dataesr/scientific-paragraphs-categorization huggingface dataset:
1. These models were then processed through ClearASIL and the impact the phase had on the mass and period of the planet detected is shown in Fig. 8. When the phase of the planet is less than 0. 5 the code is able to successfully remove the spot signatures to reveal the 1 MJ planet with a 4 day orbital period. However when the planet phase is set to 0. 5 or greater the code struggles to uncover the correct planet signal. This is due to the interplay between the planet signal and the spots causing the code to struggle when fitting the RV data. Increasing the data size would help the code determing the correct planet signal in cases like this. This next test case is to show the ability of the code on uncovering a planet signal that is the same as the stellar rotation period. Again the same model star with 1 spot covering 5% of the stellar surface was used but this time the injected planet signal had mass of 1 Mj and a period of 3.
2 days (i. e. the same as the stellar rotation period). The initial semi-amplitude of the RV signal was 299±5. 1 ms -1 which completely masks the RV signal of the planet (see Fig. 9 ). The spot removal code was able to successfully reduce the stellar jitter to reveal a periodic RV variation with a semi-amplitude of 51. 5±1. 4 ms -1 which closely matches the RV signal of the injected planet (see right panel of Fig. 9 ). Fitting these RVs with a sine wave reveals a 0. 98±0. 04 MJ on a 3. 2±0. 04 day orbital period, indeed confirming the results are consistent with the same as the planet injected into this model. This highlights the ability of ClearASIL to uncover the correct planetary parameters even if the planetary orbital period is the same as the stellar rotational period.
appear in the residuals. This results in the code being unable to isolate this spot feature and remove it from of the line-profiles. Although this is annoying it does not have any impact on finding planets that could be hidden in the data. Not removing this spot feature from each of the line-profiles will create the same apparent RV shift in all the line-profiles and so will effectively change the systemic velocity. Any true shift in the centre of the line-profiles due to an orbiting planet will still be visible as is shown in the following case when a planet is added to this data. As with the previous 1 spot case, we decided to test the code further by inserting a 1 MJ planet on a 4 day circular orbit into this multiple spot model. Fig. 11  shows the RV curve for this model, which has a semi-amplitude of 87.
Variable Spot Model The previous models discussed have shown ClearASIL works for single and multiple spot scenarios. However, in both cases the line-profiles have been evenly spaced over one stellar rotational period. In reality, observations will be spread out due to limitations caused by telescope availability and the stars' visibility. So we decided to further test the code on unevenly sampled data. In this case the model star (M * = 1. 05 Msun, R * = 0. 95 Rsun, T ef f = 5657 K, Prot = 3. 2 days and i = 51. 8 • ) was used to generate 16 spectra with varying spot features that covered the time-span of the RV observations taken to confirm the transiting nature of WASP-39b  (Faedi et al. 2011). Note that we generate one model star for each of the 16 spectra with the spot features set to correspond to that particular observation.
HD49933 With Fake Planets Currently HD49933 has not been found to host any planets as has been confirmed by our results in the previous section. In order to test how a planet would affect the removal of spots with ClearASIL we again injected fake planets with a range of orbital periods into the HD49933 data. The size of the planet was set to ∼5 Jupiter mass. This should be easily detectable by our code which was able to reduce the stellar jitter by 80%. The orbital period of the planets were limited to less than 3 days due to the data only spanning 8. 5 nights. Fig. 16  shows the period and mass of planets found by the code in comparison to the model planets injected into the data. The error bars in this plot are the difference between the injected planet signals and the planets found when using ClearASIL on the data.
They reveal the code to be more accurate for short period planets than for longer period planets which is to be expected due to the short time span of the data. The plot also displays some scatter for the planet mass showing that the code struggles to get the right planet size. This is simply due to the star being very active with multiple spots at any one particular time, making it difficult for ClearASIL to accurately fit out all the spot features. These problems could be overcome with more data points. Increasing the number of data points improves the average psuedoimmaculate profile enabling any spot features present to be more accurately isolated and removed. To show that this is the case we tested the code when a 1 Jupiter mass planet on 2 day orbital period was injected into the original HD49933 data.
FUTURE WORK ON CLEARASIL ClearASIL is an effective method for distinguishing between stellar activity and planets as shown from the results in Sections 4 5. However, there are further improvements that can be made to this technique. Currently, the code uses a model line list for the LSD procedure. The accuracy of line lists to model real spectra depends upon the precision of the model atmospheres to obtain the wavelengths and depths of intermediate-strength lines. However, as  Barnes (2004)  pointed out, these model atmospheres become less accurate for cooler stars and so the line list differs more from the true spectrum which can cause problems such as continua sloping in the deconvolved profile. A way to overcome this issue would be to use a template spectrum of a slowly rotating star with the same spectral type as the target for the deconvolution.
These high SNR line-profiles are then used to isolate spot features by subtracting the average line-profile from each individual profile. By averaging the line-profiles any features in each individual line-profiles are smeared out to generate a 'pseudo-immaculate' line-profile. A better method to generate an immaculate line-profile would be to produce a model rotationally broadened line-profile for the star using its vsini. No spots would be present in this model line-profile and so subtracting it from the individual line-profiles would enhance the isolated spot features. By improving the accuracy of the modeling and removal of any spots present the code would be able to solve the systemic velocity shift problem mentioned in Section 4. 3. Most importantly for the application of our technique to real world data, the model planet fit assumes that only one planet is present in the data and that this planet has zero orbital eccentricity.
CONCLUSIONS We have described a new technique for removing stellar jitter in order to aid the discovery of planets around active stars. Clearing Activity Signals In Line-profiles (ClearASIL) uses high SNR line-profiles to isolate and remove spot features directly from the line-profiles themselves, enabling any planet signatures that are present in the data to be uncovered. The ability of this method to remove stellar jitter was tested on model data with varying spot features. The results showed that ClearASIL was able to remove over 80% of the stellar jitter, similar to techniques such as harmonic decomposition  Boisse et al. (2011). 1 Jupiter mass planets were then injected into these models to test whether the code could accurately remove the stellar jitter while revealing the injected planet signal.
As this object has no known planets in orbit we injected a 5 Jupiter mass planet into the data to show the code's ability to uncover planet signals at various orbital periods. The code was able to uncover the planet signal however the accuracy of the planet parameters varied in relation to the orbital period. As the orbital period of the planet was reduced, the accuracy of the planet parameters established by ClearASIL improved. This is expected due to the size and sampling of the data set. Further work is being undertaken to fully explore the parameter space this technique can cover e. g. what vsini works best, the effect of planet mass and different spot models on the accuracy of the results etc. However this current paper shows that this technique does work and can obtain results with only one major requirement and that is the vsini of the star needs to be within 10 to 50 kms -1 in order to be able to resolve the spot in the stellar line-profiles and accurately measure the RVs.
Indeed we recently found that AbrB2 represses the transcription of the hox operon through binding to the hox operon promoter  [17]. Mutants totally or partially depleted of any of these regulatory proteins were analyzed with DNA microarray, which however were not pan-genomic in covering (at most) only 2,852 chromosomal genes out of 3,317 (about 85%) and none of the 408 plasmidic genes of as yet unknown functions  [11, 15, 18]. Consequently, we developed truly pan-genomic (oligonucleotide) DNA microarray to truly study the genome-wide transcriptome responses to environmental stresses and/or analysis of Synechocystis strains engineered for the production of biofuel. In addition we have developped SVGMapping an R package to facilitate the analysis of the large number of omics data and visualize them onto custom-made templates depicting genome organization, metabolic pathways and cellular structures.
Using sequentially the Earray and OligoArray softwares (https://earray. chem. agilent. com and http://berry. engin. umich. edu/oligoarray2_1) we designed new and truly pan-genomic oligonucleotide DNA microarray containing a relevant part of the protein coding sequence (CS) of all chromosomal and plasmidic genes (TranSyn6803 project). Following the classical strategy, we used three, two or one 60-mer oligos, depending on the CS length: ≥ 500 bp; 500 to 120 bp; ≤ 120 bp. We performed three independent biological replicates in exponential growth and RNA isolation as we previously described  [18]  for our mutant abrB2  [17]  and the wild type (WT) strain. One dye swap was carried out for each biological replicate. RNA isolation was performed by RNeasy Midi kit (Qiagen), cDNA synthesis by reverse transcriptase superscript II kit, dNTP and aminoallyl-dUTP (Invitrogen), cDNA purified with by QIAquick PCR Purification kit (Qiagen), tagging with Cy TM Dye Post-labelling Reactive Dye Pack (Life Sciences), and synthesis of DNA microarray, hybridization and scans were accomplished by using Agilent Technologies.
Microarray signals and data were analyzed with the Limma package of the R/Bioconductor software  [19]. Microarray spot intensities were normalized by subtracting the background and using the LOWESS method with the smooth parameter set to 0. 33 as recommended  [20]. Normalized measures served to compute the ratios of Cy3/Cy5 intensity and the associated log2-transform (denoted log2-ratios) for each gene. Then, to identify genes with log2-ratios significantly different between the mutant and WT strain, p-values were calculated for each gene using a moderated t-test based on an empirical Bayes analysis, which is equivalent to shrinkage (or expansion) of the estimated sample variances towards a pooled estimate, thereby resulting in a more stable inference. p-values were adjusted for multiple testing  [21].
CONCLUSION We report on the development of truly pan-genomic (oligonucleotide) DNA microarray to study the genome-wide transcriptome responses of the best-characterized cyanobacterium Synechocystis to environmental stresses and/or to reprogramming for the production of biofuels. In addition we have developed SVGMapping an R package to facilitate the analysis of the large number of omics data and visualize them onto custom-made templates depicting genome organization, metabolic pathways and cellular structures. Using these tools, we characterized and compared the transcriptome profiles of our abrB2 deletion mutant, with an improved hydrogen production  [17] , and of the WT strain. We show here that AbrB2 is a master regulator that regulates (mostly negatively) a large number of chromosomal genes operating in hydrogen production, regulation, metal transport and protection against oxidative stress, as well as numerous plasmid genes of as yet unknown function.
ACKNOWLEDGEMENTS This work was supported by the  Agence Nationale de la Recherche  Grant  ANR-09-BIOE-002-01  (EngineeringH2cyano) and  CNRS  ( Centre National de la Recherche Scientifique )  Programme Interdisciplinaire Energie PIE2 (Reprogramhydrogen ). We also thank  Cécile Fairhead , leader of « plateau technique  » ArraySud  for her help in the design of oligonucleotides used for DNA microarray.
Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool-Seq) is becoming a popular strategy for genome-wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool-seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool-Seq data. We introduce Pool-hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool-Seq sample. Pool-hmm includes several options that allow a flexible analysis of Pool-Seq data, and can be run in parallel on several processors. Source code and documentation for Pool-hmm is freely available at https://qgsp. jouy.
Uncertainty concerning the true allele frequencies in the pool, which might typically be higher for sites with low coverage or bad quality scores, is thus taken into account in the analyses. Possible biases arising from unequal DNA concentration or quality among individuals are not accounted for by this method, but these effects are expected to be limited for large sample sizes  (Futschik et al. 2010). In this work, we propose a Python program, denoted Pool-hmm, that implements the method of  Boitard et al. (2012). The two main applications of this program are AFS estimation and detection of selective sweeps, in a given region. These two applications are implemented independently, so it is possible, for instance, to detect selective sweeps based on a background AFS that is specified by the user.
g. introns vs. exons). We provide a script that filters the input pileup file for any feature present in an annotation. gtf file. Pool-hmm then infers the AFS based on the filtered pileup. Note that the allele frequencies considered by Poolhmm are sample allele frequencies (from 0 to n) and not population allele frequencies (from 0 to 1), see for instance the AFS in Fig. 1. Population and sample frequencies are closely related and essentially provide the same information, but inference based on coalescent theory, as the derivations of  Nielsen et al. (2005)  that are used in our sweep detection model, naturally involve sample allele frequencies.
Conclusion Pool-hmm is the first software tool for Pool-Seq data that provides a probabilistic allele frequency estimation and detects selective sweeps on a genomic scale. The implemented statistical algorithms account for two important features of Pool-Seq data, the random sampling among chromosomes within the pool and sequencing errors. Pool-hmm includes several options that allow a flexible analysis of Pool-Seq data. *Python pool-hmm. py -input-file quail -n 20 -a 'reference' -only-spectrum -theta 0. 005 -ratio 50. †Python pool-hmm. py -input-file quail -n 20 -a 'reference' -pred -spectrum-file quail -k 0. 0000000001. ‡Python pool-hmm. py -input-file quail -n 20 -a 'reference' -pred -emit-file -k 0. 0000000001. §Python pool-hmm. py -input-file quail -n 20 -a 'reference' -estim -spectrum-file quail.
Numerous image analysis software are now available  [11]  many of which are open-source  [12]. In experimental ecology, such progresses enables the acquisition of large amounts of pictures and the tracking of individual behaviour, fecundity or growth trajectory on a fine time scale and over long periods of time. The measurement itself can be made manually on a computer using appropriate image analysis software such as ImageJ  [12, 13]  to estimate egg and body sizes for instance  [5, 14]. Massive image capture and analysis can also be used to follow the size and structure of an experimental population. But even on pictures, measurements remain time-consuming and may quickly become impractical. Reliable and reproducible automatic counting and measuring methods are then needed. Various authors have designed and proposed image analysis methods to automatically measure or count small organisms in the laboratory  [15] [16] [17] [18] [19].
It requires simple material (a digital camera or a webcam, a stand and a good lighting device) and an image processing program. Here we used the open-source ImageJ software  [13]. We first describe the different steps of the image segmentation using a laboratory population of the springtail Folsomia candida (Collembola, Isotomidae) as a model microcosm  [23]. The same method is applied using different environments to illustrate the variety of biological models and questions that can be tackled with it. We then give some quantitative assessments of the method's reliability and robustness using again the collembolans as a case study. Finally, we describe an ImageJ plugin that we have developed to automate batch laboratory population census.
1 to 3 mm) into 145 classes, each of 0. 02 mm width, and analysed the number of individuals in these classes using a generalised additive model (GAM, function gam of the mgcv library in R) with a Poisson distribution, a cubic regression spline, and an imposed high number of knots (k = 20) to fit the data closely. We then tested differences between the pictures and the stacks of images using anova and Chi-squared tests. Are the counting measurements reliable?. To test whether our counting method is reliable, we compared -for several collembolan densities -the number of particles automatically counted using the background removal method with a measurement made manually. Ten sets of 20 pictures each were analysed automatically to measure the mean number of particles and their total surface. The number of individuals was also measured manually on one picture belonging to each set (Figure  6c-d ).
Automated Implementation We present an ImageJ plugin called BP_sensor for ''Batch population sensor'' that we have developed to automate the measurements of size and structure in many replicated laboratory populations raised in experimental microcosms (Figure  S1 ). This plugin was specifically designed to track populations of the Collembola Folsomia candida but it is versatile enough to be easily adapted to other experimental setups (Figure  S2 ). It automatically performs the recursive census of multiple laboratory populations of collembolans (Figure  S3 ). The code is written in Java and runs within the freely available and open-source ImageJ software  [13]  (http://rsbweb. nih. gov/ij/). We provide in the Supporting Information the commented Java code (File S2) that can be directly compiled and run with ImageJ as well as a set of image examples (File S3) and some explanations (File S1) to help understand how it works and how it can be customised (see Table  S1  in File S1).
This research was supported by a  French grant  from the  Programme interdisciplinaire du vivant ''Longeviteet vieillissement '' funded by the  Centre National de la Recherche Scientifique (CNRS)  and by a grant from the  French National Research Agency  ( ANR EvoRange ), reference  ANR-09-PEXT-011. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
RNA-Seq and Data Processing to Call Chimeric Transcripts Sections (10 × 10 μm thick) were cut from frozen tumor blocks. RNA extraction was performed using TRIzol reagent (Life Technologies, Zug, Switzerland). RNA integrity was validated by 2100 Bioanalyzer (Agilent Technologies, Basel, Switzerland). Paired-end (PE) RNA-Seq was conducted at Prognosys Biosciences (La Jolla, CA) using the Genome Analyzer II from Illumina (San Diego, CA). Library preparation was done according to existing protocols  [13]  with the following parameters: 550-bp fragment length of the cDNA library and 50-to 100-bp read length. In total, 298 Mio reads were generated, of which 151 Mio (50. 8%) reads were mapped to the human genome reference sequence (hg18) and a custom-generated splice junction library based on UCSC Known Genes annotation data set (Table  W2 ).
We allowed for up to two mismatches per read. We also discarded reads that could be aligned to more than five distinct locations in the genome. If a read could be mapped to up to five locations, the one with the best score (i. e. , less number of mismatches) is selected. As part of a standardized RNA-Seq analysis procedure in the Gerstein Lab at Yale University  [22] , the data were made anonymous and genome-wide gene and exon expression values [reads per kilobase of the gene model per million mapped reads (RPKM)] were calculated for all six RCCs. The PE reads were then processed through FusionSeq. All steps in this analysis pipeline are described in detail by Sboner et al. [18]. In brief, the program aligns all reads to the human genome (hg18) and searches for PE reads where one read maps to gene A and the other read maps to gene B.
Gene Expression Profiling from RNA-Seq Data We calculated gene expression from RNA-Seq data as proposed by Mortazavi et al. [23]. All genome-wide RPKM values were quantile normalized. The composite exon model used here is described in detail elsewhere  [18]. For gene expression analysis by RNA-Seq, we selected 80 genes from the renal translocation carcinoma signature published by Camparo et al. [17]  and calculated the fold change expression in our tRCC compared to the mean expression level of the five ccRCCs. Three gene IDs from this signature could not be found in our gene list. Hierarchical clustering was done with the MultiExperiment Viewer (MeV; Dana-Farber Cancer Institute, Boston, MA). Significance (P <. 05) was calculated by one-way analysis of variance (ANOVA) and Bonferroni correction.
Validation of Protein Markers by IHC We selected EEF1A2, MUC1, CNTN3, SV2B, NTSR2, TRIM63, and IGFN1 because these genes were significantly differentially regulated and antibodies suitable for paraffin IHC were available. For the putative TFE3 translocation and TFE3-expressing/nontranslocated RCC marker proteins SV2B (NBP1-46368; Novus Biologicals, Cambridge, United Kingdom), NTSR2 (LS-A1265; LifeSpan Biosciences, Seattle, WA and ab48273; Abcam, Cambridge, United Kingdom), TRIM63/MURF1 (AP16114PU-N; Acris, Herford, Germany), and DKFZp434B1231/IGFN1 (H91156-A01; Abnova, Heidelberg, Germany and HPA039566; Sigma, St Gallen, Switzerland), we were not able to establish working IHC procedures. IHC for EEF1A2 (Human Protein Atlas, sc68481, 1:50; Santa Cruz Biotechnology), MUC1 (NCL-MUC-1, clone Ma695, 1:50; Novocastra, Nunningen, Switzerland), and CNTN3 (Human Protein Atlas, NBP1-89971, 1:50; Novus Biologicals) revealed reliable results with breast cancer, normal lung, and normal uterus as positive controls, respectively.
Interestingly, five SFPQ-TFE3 tRCCs were intermingled in a cohort of more than 400 histologically diagnosed ccRCC cases  [16] , demonstrating the difficult differential diagnosis between ccRCC and TFE3 tRCC, because both tumor types share some morphologic features. SFPQ-TFE3 fusions are also found in perivascular epithelioid cell tumors; the case reported by Tanaka et al. [31]  located to the gastrointestinal tract and expressed a fusion transcript between SFPQ exon 7 and TFE3 exon 5. In our attempt to identify novel gene fusions in RCC, we were confronted with some low-score gene fusion candidates. Although we reconciled with the Mitelman Database of Chromosome Aberrations Figure  3. TMED6-COG8 (TC) read-through in RCC. (A) DNA: Schematic representation of the genomic structure of TMED6 and COG8 on the DNA (-) strand.