dataesr/scientific-paragraphs-categorization huggingface dataset:
Internet-based self-help interventions offer potential solutions to these barriers. Immediately accessible and less costly, online interventions may offer a valuable alternative to face-to-face therapy. Previous studies and meta-analyses have demonstrated unguided Internet-based self-help interventions to be effective for common mental disorders, with a pooled effect size of 0. 28, but dropout rates are high  [8]. CBT programs have been successfully delivered on the Internet  [19, 20]. However, to our knowledge, no study has examined the effectiveness of Internet-based IPT. The present study examined the effectiveness of Internet-delivered IPT and a new Internet-delivered CBT module (from e-couch  [21] ) compared to an online CBT intervention (MoodGYM). MoodGYM was originally developed for youth, but has known efficacy in reducing depressive symptoms in adults  [22] [23] [24].
The trial was designed within a noninferiority framework. Noninferiority trials are used when there is clear evidence of efficacy for an existing standard treatment, such that it is ethically unacceptable to employ a placebo or inactive control group  [25]  and when a new treatment is hypothesized to have comparable, but not necessarily superior, effectiveness to the established intervention  [26]. We hypothesized that the new Internet-delivered modules of IPT and CBT would be noninferior to a CBT module (MoodGYM) in reducing symptoms of depression and anxiety. We also predicted that the Internet-delivered IPT module would be rated by its users as being as feasible, acceptable, and satisfactory as MoodGYM.
Participants and Procedure This automated, 3-arm, fully self-guided, online noninferiority trial compared 2 new treatments (IPT and CBT) to an active control treatment (MoodGYM) for depressed individuals. The Internet-delivered CBT and IPT interventions (from e-couch) were developed at the Centre for Mental Health Research (CMHR) at the Australian National University (ANU). The e-couch program targets a range of conditions currently (depression, generalized anxiety disorder, social anxiety disorder) with other conditions to be added in the future. It also provides modules for bereavement and loss, as well as divorce and separation. It comprises a mental health literacy component and psychotherapeutic components for each condition (eg, CBT, IPT, applied relaxation, physical activity, and behavioral activation for depression).
Effectiveness and Noninferiority Results are presented for 3 groups: all participants (all those enrolled in the trial, ITT), completers (those completing online surveys at posttest and at 6-month follow-up), and adherent completers (those completing ≥50% of the modules). For completers, the within-group effect sizes on the primary outcome measure CES-D were large for all treatments at posttest (IPT d=0. 76 vs CBT d=0. 87) and follow-up (IPT d=1. 02 vs CBT d=1. 44). Between-group effect sizes were small (posttest: IPT vs MoodGYM d=0. 14, 95% CI -0. 06 to 0. 35; CBT vs MoodGYM d=0. 05, 95% CI -0. 17 to 0. 26; follow-up: IPT vs MoodGYM d=0. 18, 95% CI -0. 09 to 0. 45; CBT vs MoodGYM d=0. 12, 95% CI -0. 15 to 0. 39). Within-group effect sizes for adherent completers ranged from d=0. 74 to d=0.
This could suggest that the new programs are more acceptable, particularly to adults. Within a noninferiority framework, this finding is very important, because newer implementations of e-therapy were at least as effective as MoodGYM, whereas completion rates-a key problem in this field [51]-were higher. However, MoodGYM is a well-known open-access program. Some participants assigned to MoodGYM could have undertaken the program previously, and if so may have been less willing to finish the intervention.
Acknowledgments This study was funded by the  Faculty of Psychology and Education of the VU University, Amsterdam , and the  Center for Mental Health Research at The Australian National University. HC is supported by  NHMRC  Fellowship  525411. KG is supported by an  NHMRC   Senior Research Fellowship No 525413. We wish to thank  Ada Tam ,  Philip Batterham ,  Jos Twisk ,  Bob Forrester ,  Filip Smit , and  Lisanne Warmerdam  for their helpful comments and participation in the project.
e. for any other potential miRNA r 2 within the stem, the free energy associated to r (together with its complement r Ã ) is lower than the one of r 2 (with r Ã 2 ). The MFE is computed with RNAeval  (21). Thresholds in condition 2 are less strict than the ones used to validate pre-miRNA structures in  (13). This choice is due to the fact that, here, we consider the structure of several pre-miRNAs all together instead of just one. The thresholds are computed as in (  13 ) by looking at the distribution of distances between r and r Ã (defined as jlðrÞ À lðr Ã Þj=lðrÞ for miRNAs in the miRBase data set and by using at least +3 as an acceptable distance, where m and s are mean and standard deviation of the distribution). The threshold on the percentage of unmatched nucleotides is computed as 0 +2 0 , where 0 and 0 are mean and standard deviation of the distribution of values computed on the miRBase v14 data set.
Functional analysis of targets To realize a functional analysis of the potential targets of our predicted miRNAs, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6. 7  (33, 34). Given a set of RefSeq mRNAs containing potential targets, DAVID extracted, whenever possible, those gene ontology (GO) terms classified as biological processes (BP) and molecular functions (MF), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Protein Information Resource (PIR) keywords that were over-represented in the set of genes. The results of the analysis are reported in the folders in Supplementary Data Set 4, where one finds a list of enriched functions, the associated P-value (DAVID EASE score), the fold enrichment value and the Benjamini-corrected P-value obtained after multiple testing corrections.
Data sets Known miRNA sequences were retrieved from miRBase v. 16 at www. mirbase. org (collecting miRNAs from 142 species), and we used them for making the analysis based on paralogous sequences. The miRBase v. 13 (corresponding to the human genome assembly that we analysed) was used to determine the coverage of miRNAs lying in fragile sites and structural cluster regions  (23) (24) (25). Several sets of deep-sequencing reads were used to predict structural clusters. Data sets were retrieved from the Gene Expression Omnibus database at NCBI (http:// www. ncbi. nlm. nih. gov/geo/) and from the Sequence Read Archive at NCBI (http://www. ncbi. nlm. nih. gov/sra/ (see Supplementary Table  S1  for accession numbers). Among deep-sequencing data coming from the Sequence Read Archive, some were extracted from breast cancer cells (SRR015446, SRR015447 and SRR015448), and the others from 12 melanoma and pigment cells.
Reads coming from Gene Expression Omnibus archive, originate from cell lines derived from cervical cancer cells (GSE14362 and GSE10829), small RNAs from human embryonic stem cells, derived neural progenitors and neurons (GSE13483), endogenous small RNAs associated to human Argonaute 1 and 2 (GSE13370). Predictions are made by putting together reads from all experiments, but the origin of the deep-sequencing read appearing in a predicted structural cluster is indicated in Supplementary Data Set 2. Sets of deep-sequencing reads were mapped to the human genome and filtered with MicroRazerS (29) that imposes that the same sequence cannot match more than five different positions in the genome. Three data sets of genes involved in different biological pathways have been used: (1) KEGG PATHWAY database: it is a collection of manually drawn pathway maps representing the knowledge on the molecular interaction and reaction networks for metabolism, genetic information processing, environmental information processing, cellular processes, organismal systems and human diseases  (37, 38).
Implementation and outputs The program, called MIReStruC (standing for 'miRNA Structural Cluster'), has been implemented in bash, C, Awk and Python. It is available at the address http:// www. ihes. fr/$carbone/data9/. Parameters and default values are described in the Supplementary Data Set 1. The tool uses several published tools. The tool MiReNA is found at http://www. ihes. fr/$carbone/data8/. It has been used with the same default values for thresholds set in  (13)  with the exception of thresholds for criteria V set to 0. 69 and criteria III set to 28. To produce secondary structures, MiReNA uses an adapted implementation of RNAfold  (21). The MFE value of two miRNAs matching is obtained using RNAeval  (21). The list of predicted human structural clusters is given in Supplementary Data Set 2.
) The number of known miRNAs is computed on the miRBase data set. The number of known human miRNAs is given in parenthesis. A full set of information, organized by chromosome, is reported in Supplementary Table  S3. The total number of predictions obtained by the three methods is reported in the last line. Identical predictions (see 'Materials and Methods' section) are counted once. See Supplementary Figure  S3. The hairpin arm giving rise to the dominant mature miRNA can switch in different tissues and in different developmental times as observed in many species  (53) (54) (55) (56) (57)  and possibly generate the miRNA and the miRNA* with a simultaneous functional regulation  (51). In this regard, notice that the accumulation of short reads indicating the mature miRNA was not used by our method to predict miRNAs.
Transcriptional regulation is highlighted by the analysis of BP GO terms (P < 1:4e À13 ), MF GO terms (P < 2:4e À13 ) and PIR keywords in the Swissprot database (P < 2:4e À17 ). This suggests that predicted miRNAs might be involved in the degradation of transcription factors, as it is the case for the two already known miRNAs of chromosome 13 regulating protein E2F1 and being regulated by c-Myc that also regulates E2F1  (17). Another important class of proteins identified by the analysis is the one involved in molecular binding. It concerns binding to various molecules (ion, cation, DNA); it is very well represented and statistically significant (see MF GO terms in Table  4  and Supplementary Data Set 4). By considering KEGG pathways, we obtain that 'pathways in cancer' has one of the smallest P-values (P < 5:4e À9 ), and that 14 pathways corresponding to different types of cancers are ranked as statistically significant among all pathways.
This indicates an involvement of our predicted miRNAs in cancer development. Notice that 'melanogenesis' (P < 1:1e À5 ) and 'melanoma' (P < 2:8e À5 ) pathways are identified, and this should be understood by keeping in mind that we used Chromatin Immunoprecipitation Sequencing (ChIP-seq) data from skin cells for structural clusters prediction and that 115 miRNAs (20%) targeting 3 0 UTRs associated to 'melanogenesis' and 'melanoma' over a total of 550 are contained in structural clusters identified using deepsequencing data. In agreement with this analysis, PIR keywords analysis of the Swiss-Prot database identifies 'disease mutation' (P < 1:6e À13 ) as statistically significant (see KEGG and Swiss-Prot in Table  4  and Supplementary Data Set 4). The analysis of the Swiss-Prot database identifies 'alternative splicing' (P < 4:7e À74 ) and 'phosphoproteins' (P < 2e À59 ) as the most significant outcomes, suggesting the involvement of predicted miRNAs in other forms of regulation (see Table  4 ).
Finally, we notice that several of the pathways that we had obtained in the functional analysis of structural cluster regions also appear as significant. Among them, there are apoptosis and several important signalling pathways (P53, WnT, MAPK, Hedgehog, mTOR, VEGF, Notch) (Table  4  and Supplementary Data Set 4). For the small set, the signal is stronger than for the large set of pairs: BP GO terms associated to 'regulation of' cover >48% of terms for P < 0:01 and >45% for P < 0:1. This confirms the implication of predicted miRNAs in regulatory functions. The same observations on transcription regulation and binding activity of the targets associated to MF GO terms and PIR keywords in the Swiss-Prot database hold true (see Supplementary For 3 0 UTR and CDS regions, we report the number of miRNA/target pairs, the number of 3 0 UTRs or CDSs containing targets, the number of miRNAs for which targets are predicted, the number of different predicted structural clusters (416) with at least a miRNA that has a target, the number of different genes involving targets (DAVID_IDs), the number of GO terms characterizing the best miRNA/target pairs and found in BP and MF GO classes.
The same analysis is reported for the KEGG data set and the PIR keywords of Swiss-Prot. For the four data sets, GO, KEGG and PIR terms with P-value, P < 0:1 or P < 0:01 are counted. The number of predicted structural clusters containing at least two or three miRNAs that target either 3 0 UTR or CDS regions is reported. Both large and small sets of miRNA/3 0 UTR and miRNA/CDS pairs are considered and for those the total number of predicted structural clusters with at least one miRNA that targets 3 0 UTR or CDS regions is given. Data Set 4). Again, 'alternative splicing' (P < 3:07e À28 ) is the most significant outcome of the PIR keywords analysis in Swiss-Prot database. By considering KEGG pathways, 12 pathways corresponding to different types of cancers are highlighted as statistically significant, and this confirms the implication of predicted miRNAs in cancer development.
The most significant outcomes are listed for GO (BP and MF), KEGG and Swiss-Prot databases. Pathways related to regulation (orange), binding (blue), signalling (pink), cancer (green) are highlighted. whole structural cluster, has no good target, a prediction may be eliminated. In this respect, it is important to observe that most predicted structural clusters contain miRNAs with targets displaying a high miRNA/target binding energy (Table  3 ), and that many miRNAs targeting genes belonging to the same functional class co-exist within the same structural clusters (Table  5 ). Also, miRNA prediction methods do not present differentiated functional classes of targets. The functional analysis on miRNAs predicted by paralogous sequences and by deep-sequencing data taken separately, provides comparable results to those described earlier in the text (compare predictions based on deep-sequencing data to all predictions in Table  5 , and see Supplementary Data Set 6 for the differentiated analysis on the large set).
Among targets obtained from deep-sequencing data, we observe a stronger signal of pathways in cancer and melanogenesis obtained for the KEGGs data set in agreement with the For each pathway, the number of mRNAs in the pathway containing at least one target and the number of miRNAs with at least one target in these mRNAs are reported in the last two columns. For the set of miRNAs targeting genes associated to a specific pathway, we report the number of structural clusters (SCs) containing at least one of the miRNAs in the set (second column), the number of SCs with all their miRNAs in the set (thirrd column) and the ratio of these two numbers (fourth column). Pathways with a ratio ! 30% (blue) and ! 40% (green) are highlighted. In the second and third columns, the numbers in parenthesis correspond to structural clusters predicted with deep-sequencing data.
Structural cluster regions and functional chromosomal organization We studied whether structural clusters are located in chromosomal regions containing genes involved in specific biological pathways, possibly implicated in human diseases, and analysed the KEGG database  (37, 38). To evaluate the amount of genes in the proximity of structural clusters, we computed the coverage of functionally related genes within structural cluster regions for gradually increasing region sizes. (Histograms describing the distribution of structural cluster regions for 25% chromosomal coverage are reported in Supplementary Figure  S4. ) P-values are computed on the associated curves. This analysis has been exhaustively carried on subclasses and pathways of all KEGG's classes: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism and organismal systems (Supplementary Table  S1 ).
ACKNOWLEDGEMENTS The authors thank  Dave Du Verle  for providing the software searching for palindromic regions within a genome, and  Anne Lopes  for comments on the manuscript. A. M. participated in the design of the study, implemented the algorithm, performed the in silico experiments and participated in their analysis. A. C. conceived the study, participated in its design and coordination, analysed the data and wrote the article. All authors read and approved the final manuscript.
1e À4 Melanogenesis 78 1. 1e À5 1. 381981247 2. 3e À4 mTOR signalling pathway 45 1. 9e À5 1. 517930586 3. 7e À4 Pancreatic cancer 59 1. 9e À5 1. 437349086 3. 5e À4 ErbB signalling pathway 69 2. 5e À5 1. 391145579 4. 2e À4 Melanoma 58 2. 8e À5 1. 432888466 4. 2e À4 Functional analysis based on PIR keywords of Swiss-Prot database Pathways Target P-value Fold Enrichment Benjamini Alternative splicing 4889 4. 7e À74 1. 141082076 4. 80e À71 Phosphoprotein 4692 2e À59 1. 129027815 1. 01e À56 Transcription regulation 1333 2. 4e À17 1. 149882304 8. 1e À15 Transcription 1358 1. 7e À16 1. 145994025 5. 6e À14 Kinase 494 1. 1e À15 1. 254877097 2. 2e À13 Disease mutation 1048 1. 6e À13 1. 151207963 2. 7e À11.
Following irradiation, the medium were replaced by a regular medium. Six days (for PS120 cells) or 10 days (for LS174Tr cells) following irradiation, cells were fixed, stained with Giemsa, and counted using ImageJ® software. Of note for both LS174 and PS120 cells, irradiation was not performed but after two or three cell divisions. This raises the possibility of microcolony formation and overestimation of the number of colonies, however, we used caution to exclude microcolonies from our final analysis. Furthermore, our calculations involve a ratio between experimental clones and control. Therefore, the absolute number of clones is normalized because every condition grows with the same amplitude of error.
7 in the absence (-Tet) or presence (+Tet) of Tet for 8 days before they were irradiated (8 Gy) or not (0 Gy). After irradiation, spheroids were transferred to polyhema-coated 96-well plates containing fresh medium for 5 days. Spheroids were then subjected to Accutase dissociation and individualized live cells (C) and dead cells (D) were counted using trypan blue. The spheroid proliferation index was calculated as the ratio of the number of living cells counted at day 13 to the number of cells at day 0. Data represent the average of three independent experiments. (E) The clonogenic capacity of LS174Tr-shca9/ctl -/+Tet and LS174TR-shca9/ca12 - -/+Tet cells exposed to hypoxia (1% O 2 ) for 48 h in a regular medium, was measured 10 days after irradiation (0,1, 2, 4, 6, and 8 Gy). Dishes were stained with Giemsa (Fluka).
ACKNOWLEDGMENTS The laboratory is funded by the  Ligue Nationale Contre le Cancer (Equipe labellisée) , the  EU-FP7 -" METOXIA ," the  Association pour la Recherche contre le Cancer , the  Institut National du Cancer , the  Agence Nationale pour la Recherche , the  Centre Antoine Lacassagne , the  Centre National de la Recherche Scientifique , the  Institut National de la Santé et de la Recherche Médicale , and the  University of Nice. Scott K. Parks was funded by The  Natural Sciences and Engineering Research Council of Canada (NSERC)  and the  Association pour la Recherche contre le Cancer (ARC). Johanna Chiche  was funded by the  "METOXIA" grant  and the  ARC.
The ratio of flow rate to bath volume ensured complete exchange within 2 min. performed with Clampfit 9 (Molecular Device, Sunnyvale, CA, USA). Data were sampled at 20 kHz and filtered with a cut off frequency of 2 kHz. The resting membrane potential (RMP) was measured immediately after break-in and establishing whole-cell recording. The membrane input resistance (R in ) was calculated by measuring the amplitude of voltage responses to steady hyperpolarizing current steps of increasing intensity, using the Clampfit 10. 0 program (Molecular Device, Sunnyvale, CA, USA). Spontaneous action potential-dependent and independent (miniature) AMPA and GABA A -mediated postsynaptic currents were analyzed using Clampfit 10. 0 (Axon Instruments). This program uses a detection algorithm based on a sliding template.
ncbi. nlm. nih. gov/BLAST/). The negative controls remained negative in all experiments, and no archaeal DNA was extracted from the water used as negative control. The internal plasmid control was detected in all specimens, with median Ct value of 24. 6 for automated protocol; of 23. 9 for semi-automated protocol and of 23. 6 for manual protocol. Likewise, all bacteria detection was positive in all specimens with respective Ct value of 32. 1, 25. 9 and 26. 4. These data indicated the absence of PCR inhibition in any of the tested protocols. All real time-PCR positive product sequences had 99% sequence similarity to the M. smithii reference sequence (GenBank accession number CP000678). This test was used as a positive control for real-time PCR detection. We compared three microbial DNA extraction protocols to identify an optimized protocol to obtain archaea DNA from human fecal specimens (Table  1 ).
Aims. We reanalyze FEROS observations of the star HIP 11952 to reassess the existence of the proposed planetary system. Methods. The radial velocity of the spectra were measured by cross-correlating the observed spectrum with a synthetic template. We also analyzed a large dataset of FEROS and HARPS archival data of the calibrator HD 10700 spanning over more than five years. We compared the barycentric velocities computed by the FEROS and HARPS pipelines. Results. The barycentric correction of the FEROS-DRS pipeline was found to be inaccurate and to introduce an artificial one-year period with a semi-amplitude of 62 m s -1. Thus the reanalysis of the FEROS data does not support the existence of planets around HIP 11952.
(2013)  did not find any evidence for planets around HIP 11952. In this research note we reanalyze the RV signal of the FEROS data to clarify this discrepancy. In a first step we used the star HD 10700 to compare FEROS and HARPS measurements since there are many observations available for this object taken with both instruments. Recently,  Tuomi et al. (2013)  found a signal of five significant periodicities in the RV of HD 10700 that might be caused by a system of five planets with minimum masses between 2. 0 and 6. 6 M ⊕. Since those signals are associated to RV variations below 1 m s -1 , the object is still suited to compare the FEROS  (Kaufer et al. 1999)  and HARPS  (Mayor et al. 2003)  data, taking into account the long-term precision of the FEROS RV measurements of about >10 m s -1.
The comparison reveals that the barycentric correction of the FEROS pipeline data is less acccurate than needed for long-period RV monitoring. We applied a much more accurate correction to the FEROS data of HIP 11952 and show that with this improved correction there is indeed no evidence for planets around this object. The paper is organized as follows: Sections 2 and 3 present the observations and RV measurements of HD 10700 and HIP 11952, respectively. The effect of the inaccurate barycentric correction on FEROS RV measurements is presented in Sec. 4. The conclusions can be found in Sec. 5.