dataesr/scientific-paragraphs-categorization huggingface dataset:
When several such variables need to be controlled for, equation (  5 ) is applied recursively. tember 2011). For each protein-coding gene, amino acid sequences were aligned using MUSCLE  (Edgar 2004 ). The alignments were checked by eye and manually corrected if needed, and then used as a template for aligning nucleotide sequences while respecting the underlying coding structure. All the species names, taxonomic information, gene accession numbers, and the alignments are available on Dryad (http:// datadryad. org, doi:10. 5061/dryad. 72594).
Life-History Traits Body mass for all the bird species analyzed here were obtained from  Dunning (2007). Maximum recorded lifespan (a proxy for longevity) and female age at sexual maturity were extracted from the AnAge database (de Magalha ˜es and Costa 2009). In the case of birds, where longevity and age at sexual maturity were recorded, we used the mean traits at the genus level. In the case of placentals, data for the three LHT were obtained from the PANTheria database  (Jones et al. 2009). For birds, body mass was available for all the 92 species whereas maximum longevity and age at sexual maturity were available for 60 and 44 species, respectively. For placental mammals, body mass was available for 230 species, maximum longevity for 189 and age at sexual maturity for 183 species. The distribution of these traits is shown in supplementary figure  S2 , Supplementary Material online.
0 B FIG. 1. -Pairwise uncorrected divergence against phylogenetic distance estimated on the bird (A) and on the placental (B) data sets. On both panels, gray dots correspond to measures based on amino acids and black triangles to measures based on nucleotides. Phylogenetic distances are estimated by maximum likelihood using CODEML  (Yang 2007 ; mtmam. dat substitution matrix) for the amino acid data, and BASEML (GTR+GAMMA substitution model) for nucleotides.
On the other hand, early placentals do not have especially low Kr/Kc ratios: Kr/Kc is larger in early branches connecting placental orders, than within Rodentia or Eulipotyphla (mean Kr/Kc ¼ 0. 85; CI: 0. 69-1. 1 and mean ¼ 0. 85; CI: 0. 70-0. 99 for Laurasiatheria and Euarchontoglires ancestor vs. mean ¼ 0. 93; CI ¼ 0. 71 and 1. 22 for terminal branches of Rodentia and mean ¼ 0. 72; CI: 0. 63-0. 96 for terminal branches of Eulipotyphla). Such an intermediate Kr/Kc ratio suggests that early placentals were not especially small: indeed, even when taking into account the covariance between body mass and Kr/Kc (which leads to a smaller inferred ancestral body mass), the estimated body mass of the last common ancestor of placentals is bigger than the mass of a majority of extant mammals. The median body mass of extant mammals is 100 g, and only 23 % of the extant species weigh more than 2 kg (N ¼ 3,913 species available in PanTheria database,  Jones et al.
Acknowledgments This work was supported by the ARCAD foundation project (http://www. arcad-project. org/)to B. N. ; the  Natural Science and Engineering Research Council of Canada  and the  Canadian Foundation for Innovation  to N. U. and N. L. ; and the  French Agence Nationale pour La Recherche ,  Ancestrome  project to N. L. This is ISEM publication number ISE-M 2013-068. Computational resources were provided by  Calcul Quebec, Compute Canada , and  Montpellier MBB Platform.
Confocal imaging For confocal imaging, agro-infiltrated tobacco leaves (mounted in water) were directly imaged on a Leica Confocal TCS SP2 (Leica Microsystems). The CFP was imaged with 458 nm excitation using the dichroic mirror DD458/514 and detection window of 465-505 nm; the GFP was imaged with 488 nm excitation using the dichroic mirror DD488/543 and a detection window of 500-580 nm; the RFP was imaged with 543 nm excitation using the dichroic mirror DD488/543 and a detection window of 580-670 nm. For the co-localizations, all of the images were taken by sequential acquisition. Image analysis was performed using the National Institute of Health ImageJ (http://rsb. info. nih. gov/ij/) software.
ACKNOWLEDGEMENTS We thank  Bruno Letarnac ,  Herve Ferry ,  Philippe Marechal  and  Fabrice Petitpas  for plant care. This work has benefited from the facilities and expertise of the  Imagif Cell Biology Unit of the Gif campus  (www. imagif. cnrs. fr),which is supported by the  Conseil Ge´ne´ral de l'Essonne. A. B. M. , F. B. and A. M. carried out the cloning and the imaging experiments;  A. E. Md. A. , H. V. and A. C. M. carried out the genetic and molecular PTGS analyses; M. D. C. , A. C. M. , A. M. and H. V. designed and coordinated the study. All authors contributed to the writing and editing of the manuscript and read and approved the final manuscript.
FUNDING Agence Nationale de la Recherche  [ ANR-08-BLAN-0082  to A. C. M. and A. M. and  ANR-10-LABX-40  to M. C. and H. V. ];  Land Baden-Wu¨rttemberg , Chica und  Heinz Schaller Stiftung  and the  CellNetworks cluster of excellence  (to A. M. ). Funding for open access charge:  Agence Nationale de la Recherche  [ ANR-08-BLAN-0082 ] the Chica und  Heinz Schaller Stiftung.
Global Signal Requirements The problem of measuring the global 21cm signal is one of bandpass calibration of instrument gain and receiver temperature, and not of collecting area or thermal noise. In theory, a single well calibrated dipole can build up the required signal to noise ratio within a day. While single antenna experiments have put interesting constraints on  the global 21cm signal  (Bowman & Rogers 2010) , in practice they suffer from systematic errors introduced by the frequency dependent instrument response. Having an antenna element as part of an interferometric array such as SKA provides significantly better constraints on bandpass gain calibration and estimation of frequency dependent antenna element beam. Moreover an array environment facilitates better algorithms for RFI detection, provides information on ionospheric and multipath conditions for assessment of data quality and facilitates foreground subtraction using interferometric sky images.
Recommendations In this section, we summarize succinctly our recommendations for an optimal SKA array configuration for CD/EoR science requirements. 6. 1 Choosing an optimal and affordable SKA CD/EoR array We conclude that the best strategy for designing an SKA array optimized for EoR/CD science is: (1) Set the largest scale one would like to probe and match the station size to that requirement; additional sky area is then build up via multi-beaming (which will provide more samples, but no data for scales larger than the FoV) (2) Scale the core area to an area that can probe all power-spectrum k-modes of interest and still enable tomography (i. e. imaging) on the smallest angular scales probed by k ⊥ which require somewhat larger baselines. (3) Set the number of stations or collecting area (since the station size has now been fixed) to reach the required tomography and power spectrum sensitivity level and use multi-beaming to reduce sample variance.
At the same time, SKA will also for the first time explore the earlier phases of the Cosmic Dawn, before substantial reionization, probably mostly in a statistical way, but possibly also with low resolution tomography. These studies will teach us fundamentally new things about both the earliest phases of star and galaxy formation, as well as cosmology and even have the potential to lead to the discovery of new physical phenomena. The 21cm signal from neutral hydrogen can be analyzed in different ways. Imaging at different frequencies will give us a tomographic volume with both spatial and evolutionary information. These data sets can be analyzed to characterize the sizes and shapes of ionized regions, as well as the density structures in still neutral regions. This information can also be combined with other probes of the EoR/CD, such as galaxy and QSO surveys, and the different types of background radiation (NIRB, atomic & molecular lines, CMB), allowing us to make the connection between the properties of the galaxies and the effect they have on the IGM.
These fields need to be observed within one observation and cannot be reconstructed from different observations of smaller fields. The redshifted 21cm signal has to be retrieved from data containing strong foregrounds, mostly galactic, but also extragalactic. In order to minimize the effects of foregrounds, the observed fields should be located in areas of low foreground emission and without strong polarization features. The techniques for foreground subtraction will be put to the test on the data from SKA precursors in the coming years. Additional complications to deal with are radio frequency interference (RFI) and effects caused by the ionosphere. Experience with LOFAR shows that even in a radio-loud environment, RFI can be dealt with, provided the telescope has sufficient frequency and time resolution, as well as a sufficient number of ADC bits.
Correcting for ionospheric effects will require reconstructing some of the three-dimensional structure of the ionosphere. For this data from long baselines is important. Since the redshifted 21cm signal is weak and the foregrounds strong, the accuracy of calibration has to be high in order to extract the 21cm signal from the total signal. Apart from dealing with effects caused outside the array, this also puts constraints on the quality of the hard-and software components of SKA. Any effects caused by these components should be small and stable enough to be calibrated out. The active precursors to SKA-low, namely GMRT, LOFAR, PAPER and MWA, have not yet succeeded in detecting any redshifted 21cm signal. Still, the experiences gained in observing at these low frequencies are important for the development of SKA-low.
Opening remarks In his opening remarks, Jim Bristow (Deputy Director of JGI) informed the committee that the JGI would experience its first budget cut in the coming year. He looked to the Advisory Committee to provide guidance on which projects should be triaged and which programs should received greater support in the future. He emphasized that whatever path would be taken, there would be long-term ramifications. The goal of JGI goal is to continue doing big science projects, allowing some of the smaller ones to be handled by smaller centers or individual labs. JGI wants to continue delivering not just raw data, but a complete package, preferably addressing high-profile science while balancing its service function to the community. Interestingly, the most expensive step in the program has been the submission to GenBank.
Continued increase in sequencing throughput and new 'omics data has resulted in a concomitant increase in demand for informatics services, both in support of the CSP Program (i. e. JGI users) and from the broader community wanting to use IMG resources for sequence analysis and annotation. This poses a major challenge and guidance from the committee is sought. Major informatics challenges fall into three areas: sequence annotation, data management and scaleup to accommodate the increase in both the number of genes and genomes that are identified and classified on a weekly basis. In the case of annotation, the lack of standards for gene calling poses a significant problem as different gene calling methods can yield significantly different answers. A multi-center workshop was discussed which sought solutions to the problem.
The approach that was taken was for the participants to download and integrate all publically available proteomics experiments and independently re-annotate the same genomes, then make comparisons. The results of this effort point to specific guidelines in terms of optimum methods to use for gene prediction and are currently prepared for publication by the multi-center consortium. Data management challenges have focused on the integration of the multiple independent IMG databases to a single one and required development of new functionality and incorporation of analytical tools for single cell genomes and Omics data. Integration of metagenomes into file systems also posed a challenge. Work on improved gene annotation is underway, specifically targeting unassembled Illumina data to provide support for handling 250 M to 1.
At present, less than 30% of the protein coding genes can be clustered with available tools (such as Pfam, COGs and KO) and as a result, only this fraction of the genes can be compared across various metagenomic datasets. New methods that would allow the efficient and scalable clustering of all the novel genes identified in metagenomes are needed and are currently under development at the JGI's Prokaryotic program A lively discussion followed Nikos' update, focused mainly on the approaches that could/should be used for protein clustering, how genes and protein families are called and what thresholds should be applied. This confirms the need for standards in this area, but it may still be too early for stable standards to emerge from the community. The apparent overlap between the pangenome and clustering projects was also discussed.
Future Grand Challenge projects were then discussed. High on the list is RAMAN spectroscopy (discussed above). There are three submissions to the ETOP project, one of which will ultimately be selected for reduction to practice. Discussions ensued about the working of this tool and how it can be used to identify various cellular storage products. Data challenges were also discussed, especially the complexity of RAMAN spectra and the need to build a database to make a system useable Standards in Genomic Sciences for strain identification. A second grand challenge that was discussed was deep single-cell sequencing from a single environment. Numerous candidate environments are possible. The Pangenome challenge was then discussed, briefly. While many aspects of the project are similar to isolate sequencing projects, this project would allow one to understand various pathways without having to know the individual strain from which it was derived.
Metatranscriptomes add to the complexity. Project data and metadata are also difficult to view in an integrated fashion. Nonetheless, there are great benefits to using isolate and single cell genomes to both aid in the assembly and interpretation of metagenomes and metatranscriptomes and metagenomes to put genomes and transcriptomes into a larger biological context. Matt described several current CSP projects that combine all elements into a single project. What these projects teach is that the workflow for isolates, single-cells, metagenomes, transcriptomes and metatranscriptomes is separate and involves two -four project managers who may not be communicating with one another and may be working in an asynchronous manner that can span several years. A prolonged discussion followed Matt's presentation, focusing on the need for developing a more unified pipeline and improved communication, not only within the JGI, but also with the users.
FEBA -Functional Encyclopedia of Bacteria and Archaea Jim Bristow briefed the Advisory Committee on the rationale and progress of the FEBA project. He opened by stating that the JGI mission is increasingly slanted towards functionalization of genomic information. The reason for this shift is that the sequence data only tells us who is there in the environment. What we ultimately want to know is what they are doing, and this requires improvement in functional genome annotation. While annotation of known organisms and their determination of their close relatives by homology works well, computational methods fail for the more distantly related Standards in Genomic Sciences species. What is needed are experimental tools that can improve the functional annotation of subsets of genes across phylogenetic space.
IMG and Portals Victor Markowitz updated the Advisory Committee on the Integrated Microbial Genomes (IMG) data management system and the various portals used to access microbial data at JGI. In his opening comments, he stressed several times that the important thing to keep in mind is that IMG is a high throughput system that integrates microbial genome and metagenome data that must often go through multiple iterations. IMG is a gene-centric database. It has seen a steep growth in the total number of genes in the database since these data were first collected in 2006. At the end of 2012, there were records for 29. 1 M genes in the IMG that were derived from the genome-sequencing program and 15. 7B genes from the metagenome program. It provides services to a community of approximately 3,100 users, worldwide, of which approximately one third are JGI collaborators.
While successful, growth in the user base is difficult to sustain because it is not just the increase in the amount of data that must be considered, but also all of the crossconnections that occur as a result of integrating all of the data to provide the end users with a more comprehensive view. The issues for the Advisory Committee to address are the allocation of resources to meet community needs and defining the scope of the intended user community. Maintenance of the underlying code base is also problematic as the IMG relies on a number of open source tools and provides user services via browsers, which are undergoing constant change. The IMG is divided into four basic subsystems of which microbial genomes is one. Data processing begins with data submissions (originally by email but now a fully automated process).
Use of standardized terminologies and concepts are critical to data acquisition, storage and analysis and standards are applied through GOLD and automation is anchored in GOLD. An automated tracking system is now in place, but defining various processes proved far more challenging than software development. Data distribution models have also changed, going from organism specific download sites to a more integrated approach with much of the data being hosted by NERSC. Overall, the IMG system offers a very good system investment by the DOE. IMG content consists predominantly of genomes and metagenomes, which are updated regularly, with 30% of the genomes and 70% of the metagenomes originating from JGI itself. The remainder comes from the broader community. Work is ongoing to adopt/apply community accepted standards.
Architecturally, the IMG has migrated from a collection of distinct project centric systems to a unified system with a suite of domain specific interfaces, backed by a single database. IMG has also migrated from a system built on a relational database, which did not scale well, to a re-engineered system building using FastBit. Workspaces have also been built into IMG to allow users to save gene sets and function sets with computations offloaded onto a supercomputer that informs users when analyses are complete. Although the IMG has evolved into a very useful resource that has kept pace with both user needs/expectations and developments in the field, it has not been without some cost. How to maintain the resource going forward, and in which form, will require making some very hard decisions.
A lively discussion followed Victor's presentation, in response to his request for guidance. Questions he raised include how to balance the expectations of different user communities (JGI/non-JGI) and whether or not parts of the current IMG function would be better served under Kbase. Other questions involved which data should be represented in IMG (i. e. , reference public genomes and metagenomes) and how all of the 'omics data should be integrated. Should IMG be focusing on Standards in Genomic Sciences comparative analysis (expensive) or data distribution (inexpensive), or expert review/curation? A second line of discussion focused on the IMG -NCBI relationship and whether or not IMG could serve as a repository for JGI data. What would the cost be and (tangible and non-tangible) would that fit within the JGI/DOE mission? Would the problem of perceived bottlenecks at NCBI, including delays in release of GenBank genome submissions be solved by such an approach? Could the IMG serve as an alternative host for genome information? Would the IMG eventually suffer from all of the same problems as NCBI? Could the IMG and other major databases stop submitting to GenBank and what would be the consequence?.
Question 2. Community sequencing program There was a consensus among the advisors that the small scale community sequencing program should be continued as it provides a great service to the broad community, but could be done at less frequent intervals than quarterly (two or three times/year). The importance of the annotation service and indexing of sequences and metadata in IMG was considered as an important aspect of the CSP. The second part of the question was whether or not the program should remain open (unconstrained) or if a more focused approach should be applied. The committee response was slightly ambivalent, favoring some constraints to ensure that projects fit well into the overall BER mission. Question 3. Future grand challenge projects 1. Continue the Phylogenetic coverage of isolates and SAGs? -The current CSP call was focused more on functional diversity and single cell genomes as opposed to phylogenetic coverage; however, the committee members thought that the best ap-proach might be to couple the two more closely.
Targeted single cell genomics and metagenomics There was strong support from the advisory panel for the JGI to continue work in these areas. The consensus was that these approaches would have a important role in future functional genomic research. Ed Delong indicated the importance of knowing which organisms were present to interpret metatranscriptomes. 4. Deep single cell and metagenome sequencing from one "flagship" environment -The key will be to pick the "right" flagship. The question is whether a single environment should be selected or would that be too narrow. Also discussed was the importance of such a project to test fundamental concepts of complete integration of all of the techniques and data into a single or small number of well-defined discrete projects to demonstrate the value of applying a systems-biology approach.
Question 4. Expansion of functional genomics projects This was considered an important direction for JGI to go, as it allows layering additional information onto the genome sequences. The next step will be to meet the expectations of the user community. The starting point will be transposon bombing, possibly with as many as 100 organisms/year. Would such a product be useful? Would there be other interaction assays that JGI should pursue? The consensus was that this would be a valuable addition to the JGI product portfolio. However, there is a need to balance this with other approaches. Selection of projects would require careful determination about the capabilities of the end users to utilize the data and, in some cases, the capability of providing JGI with appropriate information about growth of the targeted organisms.
Question 5. Informatics challenges The topic of metadata availability and quality was highlighted throughout the meeting, although it was not one of the defined informatics challenges. The availability of high-quality metadata significantly enhances the value of any of the JGI products. Tools need to be available to allow the easy capture of metadata at the point where investigators have the highest interest in a given organism, typically pre-publication/pre-submission. 1. Should we continue supporting the annotation and integration of external (i. e. non-JGI) projects in IMG? -There was a general consensus that, under the current funding climate, JGI should focus on JGI projects. Kbase is well positioned to provide some of the services to the broader community that IMG has served in the past, including downloads There are broader informatics challenges that IMG has historically provided and the JGI is encouraged to continue providing the necessary informatics support as it is mission critical, not only for the JGI but for the community in general.