dataesr/scientific-paragraphs-categorization huggingface dataset:
Accelerometers were used to identify individual attempted prey captures (APC), and animal-borne video data were used to independently verify APC and prey types. APC were identified by peaks in the variance of acceleration using the variance threshold and the minimum interval between consecutive APC (Function 1, see methods). Metric classes were calculated by comparing each individual APC identified by the accelerometer (estimate) verses APC identified in the video (actual value, with 0 = absent and 1 = present, see methods). Dataset provided is for the generic parameters of Function 1 on the testing subset of data. (PDF) S1 Text. Description of Function 1 that identified attempted prey captures (APC) and details on how video was matched to accelerometer data. (DOCX).
Acknowledgments We thank all of the Kanowna field volunteers including  J. Heritage ,  T. Knox ,  M. Berlincourt ,  S. Guggenheimer ,  N. Delaunay ,  M. Holmes ,  F. Kooman , and  M. Kriloff. We are also grateful to  A. Baylis ,  H. Lourie ,  J. Semmens , and  N. Dorville  for performing the manual video identification. We also  M. Hindell  for logistical and video camera assistance. We also thank  B. Wright ,  A. Trites ,  R. Joy , and  D. Rosen  for comments on early drafts of the manuscript.
Urea Breath Test ( 13 C-UBT) and Quantitative Real-Time PCR (qPCR) for H. Pylori The presence or absence of active H. pylori infection was evaluated for all mice one week after the infection, and again at both 6 and 18 weeks after infection, before euthanasia, with an adapted 13 C-UBT, as previously described  [15]. A qPCR was performed on extracted DNA from the stomach in order to evaluate the putative effect of curcumin treatment on H. pylori. A real-time PCR based on SYBR Green technology was used to quantify H. pylori and mouse GAPDH (Glyceraldhehyde 3-phosphate deshydrogenase), an epithelial cell target, in stomach DNA extracts. H. pylori specific PCR targets the gene encoding 23S rRNA of H. pylori as already described  [16]. GAPDH primers (mGapdh1for: CTGCAGGTTCTCCACACCTATG; mGapdh1rev: GAATTTGCCGTGAGTGGAGTC) were designed using Primer Express software (Life Technologies).
Data was normalized to the mean values of the five HKG, and the relative amount of RNA was calculated using the 2 -ΔCt method. Fold-change calculations were done using SABiosciences' data analysis software, which automatically calculates the fold-change in gene expression between the infected non-treated mice and the control group (IG versus CG), and between the infected and curcumin treated mice and the control group (IG + C versus CG). Fold-change values greater than one indicate an up-regulation, while fold-changes less than one indicate a down-regulation.
Introduction Panobinostat is a pan-histone deacetylase inhibitor (HDACi) with low nanomolar activity against class I, II, and IV histone deacetylases  [1, 2]. Panobinostat (Farydak®) in combination with bortezomib and dexamethasone was recently approved in the USA, European Union, and Japan, for treatment of relapsed or relapsed and refractory multiple myeloma, in patients who had received at least 2 prior regimens, including bortezomib and an immunomodulatory agent. The anticancer activity of panobinostat is thought to be due to its effect on epigenetic modulation and inhibition of proteolytic Information on clinical trials The data presented in this manuscript are from the following 2 clinical trials: PANORAMA-1 and B2207. Both the trials are registered at ClinicalTrials. gov with the following identifier numbers: NCT01023308 (for PANORAMA-1) and NCT00532389 (for B2207).
Methods Multiple clinical trials with panobinostat PK are compared in the current analysis. All patients in the trials gave written informed consent for participation, and the study protocols were approved by the institutional review board/independent ethics committee/research ethics board at each study site. The clinical trials PANORAMA-1  [7]  and B2207  [8]  are registered at ClinicalTrials. gov with the identifier numbers NCT01023308 and NCT00532389, respectively. A detailed description of the study design and key results for panobinostat single-agent trials are available in the published literature  [9, 11, 12, 18].
B2207 trial PK assessment was carried out in the dose escalation and dose expansion parts of the trial for both panobinostat and bortezomib. In the dose escalation part, PK samples were collected on cycle 1, day 8, and cycle 1, day 15 at pre-dose, and various intervals post-dose up to 48 h. In the dose expansion part, an initial PK sampling was done on cycle 1 day 8 to obtain PK data for panobinostat and bortezomib in the absence of dexamethasone. Subsequent PK sampling was done on cycle 2, day 8 to obtain PK data for panobinostat and bortezomib in the presence of dexamethasone. Concentration-time profiles of dexamethasone were not assessed. Blood samples were taken at pre-dose (for both drugs): at 5 and 15 min (for bortezomib alone) and at 0. 5, 1, 2, 3, 4, 6, 8, 24, and 48 h post-dose (for both drugs).
Efficacy and safety assessment The two combination studies, B2207 and PANORAMA-1, reported efficacy data in patients with relapsed and refractory multiple myeloma  [7, 8]. In the current analysis, exposureefficacy relationship was explored using the efficacy data (ORR based on IMWG criteria) from the B2207 trial. Exposure-safety relationship was also characterized for the dose ranges of 10 to 30 mg panobinostat and 1 or 1. 3 mg/m 2 bortezomib in the B2207 trial.
Nuclear organization can impact on all aspects of genome life cycle. This organization is thoroughly investigated by advanced imaging and chromosome conformation capture techniques, providing considerable amount of datasets describing the spatial organization of chromosomes. In this review, we will focus on polymer models to describe chromosome statics and dynamics in the yeast S. cerevisiae. We suggest that the equilibrium configuration of a polymer chain tethered at both ends and placed in a confined volume is consistent with the current literature, implying that local chromatin interactions play a secondary role in yeast nuclear organization. Future challenges are to reach an integrated multi-scale description of yeast chromosome organization, which is crucially needed to improve our understanding of regulation of genomic transaction.
Over the last decade we witnessed the advent of genomic methods to sense nuclear architecture, such as Chromatin immunoprecipitation (ChIP), DNA adenine methyltransferase identification (DamID), and the most widely used intra-molecular ligation of cross-linked DNA, named chromosome conformation capture (3C), and its genome wide derivatives including Hi-C  [16, 17]. This booming field calls for new models to integrate datasets of different nature (microscopic distance measurements, ChIP, DamID, contact frequency map from 3C), and coarse-grained polymer physics models met some success in the recapitulation of heterogeneous data with a single and unified representation  [18]. Necessary improvements are still needed to enhance the spatial resolution of polymer models so as to recapitulate the folding principle of DNA, chromatin, and chromosomes.
Models of nuclear architecture: Direct vs indirect modeling In the last 25 years, essentially two classes of models have been proposed to describe genome organization: direct (or data driven) modeling or inverse (or physics driven) modeling (for review, see  [19] ). In direct modeling, experimental datasets are used as inputs, and modeling is built by minimizing the discrepancy of the model to the data. Therefore, such models are tailored to recapitulate input data but by construction, they have little or no predictive value, and new datasets must be obtained before generating a modified model. They can be however very useful since they recapitulate complex data in a frame which is usually amenable to be visually interpreted directly. The other approach consists to build a model with a set of assumptions involving, among others, the mechanics of chromosomes (rigidity and friction) and the geometry of the nucleus.
The output of the model can be compared with experiments  [20] [21] [22] , and its predictive value can be challenged with novel datasets or whenever the set of microscopic parameters that fit experiments appear to be inconsistent with the literature. In fact the consistency of the model with experiments does not imply its validity, especially because the essential parameters to describe large-scale nuclear architecture remain elusive. At this step we propose to highlight some of the main conclusions inferred from modeling of eukaryotic organization with polymer physics.
5 to -1 for different human cell lines  [28]. The general relevance of the crumpled globule model has therefore been called into question, because p(s)~s -1. 5 is expected to be detected in equilibrium globules. Concerning the yeast S. cerevisiae, only two genome-wide datasets are available  [23, 29] , and more data and analysis are needed to confirm or invalidate the actual folding scheme. Notably possible fixation artifacts (some of which can be normalized) in 3C techniques, and the difficulties to convert contact frequency to physical distances should not be ignored  [30] [31] [32] [33]. It has been suggested that one way around these technical limitations was to combine 3C methods with microscopy observations, which are now combined  [17, 25]. We thus conclude that the folding principles of chromosomes at the entire genome level remain controversial, but the number of contributions in this booming field should rapidly clarify these central questions.
This second regime corresponds to Rouse model of polymer dynamics, in which the MSD increases with time with a power-law scaling of 0. 54. The red rectangle represents the observation window in a real experiment with finite temporal and spatial resolutions. (B) The blue dataset represents the average MSD over 45 trajectories for a selected bright locus located at position 380 kb on chromosome XII (room temperature). Note that standard errors are indicated as vertical caps. Two regimes can be distinguished with a sharp increase of the MSD at short time scales (scaling exponent of 0. 75) followed by a behavior consistent with the Rouse regime (scaling exponent of 0. 54). Respective trend lines are shown in black and red.
and probes for DNAJA1 gene (the genetic marker for beef tenderness) were designed by the Nucleic Acid Facility of the Pennsylvania State University using sequence data from Genbank and the real-time PCR probe/primer design software Primer Express (v2. 0, Applied Biosystems). For real-time qPCR analysis performed by the Nucleic Acid Facility of the Pennsylvania State University, DNAsetreated RNA was reverse-transcribed using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Foster City CA) and the protocol provided with the kit. Quantitation by real-time PCR was done by adding 10 or 20 ng of cDNA in a reaction with 2X TaqMan Universal PCR Master Mix (Applied Biosystems, Foster City CA) in a volume of 20 µL. Primer was added at a concentration of 400 nM and a probe at 200 nM which was.
Relative Level of DNAJA1 Gene Expression Primers and probes for DNAJA1 gene (the genetic marker for beef tenderness) were designed by the Nucleic Acid Facility of the Pennsylvania State University using sequence data from Genbank and the real-time PCR probe/primer design software Primer Express (v2. 0, Applied Biosystems). For real-time qPCR analysis performed by the Nucleic Acid Facility of the Pennsylvania State University, DNAsetreated RNA was reverse-transcribed using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems, Foster City CA) and the protocol provided with the kit. Quantitation by real-time PCR was done by adding 10 or 20 ng of cDNA in a reaction with 2X TaqMan Universal PCR Master Mix (Applied Biosystems, Foster City CA) in a volume of 20 µL. Primer was added at a concentration of 400 nM and a probe at 200 nM which was labeled with a 5' FAM and a 3' Black Hole Quencher (Biosearch Tech, Novato, CA).
COREGNET is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, COREGNET can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific regulator activity, identify cooperative transcription factors and analyze the sample-specific combinations of active regulators through an interactive visualization tool. In this study COREGNET was used to identify driver regulators of bladder cancer.
Introduction Recent advances in genomics enabled the profiling of thousands of tumors by large consortia and individual laboratories. While the amount of data holds great promise for our understanding of tumorigenesis, these datasets necessitate efficient methodologies to extract valuable knowledge from them. Transcriptomics is the most commonly available type of tumor large-scale data. The transcriptome reflects the genetic, epigenetic and environmental states of a tumor tissue and determines a great extent the phenotype of cells. Therefore, one of the first step towards the construction of a mechanistic model underlying cancer is the identification of the sets of transcription factors (TF) that actively maintain a malignant phenotype. This requires methodologies to model the tissue specificity of gene regulation by inferring trustful context-specific networks.
More importantly, these models must take into account the complexity of mammalian gene regulation often involving the coordinated action of several TF  (Panne, 2008). To identify tumor-driving active regulatory circuits, we propose a Bioconductor  (Huber et al. , 2015)  package named COREGNET to (i) reconstruct a large-scale co-regulatory network from gene expression data and by integrating additional regulatory evidences such as TF Binding site and ChIP data, (ii) estimate the activity of each TF of the network in any given sample, (iii) predict sets of cooperative TF and (iv) identify sample-specific combination of active and driver TF using an interactive visualization tool integrating genomic aberrations. The proposed methods can be used as independent modules with alternative inputs such as networks inferred by other methods, experimentally defined networks or a different transcriptomic data for TF activity prediction and visualization (e.
Regulatory network inference and refinement To reconstruct a large-scale regulatory network from gene expression data, the COREGNET package implements the H-LICORN algorithm  (Chebil et al. , 2014; Elati et al. , 2007). Compared to current methods, H-LICORN focuses on the identification of cooperative regulators of genes. It was proven to have comparable TF-gene pairs V C The Author 2015. Published by Oxford University Press. prediction performance with state of the art methods in synthetic and Human datasets  (Chebil et al. , 2014)  and to retrieve more plausible cooperative TF pairs in yeast  (Elati et al. , 2007 , Lai et al. , 2014)  and Human datasets (see Supplementary Information). To obtain a trustful network, regulatory and co-regulatory interactions can be integrated as additional evidences.
Integrative visualization of transcriptional activity Transcriptomes are summarized by the influence of the regulators on the expression of their target genes in the analyzed samples. This abstraction of the transcriptomes through TF activity reduces the number of features thereby simplifying the visualization of the dataset. Moreover, the co-regulation network unravels the combination of TF at work in the studied samples. Thus, an interactive visualization tool is embedded in the COREGNET package to analyze several layers of information through the sets of active co-regulators. The co-regulation network is accessible through a Cytoscape  (Shannon et al. , 2003)  widget with functionalities to display sets of active co-regulators in particular samples or subtypes (examples in Fig. 1  and in Supplementary Information).
Case study The COREGNET package was used to analyze a set of bladder cancer samples for which both transcriptome and genomic alterations were available  (Biton et al. , 2014 , Rebouissou et al. , 2014). The inferred network was used to estimate the influence of regulators and the visualization tool to identify active sets of master regulators for each bladder cancer subtype. In line with previous studies  (Biton et al. , 2014; Choi et al. , 2014) , PPARc was found to be the most active TF in the luminal bladder cancer TCGA subgroup I. Association with genomic alteration suggesting PPARc to be a major driver of these tumors (see Supplementary Information), which has been recently validated experimentally  (Biton et al. , 2014). Moreover, FOXA1, an effector of PPARc  (Varley et al. , 2008)  and a co-factor of GATA3 in luminal breast cancer  (Kong et al.
6 mM. At specific time intervals, aliquots were removed for quenching in 0. 5 M sodium acetate, pH 5. 2, and ethanol-precipitated. Sample treatment and data collection were performed as described previously  (15). [ 3 H]Gln was used to determine the K m of GlnRS for Gln because of the low K m values of GlnRS for tRNA and Gln. The reaction contained 30 mM HEPES, pH 7. 5, 25 mM KCl, 15 mM MgCl 2 , 5 mM DTT, 4 mM ATP, 10 M tRNA Gln (CUG), and 0. 1 mg/ml BSA at 37 °C. The concen- tration of [ 3 H]Gln varied between 2. 5 and 25 M. The reaction was initiated by adding 5 nM GlnRS. Sample treatment and data collection were performed as described previously  (15). All experiments were repeated successfully.
Collection of human renal ADPKD tissue and patient characteristics. Kidney specimens of seven patients (six men, one woman; age, 55. 6 ± 9. 3 years (mean ± S. D. )) were obtained as described previously. 13 Briefly, tissue was fixed immediately after nephrectomy in 3% paraformaldehyde (pH 7. 4). Six patients were on hemodialysis at the time of nephrectomy, thus representing rather late stages of ADPKD. Collection and analysis of tissue samples were approved by the local ethics committee. shRNA and generation of ANO6-deficient cells. Primers complementary to two distinct regions of Canis familiaris ANO6 (accession number XP_852020. 1) were cloned BglII and XhoI into the pSUPERIOR vector (Oligoengine, Seattle, WA). Correct cloning was verified by sequencing. As a negative control, pSUPERIOR containing a scrambled sequence was purchased from Oligoengine.
Acknowledgements. We thank  Barbara Teschemacher  and  Margot Rehm  for excellent technical support. We thank  Ruth Stadler  for her support with the confocal microscopy. VF was supported by the  Interdisciplinary Center of Clinical Research (IZKF)  at the  University of Erlangen-Nuremberg. BB was supported by the  Deutsche Forschungsgemeinschaft  ( DFG BU2918/2-1 ),  Else-Kroener-Fresenius Stiftung  ( 2013_A299 ), the  Bayerische Forschungsallianz  ( BayIntAn_FAU_2014_134 ) and the  Center for Kidney and Blood Pressure Research Regensburg-Erlangen-Nuremberg (REN). RS and KK were supported by  SFB699 A7/A12. The present work was performed by VF in fulfillment of the requirements for obtaining the degree 'Dr Med'.
Production of Transgenic Tobacco Overexpressing Plastid Transketolase A database search identified two genes encoding plastid TK in Arabidopsis, TKL1 and TKL2. The microarray data available in the database (https://genevestigator. com/gv/plant. jsp) revealed different anatomical and developmental expression patterns for each of the Arabidopsis TKL genes. TKL1 is preferentially expressed in most organs, including photosynthetic tissues, except for senescing leaves and seeds, where TK2 was more highly expressed (Supplemental Figure  1A ). Based on these data, a full-length At-TKL1 cDNA was used to prepare a sense gene construct in the binary vector pRoK2 containing the cauliflower mosaic virus 35S promoter and NOS terminator. The recombinant vector was transferred to Agrobacterium tumefaciens, and this was used to transform wild-type tobacco.
Photosynthetic capacity was compared in the cotyledons and the first true leaves of TKox and wild-type seedlings (10 d after germination), grown in media with or without TPP, using chlorophyll fluorescence imaging. The data showed that the maximum quantum efficiency of photosystem II (PSII) photochemistry, F v /F m , and the operating efficiency of PSII photochemistry, F q '/F m ', were significantly reduced in TKox lines grown in normal media, compared with the equivalent wild-type plants (Figure  7B ; Supplemental Figures  5A  and 5B ). Whereas the fluorescence signals were relatively homogeneous in wild-type plants, the TKox lines showed variable fluorescence signals. By contrast, TKox plants grown in media supplemented with TPP (10 mg L 21 ) had a similar F v /F m and F q '/F m ' to that of wild-type plants.
2 (cotyledon fully expanded and 1st true leaf visible). All material was immediately snap frozen in liquid nitrogen and stored at 280°C for later analysis. Primary root length was measured by positioning the plates vertically in the growth chamber. Shoot/rosette growth of younger plants (<26 d) was recorded by photographing the plants. Images were analyzed using the ImageJ software package (Image J version 1. 45). For destructive harvest growth analysis, leaves and roots were separated from the plants and laid out flat, photographed, and analyzed using ImageJ, while dry weights were obtained by drying plant material at 60°C until a constant weight was obtained (;4 d).
The cycling conditions were as follows: initial denaturation, 95°C, 2 min to activate the Taq polymerase, followed by 45 cycles of denaturation at 95°C for 5 s, annealing at 60°C for 10 s, and extension at 72°C for 10 s. The amplification was followed by a melting curve analysis that ranged from 65 to 90°C with a temperature increase rate of 0. 5°C. Baseline, threshold cycles (Cq values), and results were determined using the Bio-Rad CFX manager software (v3. 1) according to the manufacturer's instructions. The qPCR reference genes used (elongation factor and actin) were taken from  Schmidt and Delaney (2010). All other primers were produced in Primer3 on sequences from the NCBI database except those used to identify the alternate splicing of ThiC primers  (Wachter et al. , 2007). Confirmation of the correct PCR product being synthesized was done by running the qPCR samples on an agarose gel and from qPCR melting temperature analysis.
The new open conformation is kinetically separated from the closed one. To further investigate the stability of the new cavity beyond the results of enhanced sampling simulations, we performed 32 additional unbiased MD simulations starting from configurations of basin S2. 8 representative structures were extracted Figure  1. Crystallographic structure of the HIV-1 protease dimeric complex (pdb entry 1AID  23  ). The enzymatic cavity and the three main regions forming the interface are indicated. from state S2, with intermonomer distance between 3. 2 and 3. 8 nm and a backbone RMSD between 7 and 15 Å from the crystallographic complex. From each structure, 4 independent equilibrium simulations at = T 300 K were run for 100 ns. In 26 out of 32 cases, after 100 ns the dimer remained in conformation S2, with an open secondary cavity.