dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging data To determine neural voice-sensitive areas, we first performed a voice localizer scan  (Belin et al. , 2000). Vocal compared with nonvocal sounds elicited extended activity in the bilateral STC, covering regions in the primary and secondary AC and especially in the anterior STC to pSTC (Figure  2A ). Second, to determine the frequency sensitivity of regions in the AC we performed a tonotopic localizer scan. Using 0. 88-8 kHz sine wave tones, we revealed a typical pattern of frequency maps with a mirror-symmetric frequency progression (highlow-high) centered on the HG  (Da Costa et al. , 2011) , with the low-frequency area approximately located along Heschl's sulcus (Figure  2B ). These two maps are thought to correspond to regions A1 and R (rostral area) of the primate primary auditory core  (Saenz and Langers, 2014).
Section 2 exposes the SE link adaptation techniques focused on throughput maximization bound with received signal strength and AMC to select transmission modes (TMs) in HetNets. Section 3 details the novel PE link adaptation metric proposed in the paper, which is denoted the Green Link Budget (GLB) metric, and accounts for assessment methods for software implementation in multi-RAT architectures. Section 4 deals with the performance and transmit power gains resulting from the GLB metric application upon MiWEBA test cases  [5]  covering mm-wave ultra-wideband (UWB) transmissions  [20]  and Wi-Fi extensions for indoor and hot spots. Conclusions of this paper detail perspectives of this work currently carried out in the MiWEBA project.
The multipath power sensitivity S M, expressed in decibel-milliwatts (dBm), is the minimum Fig. 1 LTE-A  AMC mechanism using CQI-to-MCS mapping Fig. 2  CQI value and modulation level correspondence, from  [18]  required received power at the PHY layer of a given TM which guaranties the QoS settled by a maximum BLER or bit error rate (BER) on data transmission. The GLB metric differs from the SE link adaptation processing in working with a throughput range fixed by the concerned service instead of the highest possible throughput as performed by AMC processing (Section 2). It is the first element arguing on PE oriented radio link selection. Furthermore, the proposed procedure to estimate the metric detailed in Section 3. 2, differentiates the line of sight (LOS) and obstructed and non-line of sight (OLOS/NLOS) propagation links, improving the selection with a conceivable transmit power reduction.
11ac/n MIMO TMs under NLOS conditions delivering a data rate targeted to 80 Mbps. Spatial division multiplex (SDM) MIMO modes are compared together with a variable number of spatial streams, denoted NSS, MCS, and bandwidth size (20 or 40 MHz). Spatial time block coding (STBC) using Alamouti code is integrated in the evaluation. MIMO spatial division multiplexing SDM (NSS, N TX , N RX ) represents the usual SDM technique considering N STS time-spatial streams, N TX , and N RX antennas at the transmitter and receiver sides, respectively. STBC (NSS, N TX , N RX ) refers to the Alamouti code implementation with a number of spatial stream (NSS = 1) lower than the number of time-spatial stream (N STS = 2) to perform spatial redundancy processing. The multi-RAT UE AP selection test case considers a UE connected to two separate AP.
Modified gravity theories often contain a scalar field of gravitational strength which interacts with matter. We examine constraints on the range and the coupling strength of a scalar gravitational degree of freedom using a subset of current data that can be safely analyzed within the linear perturbation theory. Using a model-independent implementation of scalar-tensor theories in MGCAMB in terms of two functions of the scale factor describing the mass and the coupling of the scalar degree of freedom, we derive constraints on the f (R), generalized chameleon, Symmetron and Dilaton models. Since most of the large scale structure data available today is from relatively low redshifts, only a limited range of observed scales is in the linear regime, leading to relatively weak constraints. We then perform a forecast for a future large scale structure survey, such as Large Synoptic Survey Telescope (LSST), which will map a significant volume at higher redshifts, and show that it will produce much stronger constraints on scalar interactions in specific models.
III. CONSTRAINTS FROM CURRENT DATA In this Section, we use a combination of currently available CMB, lensing and Baryonic Acoustic Oscillation (BAO) data, as well as measurements of the matter power spectrum, to derive constraints on the GBD parameters. To compute the observables, we implemented the parametrizations described in the previous Section in MGCAMB. We then use it with an appropriately modified version of CosmoMC  [62]  to obtain the posterior distributions for the model parameters. Since current data is unable to simultaneously constrain multiple GBD parameters, we will only consider models from the previous Section for which meaningful constraints are possible.
A. The datasets used in the analysis We use the measurements of CMB temperature anisotropy from the second data release of the Planck survey  [63]  in the form of the full Planck TT high-likelihood (30 < < 2500) along with the low-polarization ( < 30). We refer to the above datasets as PLC. We also consider the Planck 2015 lensing potential spectrum  [64]  extracted from mode-coupling correlations, and refer to this dataset as CMBLens. In addition to inducing higher order correlations, lensing by large scale structures affects the TT spectrum at higher , slightly damping the oscillatory features. In  [65] , and subsequently in  [63] , the lensing contribution to TT was quantified via an amplitude A L multiplying the lensing power spectrum in the calculation of the theoretical prediction for TT.
The parameter A L was used to quantify the significance of detection of the lensing contribution to TT. However, instead of measuring the expected value of A L = 1, since the lensing contribution to TT is calculated from the same model as the rest of the spectrum, the best fit value obtained for LCDM from the PLC dataset in  [63]  was A L = 1. 22 ± 0. 10, or two stan- dard deviations away from the expectation. As discussed in  [65]  (see also  [66] ) this is due to an apparent tension between the higher-and lower-data when trying to fit LCDM to Planck TT data. To negate the effect of this tension, the parameter A L was sometimes co-varied with other parameters when deriving constraints on LCDM in  [63]. In what follows, we take the view that A L is not a physical parameter and should be held fixed to 1 when deriving constraints on cosmological models.
However, we also investigate and discuss the effect of co-varying A L in the case of f (R). For BAO measurements, we used data from the 6dF survey  [67]  and from SDSS, specifically the MGS  [68]  and BOSS data releases (LOWZ and CMASS)  [69]. We also use the matter power spectrum (referred to as MPK) from SDSS LRG DR4  [70] , but only on linear scales, k ≤ 0. 1 h/Mpc. We are aware of the fact that nonlinear corrections can play a role even at k < ∼ 0. 1Mpc and that a proper treatment of the bias and the redshift space distortions (RSD) must take them into account. This was studied at length in  [71]  for the SDSS DR9 power spectrum and it was found that the differences in the upper bounds on neutrino masses obtained using four different RSD models were under 20%. Based on this, we expect that bounds on the GBD parameters (such as f R0 ) obtained from MPK are accurate to within 30%, which is sufficient given that constraints form current data are relatively weak.
B. Constraints on f (R) The Hu-Sawicki f (R) model has two parameters, f R0 and n. In what follows, we fix n = 1 because that is a common choice in the literature, and also because the two parameters are highly correlated and the current data cannot simultaneously constrain both. We chose a flat prior on log 10 f R0 within the [-7, 0] range. We have checked that changing the range of the flat prior does not The solid lines show the PDF in case of massive neutrinos with a fixed mass mν = 0. 06 eV, while the dashed lines show the PDF for the case when the neutrino mass was varying. Due to the degeneracy between fR 0 and mν , we see that the constraint on fR 0 become weaker when the neutrino mass is varied. The datasets are labeled according to the notation introduced in Sec. III A. The symbol + means that we add data on top of the PLC+BAO dataset.
For example, +lensing means PLC+BAO+lensing. affect our results. Fig. 2  shows constraints on f R0 for different combinations of datasets described in Sec. III A, after marginalizing over all the other cosmological parameters. We considered the case in which the total neutrino mass is fixed at m ν = 0. 06 eV (solid lines), and the case where it can vary within 0 ≤ m ν ≤ 1 eV (dashed lines). The results from Fig. 2  are summarized in Table  I. We can see that the combination of PLC and BAO datasets (blue lines) only weakly constrains the model. Modified gravity affects the CMB temperature anisotropy spectrum in two ways: it affects the lowpower spectrum through the ISW effect and enhances the damping at high-due to the enhancement in clustering and, as a consequence, the lensing potential. Thus, the observed lack of power at low-multipoles and the apparent preference of enhanced lensing in CMB TT, when compared to the ΛCDM prediction, can be reconciled by a non-zero f R0.
This is the reason for the peak in 0. 0 0. 1 0. 2 0. 3 0. 4 0. 5 0. 6 0. 7 Σm ν (eV) the PLC+BAO likelihood. Adding the CMBLens data (red lines) tightens the constraint substantially. The enhancement of growth due to the extra scalar interaction affects the lensing potential measured by Planck, which is known to be in excellent agreement with the LCDM prediction  [64]. Thus, the weak preference for larger f R0 coming from PLC+BAO is overwhelmed by the stronger CMBLens data that is consistent with f R0 = 0. The constraint becomes even tighter after adding the MPK and WL datasets (green lines). The dashed lines in Fig. 2  show the impact of covarying the combined mass of neutrinos, m ν , along with f R0. Massive neutrinos suppress the growth and can partially compensate for the enhanced clustering in f (R), slightly weakening the bounds on f R0.
4. Although it seems that, in the case of PLC+BAO, the lensing amplitude tension has been reconciled, we argue that this is not due to a genuine signal of modified gravity. As discussed previously, the PLC+BAO data yields a peak in the likelihood of f R0 because the preference for enhanced lensing and the lack of power at low in C T T can be reconciled with a nonzero f R0. The enhanced lensing appears to cure the A L problem and this is depicted in Fig. 4 , where we see that there is a strong degeneracy between A L and f R0 for large values of the latter (blue contours). However such large values of f R0 are ruled out once we add the datasets that probe clustrering (green contours). Still, the value of A L when co-fit with f R0 is in better disagreement with the prediction. For the combination of all data we find A L = 1.
The research of AH, AP, AZ and LP is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). AP was sponsored in part by the Robert Frazier Memorial Fellowship. AZ is supported in part by the Bert Henry Memorial Entrance Scholarship at SFU. GBZ is supported by the Strategic Priority Research Program "The Emergence of Cosmological Structures" of the Chinese Academy of Sciences Grant No. XDB09000000. This research was enabled in part by support provided by WestGrid [85] and Compute Canada [86]. P. B. acknowledges partial support from the European Union FP7 ITN INVISIBLES (Marie Curie Actions, PITN-GA-2011-289442) and from the Agence Nationale de la Recherche under contract ANR 2010 BLANC 0413 01. ACD acknowledges partial support from STFC under grants ST/L000385/1 and ST/L000636/1.
ACKNOWLEDGMENTS The research of AH, AP, AZ and LP is supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). AP was sponsored in part by the Robert Frazier Memorial Fellowship. AZ is supported in part by the Bert Henry Memorial Entrance Scholarship at SFU. GBZ is supported by the Strategic Priority Research Program "The Emergence of Cosmological Structures" of the Chinese Academy of Sciences Grant No. XDB09000000. This research was enabled in part by support provided by WestGrid [85] and Compute Canada [86]. P. B. acknowledges partial support from the European Union FP7 ITN INVISIBLES (Marie Curie Actions, PITN-GA-2011-289442) and from the Agence Nationale de la Recherche under contract ANR 2010 BLANC 0413 01. ACD acknowledges partial support from STFC under grants ST/L000385/1 and ST/L000636/1.
Gene ontology (GO) enrichment analysis. More than 400 GO entries with p-values were collected and assigned to three major categories viz. , biological process (BP), molecular functions (MF) and cellular components (CC). The redundant GO entries were filtered for retrieving a positive dataset. Then GO entries were submitted to reduce visualize gene ontology (REViGO) tool for generating scatter plot of MF (Fig. 2B ) and then the proteins were converted to Cytoscape plug-in model for constructing PPI network.
The major non-interacting proteins were pheromone-, fatty acid derivative-, glycoprotein-, lipid-, drug-, small molecule-, apolipoprotein-, ion-, and protein-lipid complex binding protein (Fig. 2E ). The integrated proteins datasets were connected by edges to the nodes of proteins, which indicated the molecular interaction between the proteins. The depiction of P-value significance with log size enrichment as different colored parameter scales are red, blue, green, and yellow stripes for 0x-2x, 2x-4x, 4x-6x, and 6x to a maximum of 7. 6x, respectively (Supplementary Table  S1 ). The blue and green nodes shows the maximum threshold of the log size for the scale 2x-6x, which correspond to the binding protein.
The topology of H-bond interaction in EULP is shown in Supplementary Fig. S2A. The EULP showed outside topology, which was identified from TMHMM, HMMTOP and TMPRED servers. It contains two transmembrane segments with critical loop length of 60 residues and the cutoff for putative transmembrane segments score was 0. 60, which was identified from TOPPRED method. It was also evident that EULP occurs in non-cytoplasmic cellular location, which was predicted using Phobius (Supplementary Fig. S2B-E ). The typical appearance of β -barrel shape and the phosphorylation sites are represented as a cartoon of EULP structure (Fig. 3B, C ). Superimposition alignment. Structural superimposition was performed for prediction of pair-wise structural alignment between the EULP and FABP (PDB ID: 4A60_A) using improved protein block alignment (iPBA) webserver.
Methods Evolutionary relationship analysis. Collection of lipocalin sequences of rat. The MLF protein sequences of rat such as MUP, FABP, OBP, and RBP were retrieved from non-redundant protein (nr) database of National Centre for Biological Information (NCBI) (http://www. ncbi. nlm. nih. gov) and protein data bank (PDB) (http:// www. rcsb. org). The retrieval of similar proteins was based on iterated profile search with alignment scoring matrix, which was made by position-specific iterated basic local alignment searching tool (PSI-BLAST)  46. First, around 75 sequences were collected, which were submitted for removing redundant sequences at 99% adopting cluster database at high identity with tolerance by (CD-HIT) server (http://www. bioinformatics. org/cd-hit)  47. Phylogenetic tree construction.
GO enrichment and PPI network analysis. Identification of GO ID, enrichment, and elucidation of p-values of EULP sequence were carried out through GO consortium (http://geneontology. org/page/ go-enrichment-analysis) connected to PANTHER classification system. The predicted GO term was used to assign proteins to distinct clusters and generate a scatter plot using REViGO analysis that is specific to molecular function (http://revigo. irb. hr)  50. The tree map of PPI was predicted and retrieved as Cytoscape plugin for generating integrated network. The integrated network was visualized through Cytoscape V. 3. 2. 1, which is an open source software platform supported with Java environment to develop complex biological networks  51. Also, Cytoscape adopts nested network style with log size-based group attributes for establishing circular layout of PPI network.
Phosphorylation site. Phosphorylation sites and kinase-specific phosphorylation sites such as Ser (S), Thr (T) and Tyr (Y) of EULP sequence were predicted using the online web servers NetPhos 3. 1 (http://www. cbs. dtu. dk/services/NetPhos-3. 1/)  30. The program works based on artificial neural network algorithms. Structural documentation. Conserved residue analysis. The EULP model was analyzed for the identification of functional residues and mapping conserved regions in both exposed and buried residues of structure using ConSeq and ConSurf tool (http://consurf. tau. ac. il/)  52. This is an online web server, which calculates conservation score of the position of individual amino acids among their close sequence homologues using Bayesian algorithm, and the default JTT matrix was used as evolutionary substitution model.
The Consurf algorithm was used to evaluate confidence intervals of calculated conservation score visualization based on 1-9 grade scale depicting variation in color. The most variable residue positions were present in grade 1 (turquoise) and the most conserved residue positions were present in grade 9 (dark red). Accordingly, the protein structure was also colored and it was visualized by JSmol (default mode for First glance). Topology prediction. The prediction of EULP structural topology was carried out using various web-based online servers such as ProFunc (http://www. ebi. ac. uk/thornton-srv/databases/profunc)  53  from EBI, TMHMM, HMMTOP, TMPRED, TOPPRED, and Phobius for getting a prominent fold architecture of the target protein. The multiple web servers were used to compute the inside and outside topologies of the N-terminal residue and the prediction of hydrophobicity plot for analysis of transmembrane (TM) region in the protein.
Structural superimposition of EULP. Homology-modeled structure of EULP and its template structure (PDB: 4A60_A) were superimposed using iPBA (http:// www. dsimb. inserm. fr/ dsimb_ tools/ ipba/ index. php). This algorithm was used for pair-wise comparison of multiple structures and it employs anchor-based dynamic programming for identifying structurally conserved regions as local alignments. The degree of structural similarity between backbone Cα atom pairs of the model and the respective templates was calculated by RMSD. The iPBA was extensively outperformed using a nontrivial benchmark dataset and compared to other well established structural alignment methods  36. Binding site residue. The active site of the modeled EULP was analyzed using CASTp (http://sts. bioe. uic. edu/ castp/calculation.
Protein model submission. The 3D coordinates of homology-modeled protein was structurally documented and deposited in Protein Model Database (PMDB) (https://bioinformatics. cineca. it/PMDB/). The database provides information about manually built homology-modeled proteins in a manner simple to deposit or retrieve the model in PMDB. The EULP model was deposited successfully and accepted in PMDB. After submission, a unique identification number (PMDB ID: PM0079851) was provided by PMDB for the EULP model. Simulation of EULP structure. The MD simulation was carried out for the modeled EULP using GROMACS V. 4. 5. 5 (http://www. gromacs. org/)  37 software package to analyze the stability of the protein. The MD simulation of EULP was performed using GROMOS96 53a6 force field with twin-range cut off (0.
Also, the fluorescent compound 2-naphthol was employed to find a binding affinity and residual interaction towards EULP using docking. Tanimoto similarity analysis expounds that the 3D structures of farnesol and 2-naphthol are not similar (Supplementary Information Fig. S3C ). Physico-chemical property analysis and Optimization of ligand. Physico-chemical properties of all ligands were identified from various databases such as PubChem, ChemSpider, and DrugBank (Supplementary Table  S5 ). In addition, the physico-chemical properties of pheromone compounds which included H-bond donor-acceptor, topological polar surface area (A 2 ), density, boiling, melting point, octanol/water partition coefficient and Lipinski RO5 were tabulated and presented in Supplementary Table  5. The energy minimization of EULP and all ligands were prepared in Swiss-PDBViewer adopting GROMOS96 Force Field algorithm.
Protein-ligand complex. Interaction of novel endogenous ligands with the EULP model was analyzed by docking module in Discovery Studio. Accelrys Software Inc (V. 2. 1) (Accelrys, San Diego, USA) was used for molecular docking studies (http://accelrys. com/products/discovery-studio/). Experimental animals. The female house rats (Rattus rattus) were trapped at residential areas close to Bharathidasan University, with proper permission from the house owners. The reproductively active female rats (150-200 g) were selected and acclimatized to laboratory conditions for a month. The rats were housed in polypropylene cages (40 × 25 × 15) with 2 cm rice husk lining at the bottom as hygienic bedding material, which was changed once every three days. The regular estrous cycling animals were selected for further experimental study.
In this study, 2-naphthol was used without further purification and the concentration of EULP protein (24 μ M), and 2-naphthol (1 mM stock) were prepared in 100% ice-cold ethanol, which was stored in the dark at 4 °C. The 2-naphthol was further diluted for each experiment to a final concentration of 25 μ M and the 10 mM phosphate buffer used at pH 7. 2 at room temperature in all experiments. All pH values were measured by a digital pH meter with a combined glass-calomel electrode (Systronics, India). Fluorescence intensity and the measurements were carried out using Synergy HTX multi-mode microplate reader (BioTek Instruments, Inc. , Winooski, VT, U. S. A) and FP-6500 spectrofluorometer. Fluorescence determinations were made using a tungsten halogen lamp with a PMT detector for maximum sensitivity and the data collection, analysis of results, and exporting were done by Gen5 Data Analysis Software.
Acknowledgements The  BTIS (Biotechnology Information System ),  DBT  ( Department of Biotechnology ),  Ministry of Science and Technology, Government of India , provided facility for docking study. The instrumentation facility was availed from  DST-FIST level I, UGC-SAP phase II and DBT-AIST, Government of India. BG and PP acknowledges the  Lee Kong Chian School of Medicine, Nanyang Technological University MOE Start-Up  Grant, Singapore. D. R. thanks  DBT-AIST , for the Research Fellowship.
For vast areas of the globe and large parts of the tree of life, data needed to inform trait diversity is incomplete. Such trait data, when fully assembled, however, form the link between the evolutionary history of organisms, their assembly into communities, and the nature and functioning of ecosystems. Recent efforts to close data gaps have focused on collating trait-by-species databases, which only provide species-level, aggregated value ranges for traits of interest and often lack the direct observations on which those ranges are based. Perhaps under-appreciated is that digitized biocollection records collectively contain a vast trove of trait data measured directly from individuals, but this content remains hidden and highly heterogeneous, impeding discoverability and use. We developed and deployed a suite of openly accessible software tools in order to collate a full set of trait descriptions and extract two key traits, body length and mass, from >18 million specimen records in VertNet, a global biodiversity data publisher and aggregator.