Identification, Prediction and Data Analysis of Noncoding RNAs: A Review

2019 ◽  
Vol 15 (3) ◽  
pp. 216-230 ◽  
Author(s):  
Abbasali Emamjomeh ◽  
Javad Zahiri ◽  
Mehrdad Asadian ◽  
Mehrdad Behmanesh ◽  
Barat A. Fakheri ◽  
...  
Keyword(s):  
Computational Methods ◽  
State Of The Art ◽  
Noncoding Rnas ◽  
Research Field ◽  
Design Strategies ◽  
Structural Prediction ◽  
Computational Costs ◽  
Cellular Processes ◽  
Laboratory Techniques ◽  
Fast Prediction

Background:Noncoding RNAs (ncRNAs) which play an important role in various cellular processes are important in medicine as well as in drug design strategies. Different studies have shown that ncRNAs are dis-regulated in cancer cells and play an important role in human tumorigenesis. Therefore, it is important to identify and predict such molecules by experimental and computational methods, respectively. However, to avoid expensive experimental methods, computational algorithms have been developed for accurately and fast prediction of ncRNAs.Objective:The aim of this review was to introduce the experimental and computational methods to identify and predict ncRNAs structure. Also, we explained the ncRNA’s roles in cellular processes and drugs design, briefly.Method:In this survey, we will introduce ncRNAs and their roles in biological and medicinal processes. Then, some important laboratory techniques will be studied to identify ncRNAs. Finally, the state-of-the-art models and algorithms will be introduced along with important tools and databases.Results:The results showed that the integration of experimental and computational approaches improves to identify ncRNAs. Moreover, the high accurate databases, algorithms and tools were compared to predict the ncRNAs.Conclusion:ncRNAs prediction is an exciting research field, but there are different difficulties. It requires accurate and reliable algorithms and tools. Also, it should be mentioned that computational costs of such algorithm including running time and usage memory are very important. Finally, some suggestions were presented to improve computational methods of ncRNAs gene and structural prediction.

Noncoding RNAs in Medicinal Plants and Their Regulatory Roles in Bioactive Compound Production

2020 ◽  
Vol 21 ◽  
Author(s):  
Caili Li ◽  
Meizhen Wang ◽  
Xiaoxiao Qiu ◽  
Hong Zhou ◽  
Shanfa Lu
Keyword(s):  
Medicinal Plants ◽  
Current Knowledge ◽  
Salvia Miltiorrhiza ◽  
Noncoding Rnas ◽  
Bioactive Compound ◽  
Research Field ◽  
Model Systems ◽  
Small Interfering Rnas ◽  
Regulatory Pathways ◽  
Regulatory Modules

Background: Noncoding RNAs (ncRNAs), such as microRNAs (miRNAs), small interfering RNAs (siRNAs) and long noncoding RNAs (lncRNAs), play significant regulatory roles in plant development and secondary metabolism and are involved in plant response to biotic and abiotic stresses. They have been intensively studied in model systems and crops for approximately two decades and massive amount of information have been obtained. However, for medicinal plants, ncRNAs, particularly their regulatory roles in bioactive compound biosynthesis, are just emerging as a hot research field. Objective: This review aims to summarize current knowledge on herbal ncRNAs and their regulatory roles in bioactive compound production. Results and Conclusion: So far, scientists have identified thousands of miRNA candidates from over 50 medicinal plant species and 11794 lncRNAs from Salvia miltiorrhiza, Panax ginseng, and Digitalis purpurea. Among them, more than 30 miRNAs and five lncRNAs have been predicted to regulate bioactive compound production. The regulation may achieve through various regulatory modules and pathways, such as the miR397-LAC module, the miR12112-PPO module, the miR156-SPL module, the miR828-MYB module, the miR858-MYB module, and other siRNA and lncRNA regulatory pathways. Further functional analysis of herbal ncRNAs will provide useful information for quality and quantity improvement of medicinal plants.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

scholarly journals The Role of lncRNA in the Development of Tumors, Including Breast Cancer

2021 ◽  
Vol 22 (16) ◽  
pp. 8427
Author(s):  
Beata Smolarz ◽  
Anna Zadrożna-Nowak ◽  
Hanna Romanowicz
Keyword(s):  
Breast Cancer ◽  
Breast Carcinoma ◽  
Present Article ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Regulation Of Transcription ◽  
Ribonucleic Acids ◽  
Detailed Review ◽  
Cellular Processes

Long noncoding RNAs (lncRNAs) are the largest groups of ribonucleic acids, but, despite the increasing amount of literature data, the least understood. Given the involvement of lncRNA in basic cellular processes, especially in the regulation of transcription, the role of these noncoding molecules seems to be of great importance for the proper functioning of the organism. Studies have shown a relationship between disturbed lncRNA expression and the pathogenesis of many diseases, including cancer. The present article presents a detailed review of the latest reports and data regarding the importance of lncRNA in the development of cancers, including breast carcinoma.

scholarly journals Immunomodulatory Nanomedicine for the Treatment of Atherosclerosis

2021 ◽  
Vol 10 (14) ◽  
pp. 3185
Author(s):  
Linsey J. F. Peters ◽  
Alexander Jans ◽  
Matthias Bartneck ◽  
Emiel P. C. van der Vorst
Keyword(s):  
Cell Proliferation ◽  
Drug Carriers ◽  
Research Field ◽  
General Introduction ◽  
Cellular Receptors ◽  
Cellular Processes ◽  
Potential Applications ◽  
Current Therapies ◽  
Based Medicine ◽  
Physiological Systems

Atherosclerosis is the main underlying cause of cardiovascular diseases (CVDs), which remain the number one contributor to mortality worldwide. Although current therapies can slow down disease progression, no treatment is available that can fully cure or reverse atherosclerosis. Nanomedicine, which is the application of nanotechnology in medicine, is an emerging field in the treatment of many pathologies, including CVDs. It enables the production of drugs that interact with cellular receptors, and allows for controlling cellular processes after entering these cells. Nanomedicine aims to repair, control and monitor biological and physiological systems via nanoparticles (NPs), which have been shown to be efficient drug carriers. In this review we will, after a general introduction, highlight the advantages and limitations of the use of such nano-based medicine, the potential applications and targeting strategies via NPs. For example, we will provide a detailed discussion on NPs that can target relevant cellular receptors, such as integrins, or cellular processes related to atherogenesis, such as vascular smooth muscle cell proliferation. Furthermore, we will underline the (ongoing) clinical trials focusing on NPs in CVDs, which might bring new insights into this research field.

scholarly journals Porous block copolymer separation membranes for 21st century sanitation and hygiene

2021 ◽  
Author(s):  
Leiming Guo ◽  
Yong Wang ◽  
Martin Steinhart
Keyword(s):  
Block Copolymer ◽  
21St Century ◽  
Water Purification ◽  
State Of The Art ◽  
Design Strategies ◽  
Separation Membranes ◽  
Sanitation And Hygiene ◽  
Device Integration ◽  
Porous Block

This review summarizes design strategies for nanoporous state-of-the-art BCP separation membranes, their preparation, their device integration and their use for water purification.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

scholarly journals Proteomic Adaptation of Clostridioides difficile to Treatment with the Antimicrobial Peptide Nisin

Cells ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 372
Author(s):  
Sandra Maaß ◽  
Jürgen Bartel ◽  
Pierre-Alexander Mücke ◽  
Rabea Schlüter ◽  
Thomas Sura ◽  
...  
Keyword(s):  
State Of The Art ◽  
Biomedical Application ◽  
Time Resolved ◽  
Antibiotic Resistant ◽  
Cellular Processes ◽  
Clostridioides Difficile ◽  
Antibiotic Associated Diarrhea ◽  
Life Threatening ◽  
Sublethal Concentrations ◽  
Interesting Alternative

Clostridioides difficile is the leading cause of antibiotic-associated diarrhea but can also result in more serious, life-threatening conditions. The incidence of C. difficile infections in hospitals is increasing, both in frequency and severity, and antibiotic-resistant C. difficile strains are advancing. Against this background antimicrobial peptides (AMPs) are an interesting alternative to classic antibiotics. Information on the effects of AMPs on C. difficile will not only enhance the knowledge for possible biomedical application but may also provide insights into mechanisms of C. difficile to adapt or counteract AMPs. This study applies state-of-the-art mass spectrometry methods to quantitatively investigate the proteomic response of C. difficile 630∆erm to sublethal concentrations of the AMP nisin allowing to follow the cellular stress adaptation in a time-resolved manner. The results do not only point at a heavy reorganization of the cellular envelope but also resulted in pronounced changes in central cellular processes such as carbohydrate metabolism. Further, the number of flagella per cell was increased during the adaptation process. The potential involvement of flagella in nisin adaptation was supported by a more resistant phenotype exhibited by a non-motile but hyper-flagellated mutant.

scholarly journals The Chinese Medicinal Formulation Guzhi Zengsheng Zhitongwan Modulates Chondrocyte Structure, Dynamics, and Metabolism by Controlling Multiple Functional Proteins

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Baojin Yao ◽  
Bocheng Lu ◽  
Mei Zhang ◽  
Hongwei Gao ◽  
Xiangyang Leng ◽  
...  
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Molecular Mechanisms ◽  
Current Knowledge ◽  
Research Field ◽  
Medical Systems ◽  
Cellular Processes ◽  
Structure Dynamics ◽  
National Medical ◽  
Medicinal Formulation

Traditional Chinese medicine is one of the oldest medical systems in the world and has its unique principles and theories in the prevention and treatment of human diseases, which are achieved through the interactions of different types of materia medica in the form of Chinese medicinal formulations. GZZSZTW, a classical and effective Chinese medicinal formulation, was designed and created by professor Bailing Liu who is the only national medical master professor in the clinical research field of traditional Chinese medicine and skeletal diseases. GZZSZTW has been widely used in clinical settings for several decades for the treatment of joint diseases. However, the underlying molecular mechanisms are still largely unknown. In the present study, we performed quantitative proteomic analysis to investigate the effects of GZZSZTW on mouse primary chondrocytes using state-of-the-art iTRAQ technology. We demonstrated that the Chinese medicinal formulation GZZSZTW modulates chondrocyte structure, dynamics, and metabolism by controlling multiple functional proteins that are involved in the cellular processes of DNA replication and transcription, protein synthesis and degradation, cytoskeleton dynamics, and signal transduction. Thus, this study has expanded the current knowledge of the molecular mechanism of GZZSZTW treatment on chondrocytes. It has also shed new light on possible strategies to further prevent and treat cartilage-related diseases using traditional Chinese medicinal formulations.

Two Orders of Magnitude Improvement in the Detection Limit of Droplet-Based Micro-Magnetofluidics with Planar Hall Effect Sensors

2021 ◽  
Vol 6 (1) ◽  
pp. 47
Author(s):  
Julian Schütt ◽  
Rico Illing ◽  
Oleksii Volkov ◽  
Tobias Kosub ◽  
Pablo Nicolás Granell ◽  
...  
Keyword(s):  
Detection Limit ◽  
Hall Effect ◽  
State Of The Art ◽  
Limit Of Detection ◽  
Research Field ◽  
High Throughput Analysis ◽  
Planar Hall Effect ◽  
Biologically Relevant ◽  
Sensing Platform ◽  
Order Of Magnitude

The detection, manipulation, and tracking of magnetic nanoparticles is of major importance in the fields of biology, biotechnology, and biomedical applications as labels as well as in drug delivery, (bio-)detection, and tissue engineering. In this regard, the trend goes towards improvements of existing state-of-the-art methodologies in the spirit of timesaving, high-throughput analysis at ultra-low volumes. Here, microfluidics offers vast advantages to address these requirements, as it deals with the control and manipulation of liquids in confined microchannels. This conjunction of microfluidics and magnetism, namely micro-magnetofluidics, is a dynamic research field, which requires novel sensor solutions to boost the detection limit of tiny quantities of magnetized objects. We present a sensing strategy relying on planar Hall effect (PHE) sensors in droplet-based micro-magnetofluidics for the detection of a multiphase liquid flow, i.e., superparamagnetic aqueous droplets in an oil carrier phase. The high resolution of the sensor allows the detection of nanoliter-sized superparamagnetic droplets with a concentration of 0.58 mg cm−3, even when they are only biased in a geomagnetic field. The limit of detection can be boosted another order of magnitude, reaching 0.04 mg cm−³ (1.4 million particles in a single 100 nL droplet) when a magnetic field of 5 mT is applied to bias the droplets. With this performance, our sensing platform outperforms the state-of-the-art solutions in droplet-based micro-magnetofluidics by a factor of 100. This allows us to detect ferrofluid droplets in clinically and biologically relevant concentrations, and even in lower concentrations, without the need of externally applied magnetic fields.

scholarly journals Gaussian Elimination versus Greedy Methods for the Synthesis of Linear Reversible Circuits

2021 ◽  
Vol 2 (3) ◽  
pp. 1-26
Author(s):  
Timothée Goubault De Brugière ◽  
Marc Baboulin ◽  
Benoît Valiron ◽  
Simon Martiel ◽  
Cyril Allouche
Keyword(s):  
Quantum Computing ◽  
State Of The Art ◽  
Gaussian Elimination ◽  
Random Operators ◽  
Lu Factorization ◽  
Large Problem ◽  
Computational Costs ◽  
Elimination Algorithm ◽  
Reversible Circuits ◽  
Greedy Methods

Linear reversible circuits represent a subclass of reversible circuits with many applications in quantum computing. These circuits can be efficiently simulated by classical computers and their size is polynomially bounded by the number of qubits, making them a good candidate to deploy efficient methods to reduce computational costs. We propose a new algorithm for synthesizing any linear reversible operator by using an optimized version of the Gaussian elimination algorithm coupled with a tuned LU factorization. We also improve the scalability of purely greedy methods. Overall, on random operators, our algorithms improve the state-of-the-art methods for specific ranges of problem sizes: The custom Gaussian elimination algorithm provides the best results for large problem sizes (n > 150), while the purely greedy methods provide quasi optimal results when n < 30. On a benchmark of reversible functions, we manage to significantly reduce the CNOT count and the depth of the circuit while keeping other metrics of importance (T-count, T-depth) as low as possible.

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