scholarly journals A New Approach to 3D Modeling of Inhomogeneous Populations of Viral Regulatory RNA

Viruses ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1108
Author(s):  
Patrick S. Osmer ◽  
Gatikrushna Singh ◽  
Kathleen Boris-Lawrie
Keyword(s):  
Tertiary Structure ◽  
Ensemble Methods ◽  
3D Models ◽  
Primer Binding Site ◽  
Regulatory Rna ◽  
Input Constraints ◽  
Rna Motifs ◽  
Genetic Studies ◽  
New Approach ◽  
Physical Context

Tertiary structure (3D) is the physical context of RNA regulatory activity. Retroviruses are RNA viruses that replicate through the proviral DNA intermediate transcribed by hosts. Proviral transcripts form inhomogeneous populations due to variable structural ensembles of overlapping regulatory RNA motifs in the 5′-untranslated region (UTR), which drive RNAs to be spliced or translated, and/or dimerized and packaged into virions. Genetic studies and structural techniques have provided fundamental input constraints to begin predicting HIV 3D conformations in silico. Using SimRNA and sets of experimentally-determined input constraints of HIVNL4-3 trans-activation responsive sequence (TAR) and pairings of unique-5′ (U5) with dimerization (DIS) or AUG motifs, we calculated a series of 3D models that differ in proximity of 5′-Cap and the junction of TAR and PolyA helices; configuration of primer binding site (PBS)-segment; and two host cofactors binding sites. Input constraints on U5-AUG pairings were most compatible with intramolecular folding of 5′-UTR motifs in energetic minima. Introducing theoretical constraints predicted metastable PolyA region drives orientation of 5′-Cap with TAR, U5 and PBS-segment helices. SimRNA and the workflow developed herein provides viable options to predict 3D conformations of inhomogeneous populations of large RNAs that have been intractable to conventional ensemble methods.

scholarly journals Photo images, 3D models and CT scanned data of loaches (Botiidae, Cobitidae and Nemacheilidae) of Japan

2018 ◽  
Vol 6 ◽  
pp. e26265 ◽  
Author(s):  
Yuichi Kano ◽  
Jun Nakajima ◽  
Takeshi Yamasaki ◽  
Jyun-ichi Kitamura ◽  
Ryoichi Tabata
Keyword(s):  
Public Interest ◽  
3D Models ◽  
Open Science ◽  
Type Specimens ◽  
Genetic Studies ◽  
Digital Archiving ◽  
Fish Biology ◽  
Freshwater Habitats ◽  
Further Development

Loach is one of the major cypriniform fishes in freshwater habitats of Japan; 35 taxa/clades have, until now, been recognised. Parallel to genetic studies, morphological examinations are needed for further development of loach study, eventually ichthyology and fish biology. Digital archiving, concerning taxonomy, ecology, ethology etc., is one of the progressive challenges for the open science of biology. This paper aimed to online publish photo images, 3D models and CT scanned data of all the known clades of loaches inhabiting Japan (103 individuals in total with several type specimens), contributing to ichthyology and public interest of biodiversity/biology.Photo images, 3D models and CT scanned data of all the known 35 taxa/clades of loaches inhabiting in Japan were online published at http://ffish.asia/loachesOfJapan and http://ffish.asia/loachesOfJapan3D.

scholarly journals Brain Cancer Prediction Based on Novel Interpretable Ensemble Gene Selection Algorithm and Classifier

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1936
Author(s):  
Abdulqader M. Almars ◽  
Majed Alwateer ◽  
Mohammed Qaraad ◽  
Souad Amjad ◽  
Hanaa Fathi ◽  
...  
Keyword(s):  
Gene Selection ◽  
Ensemble Methods ◽  
Cancer Classification ◽  
Biological Information ◽  
Gradient Boosting ◽  
New Approach ◽  
Biological Studies ◽  
Abnormal Cells ◽  
Gene Selection Algorithm ◽  
Gene Expression Levels

The growth of abnormal cells in the brain causes human brain tumors. Identifying the type of tumor is crucial for the prognosis and treatment of the patient. Data from cancer microarrays typically include fewer samples with many gene expression levels as features, reflecting the curse of dimensionality and making classifying data from microarrays challenging. In most of the examined studies, cancer classification (Malignant and benign) accuracy was examined without disclosing biological information related to the classification process. A new approach was proposed to bridge the gap between cancer classification and the interpretation of the biological studies of the genes implicated in cancer. This study aims to develop a new hybrid model for cancer classification (by using feature selection mRMRe as a key step to improve the performance of classification methods and a distributed hyperparameter optimization for gradient boosting ensemble methods). To evaluate the proposed method, NB, RF, and SVM classifiers have been chosen. In terms of the AUC, sensitivity, and specificity, the optimized CatBoost classifier performed better than the optimized XGBoost in cross-validation 5, 6, 8, and 10. With an accuracy of 0.91±0.12, the optimized CatBoost classifier is more accurate than the CatBoost classifier without optimization, which is 0.81± 0.24. By using hybrid algorithms, SVM, RF, and NB automatically become more accurate. Furthermore, in terms of accuracy, SVM and RF (0.97±0.08) achieve equivalent and higher classification accuracy than NB (0.91±0.12). The findings of relevant biomedical studies confirm the findings of the selected genes.

Geologic Modelling Using Augmented Reality

2021 ◽  
Author(s):  
Phathompat Boonyasaknanon ◽  
Raymond Pols ◽  
Katja Schulze ◽  
Robert Rundle
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
3D Models ◽  
New Approach ◽  
Domain Experts ◽  
3D Objects ◽  
Track Record ◽  
Initial Work ◽  
Geologic Model ◽  
Time Critical

Abstract An augmented reality (AR) system is presented which enhances the real-time collaboration of domain experts involved in the geologic modeling of complex reservoirs. An evaluation of traditional techniques is compared with this new approach. The objective of geologic modeling is to describe the subsurface as accurately and in as much detail as possible given the available data. This is necessarily an iterative process since as new wells are drilled more data becomes available which either validates current assumptions or forces a re-evaluation of the model. As the speed of reservoir development increases there is a need for expeditious updates of the subsurface model as working with an outdated model can lead to costly mistakes. Common practice is for a geologist to maintain the geologic model while working closely with other domain experts who are frequently not co-located with the geologist. Time-critical analysis can be hampered by the fact that reservoirs, which are inherently 3D objects, are traditionally viewed with 2D screens. The system presented here allows the geologic model to be rendered as a hologram in multiple locations to allow domain experts to collaborate and analyze the reservoir in real-time. Collaboration on 3D models has not changed significantly in a generation. For co-located personnel the approach is to gather around a 2D screen. For remote personnel the approach has been sharing a model through a 2D screen along with video chat. These approaches are not optimal for many reasons. Over the years various attempts have been tried to enhance the collaboration experience and have all fallen short. In particular virtual reality (VR) has been seen as a solution to this problem. However, we have found that augmented reality (AR) is a much better solution for many subtle reasons which are explored in the paper. AR has already acquired an impressive track record in various industries. AR will have applications in nearly all industries. For various historical reasons, the uptake for AR is much faster in some industries than others. It is too early to tell whether the use of augmented reality in geological applications will be transformative, however the results of this initial work are promising.

Bioinformatics Methods for Studying MicroRNA and ARE-Mediated Regulation of Post-Transcriptional Gene Expression

2010 ◽  
Vol 1 (3) ◽  
pp. 97-112 ◽  
Author(s):  
Richipal Singh Bindra ◽  
Jason T. L. Wang ◽  
Paramjeet Singh Bagga
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Untranslated Regions ◽  
Regulatory Rna ◽  
Rna Motifs ◽  
Protein Coding ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Target Sites ◽  
Transcriptional Gene Regulation

MicroRNAs (miRNAs) are short single-stranded RNA molecules with 21-22 nucleotides known to regulate post-transcriptional expression of protein-coding genes involved in most of the cellular processes. Prediction of miRNA targets is a challenging bioinformatics problem. AU-rich elements (AREs) are regulatory RNA motifs found in the 3’ untranslated regions (UTRs) of mRNAs, and they play dominant roles in the regulated decay of short-lived human mRNAs via specific interactions with proteins. In this paper, the authors review several miRNA target prediction tools and data sources, as well as computational methods used for the prediction of AREs. The authors discuss the connection between miRNA and ARE-mediated post-transcriptional gene regulation. Finally, a data mining method for identifying the co-occurrences of miRNA target sites in ARE containing genes is presented.

A 3D Model Watermarking Algorithm

2013 ◽  
Vol 798-799 ◽  
pp. 708-711
Author(s):  
Xiu Hu Tan
Keyword(s):  
Detection Probability ◽  
3D Model ◽  
False Alarm Probability ◽  
3D Models ◽  
Model Potential ◽  
Least Mean Square ◽  
New Approach ◽  
Geometry Model ◽  
3D Geometry ◽  
High Detection

The popularity of 3D content is on the rise since it provides an immersive experience to viewers. In this paper, we present a new approach to watermarking 3D models based on optimization statistics. Through choosing the vertexes, we are able to obtain to the embedded watermark that has the least modified to topology transform of the 3D geometry model, and then project the watermark to the space that has the least mean square error value. So, we obtain that the robustness of the approach lies in hiding a watermark in the space that is least susceptible to the 3D model potential modification. Through analysis and constraint the conditions, we can obtain a high detection probability, a low false alarm probability. The robustness of our method is demonstrated by various attacks through computer simulation.

scholarly journals RNA 3D structure prediction guided by independent folding of homologous sequences

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marcin Magnus ◽  
Kalli Kappel ◽  
Rhiju Das ◽  
Janusz M. Bujnicki
Keyword(s):  
Rna Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
3D Structure ◽  
3D Models ◽  
Target Sequence ◽  
Rna Sequences ◽  
Homologous Sequences ◽  
3D Structure Prediction ◽  
Folding Simulations

Abstract Background The understanding of the importance of RNA has dramatically changed over recent years. As in the case of proteins, the function of an RNA molecule is encoded in its tertiary structure, which in turn is determined by the molecule’s sequence. The prediction of tertiary structures of complex RNAs is still a challenging task. Results Using the observation that RNA sequences from the same RNA family fold into conserved structure, we test herein whether parallel modeling of RNA homologs can improve ab initio RNA structure prediction. EvoClustRNA is a multi-step modeling process, in which homologous sequences for the target sequence are selected using the Rfam database. Subsequently, independent folding simulations using Rosetta FARFAR and SimRNA are carried out. The model of the target sequence is selected based on the most common structural arrangement of the common helical fragments. As a test, on two blind RNA-Puzzles challenges, EvoClustRNA predictions ranked as the first of all submissions for the L-glutamine riboswitch and as the second for the ZMP riboswitch. Moreover, through a benchmark of known structures, we discovered several cases in which particular homologs were unusually amenable to structure recovery in folding simulations compared to the single original target sequence. Conclusion This work, for the first time to our knowledge, demonstrates the importance of the selection of the target sequence from an alignment of an RNA family for the success of RNA 3D structure prediction. These observations prompt investigations into a new direction of research for checking 3D structure “foldability” or “predictability” of related RNA sequences to obtain accurate predictions. To support new research in this area, we provide all relevant scripts in a documented and ready-to-use form. By exploring new ideas and identifying limitations of the current RNA 3D structure prediction methods, this work is bringing us closer to the near-native computational RNA 3D models.

3D Real-Time Modeling Issue in Gravity & Magnetic Anomalies Interpretation Tool

2011 ◽  
Vol 271-273 ◽  
pp. 495-500
Author(s):  
Jiang Ning Yin ◽  
Dun Hui Xiao
Keyword(s):  
3D Models ◽  
Magnetic Anomalies ◽  
Three Dimension ◽  
Forward Algorithm ◽  
New Approach ◽  
Time Modeling ◽  
Gravity And Magnetic ◽  
Geological Models ◽  
Interpretation System ◽  
3D Gravity

We present a new approach for constructing the initial 3D geological models in the process of man-machine interactive interpretation for gravity and magnetic anomalies. Firstly, we introduced the steps of method. It includes some auxiliary vertexes and sections techniques. Then, the forward algorithm of the model is given. And the data structure of the model is devised, later the modifying method and visualization method is discussed. This method is realized in our 3D gravity & magnetic anomaly interpretation system based on VC++6.0 and OpenGL. Using this method, the geophysical interpreter can construct or modify the geological models easily under the three dimension environment. The tool can give them visual 3D models, so it enhances the efficiency of the interpretation.

scholarly journals MOHCA-seq: RNA 3D models from single multiplexed proximity-mapping experiments

2014 ◽  
Author(s):  
Clarence Cheng ◽  
Fang-Chieh Chou ◽  
Wipapat Kladwang ◽  
Siqi Tian ◽  
Pablo Cordero ◽  
...  
Keyword(s):  
Conformational Changes ◽  
Deep Sequencing ◽  
Structure Determination ◽  
Rna Structure ◽  
Tertiary Structure ◽  
3D Models ◽  
Biological Processes ◽  
3D Information ◽  
Nucleotide Resolution

Large RNAs control myriad biological processes but challenge tertiary structure determination. We report that integrating Multiplexed •OH Cleavage Analysis with tabletop deep sequencing (MOHCA-seq) gives nucleotide-resolution proximity maps of RNA structure from single straightforward experiments. After achieving 1-nm resolution models for RNAs of known structure, MOHCA-seq reveals previously unattainable 3D information for ligand-induced conformational changes in a double glycine riboswitch and the sixth community-wide RNA puzzle, an adenosylcobalamin riboswitch.

Gene-culture interaction on human behavior and the brain

2017 ◽  
Author(s):  
Shihui Han
Keyword(s):  
Human Behavior ◽  
Functional Organization ◽  
Brain Activity ◽  
Receptor Gene ◽  
Individual Development ◽  
Genetic Studies ◽  
Oxytocin Receptor Gene ◽  
New Approach ◽  
Self Reflection ◽  
The Brain

Chapter 7 reviews empirical findings that allow consideration of biological and environmental influences on human behavior from an evolutionary perspective (e.g., gene-culture coevolution) and from a perspective of individual development (e.g., gene-culture interaction). It also reviews imaging genetic studies that link genes with brain functional organization. It introduces a cultural neuroscience paradigm for investigating genetic influences on the coupling of brain activity and culture by presenting two studies that examined how serotonin transporter functional polymorphism and oxytocin receptor gene moderate the association between interdependence and brain activities involved in self-reflection and empathy. These studies illustrate a new approach to understanding the manner with which culture interacts with gene to shape human brain activity.

scholarly journals 3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf

2020 ◽  
Vol 12 (15) ◽  
pp. 2466
Author(s):  
Elena Prado ◽  
Augusto Rodríguez-Basalo ◽  
Adolfo Cobo ◽  
Pilar Ríos ◽  
Francisco Sánchez
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
High Reliability ◽  
3D Models ◽  
Additive Models ◽  
Fine Scale ◽  
New Approach ◽  
Deep Learning Algorithm ◽  
The Relationship ◽  
Very High

The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.

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