This document discusses the collection and analysis of small subunit ribosomal RNA structures, specifically 16S and 16S-like rRNA. It provides background on how comparative methods have been used to infer the secondary and tertiary structures of rRNA molecules. It then summarizes the evolution of 16S rRNA secondary structure models, from early minimal structures to current models incorporating tertiary interactions. The objectives of the annual structure collection are outlined, including presenting updated E. coli 16S rRNA structure, additional tertiary interactions, and a sampling of structures from diverse phylogenetic domains. Examples of higher-order structure diagrams are provided for E. coli, yeast, and C. elegans rRNA.
Gracias a todos los asistentes del taller de Psicología Positiva realizado el 27 de septiembre, 2012 en la Universidad Tecnológica de Ciudad Juárez. A petición de los asistentes les subimos la teoría tratada en el mismo.
Técnicas para el Desarrollo de habilidades personales y profesionales mediante la psicología positiva
con temas como motivación, talento,comunicación, trabajo en equipo, psicología
y coaching. Para charlas de motivación soy motivador en Costa Rica Eduardo Gómez A
http://www.enriquecetupsicologia.com
Workshop de Psicologia Positiva para Micro, Pequenas e Médias Empresas.
Porque todos merecem o melhor.
Psicologia Positiva, novos rumos, novas abordagens.
Lee J.C., Gutell R.R., and Russell R. (2006).
The UAA/GAN internal loop motif: a new RNA structural element that forms a cross-strand AAA stack and long-range tertiary interactions.
Journal of Molecular Biology, 360(5):978-988.
Gracias a todos los asistentes del taller de Psicología Positiva realizado el 27 de septiembre, 2012 en la Universidad Tecnológica de Ciudad Juárez. A petición de los asistentes les subimos la teoría tratada en el mismo.
Técnicas para el Desarrollo de habilidades personales y profesionales mediante la psicología positiva
con temas como motivación, talento,comunicación, trabajo en equipo, psicología
y coaching. Para charlas de motivación soy motivador en Costa Rica Eduardo Gómez A
http://www.enriquecetupsicologia.com
Workshop de Psicologia Positiva para Micro, Pequenas e Médias Empresas.
Porque todos merecem o melhor.
Psicologia Positiva, novos rumos, novas abordagens.
Lee J.C., Gutell R.R., and Russell R. (2006).
The UAA/GAN internal loop motif: a new RNA structural element that forms a cross-strand AAA stack and long-range tertiary interactions.
Journal of Molecular Biology, 360(5):978-988.
Lee C.-Y., Lee J.C., and Gutell R.R. (2007).
Networks of interactions in the secondary and tertiary structure of ribosomal RNA.
Physica A, 386(1):564-572.
Chandramouli P., Topf M., Ménétret J.-F., Eswar N., Cannone J.J., Gutell R.R., Sali A., and Akey C.W. (2008).
Structure of the Mammalian 80S Ribosome at 8.7 Å Resolution.
Structure, 16(4):535-548.
ARTICLE HISTORY Received 22 August 2016 Accepted 5 September 2016
ABSTRACT Henri Grosjean and Eric Westhof recently presented an information-rich, alternative view of the genetic code, which takes into account current knowledge of the decoding process, including the complex nature of interactions between mRNA, tRNA and rRNA that take place during protein synthesis on the ribosome, and it also better reflects the evolution of the code. The new asymmetrical circular genetic code has a number of advantages over the traditional codon table and the previous circular diagrams (with a symmetrical/clockwise arrangement of the U, C, A, G bases). Most importantly, all sequence co-variances can be visualized and explained based on the internal logic of the thermodynamics of codon-anticodon interactions.
Gutell 123.app environ micro_2013_79_1803Robin Gutell
McFrederick Q.S., Cannone J.J., Gutell R.R., Kellner K., Plowes R.M., and Mueller U.G. (2013).
Specificity between Lactobacilli and Hymenopteran Hosts is the Exception Rather than the Rule.
Applied and Environmental Microbiology, 79:1803-1812.
Gutell R.R. (2013).
Comparative Analysis of the Higher-Order Structure of RNA.
in: Biophysics of RNA Folding. Volume editor: Rick Russell. Series title: Biophysics for the Life Sciences. Series editors: Norma Allewell, Ivan Rayment, Bertrand Garcia-Moreno, Jonathan Dinman, and Michael McCarthy. pp. 11-22. Publisher: Springer, New York, NY.
Gardner D.P., Xu W., Miranker D.P., Ozer S., Cannone J.J., and Gutell R.R. (2012).
An Accurate Scalable Template-based Alignment Algorithm.
Proceedings of 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012), Philadelphia, PA. October 4-7, 2012. IEEE Computer Society, Washington, DC, USA. pp. 237-243.
Lee J.C. and Gutell R.R. (2012).
A Comparison of the Crystal Structures of the Eukaryotic and Bacterial SSU Ribosomal RNAs Reveals Common Structural Features in the Hypervariable Regions.
PLoS ONE, 7(5):e38203.
Gardner D.P., Ren P., Ozer S., and Gutell R.R. (2011).
Statistical Potentials for Hairpin and Internal Loops Improve the Accuracy of the Predicted RNA Structure.
Journal of Molecular Biology, 413(2):473-483.2011. pp 15-22.
Ozer S., Doshi K.J., Xu W., and Gutell R.R. (2011).
rCAD: A Novel Database Schema for the Comparative Analysis of RNA.
7th IEEE International Conference on e-Science, Stockholm, Sweden. December 5-8, 2011. pp 15-22.
Jiang Y., Xu W., Thompson L.P., Gutell R., and Miranker D. (2011).
R-PASS: A Fast Structure-based RNA Sequence Alignment Algorithm.
Proceedings of 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2011), Atlanta, GA. November 12-15, 2011. IEEE Computer Society, Washington, DC, USA. pp. 618-622.
Xu W., Wongsa A., Lee J., Shang L., Cannone J.J., and Gutell R.R. (2011).
RNA2DMap: A Visual Exploration Tool of the Information in RNA's Higher-Order Structure.
Proceedings of 2011 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2011), Atlanta, GA. November 12-15, 2011. IEEE Computer Society, Washington, DC, USA. pp. 613-617.
Muralidhara C., Gross A.M., Gutell R.R., and Alter O. (2011).
Tensor Decomposition Reveals Concurrent Evolutionary Convergences and Divergences and Correlations with Structural Motifs in Ribosomal RNA.
PLoS ONE, 6(4):e18768.
Xia Z., Gardner D.P., Gutell R.R., and Ren P. (2010).
Coarse-Grained Model for Simulation of RNA Three-Dimensional Structures.
The Journal of Physical Chemistry B, 114(42):13497-13506.
Milbury C.A., Lee J.C., Cannone J.J., Gaffney P.M., and Gutell R.R. (2010).
Fragmentation of the Large Subunit Ribosomal RNA Gene in Oyster Mitochondrial Genomes.
BMC Genomics, 11(1):485.
Mueller U.G., Ishak H., Lee J.C., Sen R., and Gutell R.R. (2010).
Placement of attine ant-associated Pseudonocardia in a global phylogeny (Pseudonocardiaceae, Actinomycetales): a test of two symbiont-association models.
Antonie van Leeuwenhoek International Journal of General and Molecular Microbiology, 98(2):195-212.
Theriot E.C., Cannone J.J., Gutell R.R., and Alverson A.J. (2009).
The limits of nuclear encoded SSU rDNA for resolving the diatom phylogeny.
European Journal of Phycology, 44(3):277-290.
Wu J.C., Gardner D.P., Ozer S., Gutell R.R. and Ren P. (2009).
Correlation of RNA Secondary Structure Statistics with Thermodynamic Stability and Applications to Folding.
Journal of Molecular Biology, 391(4):769-783.
Xu W., Ozer S., and Gutell R.R. (2009).
Covariant Evolutionary Event Analysis for Base Interaction Prediction Using a Relational Database Management System for RNA.
21st International Conference on Scientific and Statistical Database Management. June 2-4, 2009. Springer-Verlag. pp. 200-216.
Chen Y.P., Evans J.D., Murphy C., Gutell R., Zuker M., Gundersen-Rindal D., and Pettis J.S. (2009).
Morphological, Molecular, and Phylogenetic Characterization of Nosema cerenae, a Microsporidian Parasite Isolated from the European Honey Bee, Apis mellifera.
The Journal of Eukaryotic Microbiology, 56(2):142-147.
Maddison D.R., Moore W., Baker M.D., Ellis T.M., Ober K.A., Cannone J.J., and Gutell R.R. (2009).
Monophyly of terrestrial adephagan beetles as indicated by three nuclear genes (Coleoptera: Carabidae and Trachypachidae).
Zoologica Scripta, 38(1):43-62.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
1. Nucleic Acids Research, 1993, Vol. 21, No. 13 3051-3054
Collection of small subunit (16S- and 16S-like) ribosomal
RNA structures
Robin Ray Gutell
MCB Biology, Campus Box 347, University of Colorado, Boulder, CO 80309-0347, USA
INTRODUCTION
Inferring higher-order structure for complex RNA molecules,
such as the ribosomal RNAs has relied primarily on comparative
methods. Underlying these methods is the premise that molecules
with different primary structure and similar functional
characteristics have similar secondary and tertiary structure
[Reviewed in: 1]. For these methods to be effective, the RNA
molecules under study need to be sufficiently similar at the
primary structure level to obtain good sequence alignments,
however these same sequences also need to be proportionately
different for positional covariance to occur, the indicator
suggesting the existence of a basepair.
The higher-order structure models for 16S rRNA have evolved
in stages. Initially, with a small number of 16S rRNA sequences
in hand, a minimal secondary structure was proposed. Increases
in the number and diversity of available 16S rRNA sequences
and paralleled with improvements in correlation analysis
algorithms has lead to the continual refinement of this structure
model. In the earlier stages only secondary structure pairings were
identified. In contrast during the latter stages only minor
refinements in these pairings occurred while several novel tertary
and non-canonical pairing constraints were proposed [reviewed
in: 2-3]. And now with over 2,200 16S and 16S-like rRNA
available sequences spanning the three phylogenetic domains and
the two organelles (Mitochondria and Chloroplast), detailed
phylogenetic, structural, and structural evolution information is
now being deciphered in great detail, although the resulting
analysis from different groups is not always congruent.
Over the years several groups have developed 16S rRNA
secondary structure models. The current versions for each are
fairly analogous with one another[2, 4-7], although this has not
always been the case. And while the current differences are small,
some are significant for the Escherichia coli and other Bacterial,
Archaea, Eucarya, and mitochondrial structure models. These
versions should also not be considered final as these models are
expected to undergo minor revisions as the number and diversity
of 16S and 16S-like sequences increases and is paralleled with
continued improvements and alternative correlation analysis
interpretations [8, unpublished work].
Over the next few years this collection, and the accompanying
23S rRNA (see Gutell, Gray, Schnare, this issue) will grow in
size and detail. The complexity of structure and the evolutionary
dimension of these structures presents us with a wonderful
opportunity to investigate RNA structural motifs and map with
some precision, the evolution ofthese RNAs and underlying RNA
structural characteristics associated with different phylogenetic
assemblages. This collection of structures should also be ofvalue
to the experimentalist studying rRNA structure and function. And
equally valuable to those studying rRNA based phylogeny.
OBJECTIVES
The objectives for this annual collection are:
1. Present our most current comparative interpretation of the
Escherichia coli 16S rRNA secondary structure [with
general agreement between C.R.Woese, H.F.Noller, and
myselfl. While this secondary structure model is stable,
minor adjustments are expected in some of the helical
pairings.
2. Present other significant correlations that suggest tertiary
and other complex structure in the Escherichia coli model.
The numbers for such structural interactions should increase
over the next few years.
3. Present a sampling of different 16S and 16S-like rRNA
higher-order structure models. Starting with a broad
sampling of phylogenetically and structurally distinct
models, additional examples will be developed to fill in this
broad phylogenetic and structure matrix. A parallel effort
will refine all previously proposed models as new
comparative structure information is obtained. To be
discussed elsewhere and in more detail, this collection of
structures will serve as the database for detailed analysis
of RNA evolution and RNA structural motifs.
The first 16S and 16Slike structure release is set for
summer/fall- 1993, and will include major representatives
from the three phylogenetic domains and organelles. The
approximate numbers will be: 25 (eu)bacteria, 10 archaea,
15 eucarya, 2 chloroplast, and 10 mitochondria. Readers
are encouraged to contact the author with suggestions for
additional higherorder structure models not currently
available.
4. Illustrated in this article are three divergent 16S and 16S-
like rRNAs structure models for: Escherichia coli, a
member of the (eu)bacteria phylogenetic domain; nuclear
encoded Saccharomyces cerevisiae, a member of the
eucarya domain; and Caenorhabditis elegans mitochondria,
one of the smallest known 16S-like rRNAs.
This work is an adjunct to the monumental effort of the RDP
(Ribosomal Database Project, see this issue); Their mandate being
to generate and make various tpes of ribosomal RNA data and
interpretation generally available. On-line access to these 16S
rRNA secondary structure files is available from the RDP
k.l 1993 Oxford University Press
2. 3052 Nucleic Acids Research, 1993, Vol. 21, No. 13
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Figure 1. Higher-order structure diagram for Escherichia coil 16S rRNA [2-3]. For secondary structure basepairings, short lines connect canonical pairs (C:G,
U:A), G:U pairs are denoted with dots, A:G pairings with larger open circles, and other non-canonical pairings with closed circles. Terdary interactions are connected
with thicker and longer solid lines. Every 10th nucleotide position is marked with a tic mark, while every 50th posiion is numbered [Sequence Accession number is J01695].
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3. Nucleic Acids Research, 1993, Vol. 21, No. 13 3053
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as in Fig. 1. [Sequence is slighdy modified from acccession number J01353].
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FIgr 3. Higher-order structure diagram for Caenorhabditis elegans mitochondrial 16S-like rRNA. Higher-order structre interactions illustrated and noted as in
Fig. 1. [Sequence Accession number is X54252].
anonymous ftp site (info.mcs.anl.gov). Currently, these
PostScript files are maintained in the
'/pub/RDP/SSU rRNA/sec_struct' directory. Additional
information about this server and access to the Ribosomal
Database Project is described in their article in this issue. Readers
unable to access this on-line information, can obtain a set of 16S
rRNA hardcopy diagrams from the author.
The interactive graphics program XRNA (developed by Bryn
Weiser, UC Santa Cruz) facilitated the generation ofthe higher-
order structure models displayed here and the PostScript files
available at the RDP anonymous ftp site. Manual alignment of
these 16S and 16S-like rRNA sequences was facilitated with the
interactive sequence alignment editor AE2 (developed by Tom
Macke, Scripps Clinic).
ACKNOWLEDGMENTS
Refining and interpreting these rRNA structure models is an on-
going and long term collaboration with Drs Carl Woese and
Harry Noller. Bryn Weiser and Tom Macke are greatfully
acknowleged for developing the wonderful programs that make
much of this analysis and presentation possible. I also wish to
tank the W.M.Keck Foundation for their generous support of
RNA science on the Boulder campus, and SUN Microsystems
for their timely donation of computer equipment. The author is
an Associate in the Program in Evolutionary Biology of the
Canadian Institute of Advanced Research. This work was
supported by the NIH (GM 48207).
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