radio to implement both ED and SP-CAF spectrum sensing algorithms. The GNU Radio is an open-source software toolkit for software-defined radio. This toolkit provides a number of radio components, prewritten in Python or C++ programming languages, which can communicate to each other using various data types. Moreover, GNU Radio offers a simple and sophisticated graphical design environment, known as GNU Radio Companion (GRC), which enables users to create signal flow graphs and automatically generates Python source code.
Designers can write their own signal processing blocks if needed and integrate them in GRC. Python is an object-oriented scripting language that runs on Linux/Windows and has great support for interfacing with C++ code. This feature is often used in GNU Radio, where the signal processing blocks are written in C++, and Python is used to 'glue' them together and control the digital data flow. This is done using Simplified Wrapper and Interface Generator (SWIG) by creating shared libraries common for both Python and C++. In other words, SWIG is an interface compiler that connects programs written in C++ with Python as shown in Figure  2. GNU Radio can work as a simulation environment, but it can also create a real radio system using SDR platforms. A snapshot of the receiver model designed in GNU radio is shown in Figure  3.
For the hardware part, we use a USRP N210 motherboard as the radio-frequency interface with a WBX daughterboard (50-to 2,200-MHz receiver) and a wide band antenna to perform spectrum sensing tests. On host processors, where all signal processing blocks take place, the GRC or GNU Radio use universal hardware drivers (UHD) to send and receive signals to/from the USRP N210 board via Gigabit Ethernet link as shown in Figure  4. Figure  5  shows a snapshot of the implemented test-bed. To minimize the complexity of this algorithm, code optimization is applied on the C++ code level. The main complex node of the code was the generation of the IDFT matrix A (see Equation  7 ), as concluded from the complexity tracking tool in GNU Radio. For this reason, the IDFT matrix is created off-line by creating lookup tables which are stored before running the experiments.
The relaxation time T 2 is determined from the signal intensity at the echo-times following each refocusing pulse (). Each voxel in an image was fitted to a single exponential using Matlab (Mathworks) to provide a map of the relaxation time throughout the sample. The inverse of the T 2 relaxation time, R 2 , provides a relaxation map in which the faster relaxing components have higher intensity. Each R 2 map was obtained from the series of 50 T 2 -weighted images sampling the T 2 decay curve. The TE dependence of the measured intensities of every individual voxel was fitted with a three-parameter exponential fit:  (1)  where S is the intensity at echo time TE; A is the intensity at TE = 0 and B is a measure of average noise in the magnitude image. The three adjustable parameters of the fit are A, B and R 2.
Software for Control of Consolidometer and Recording Results A Labview software routine was written to coordinate the recording of the consolidometer transducer output as shown in Figure  4. The outputs from the air line-pressure, and displacement transducers are transferred via a -personal measurement device‖ (PMD1208LS, RS Electronics), to the LabView program by means of a high-speed USB connection. The Labview program provides the following outputs in a graphical interface: (a) real-time graph of the transducer displacement as a function of time, showing the distance that the cartilage is compressed (in millimetres) in the time since monitoring was started (in seconds), updated every 1 s; (b) a real-time graph of the pressure as a function of time provides the hydrostatic pore pressure of the cartilage, as measured by the SAMBA transducer during the time since monitoring was started (in seconds), also updated every 1 s; (c) the real-time distance as recorded by the distance (compression) transducer in the top end of the consolidometer body; (d) a view of the pressure being applied to the system as measured by the air line-pressure transducer at the air supply point; (e) a HEX key that is transmitted from the SAMBA unit before it has been decoded, available for diagnostic work; and (f) an error display panel to warn of incorrect performance.
Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better. Results: Our web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions. Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family.