A review of Cloud computing technologies for comprehensive microRNA analyses

An Effective Plant Small Secretory Peptide Recognition Model Based on Feature Correction Strategy

Plant small secretory peptides (SSPs) play an important role in the regulation of biological processes in plants. Accurately predicting SSPs enables efficient exploration of their functions. Traditional experimental verification methods are very reliable and accurate, but they require expensive equipment and a lot of time. The method of machine learning speeds up the prediction process of SSPs, but the instability of feature extraction will also lead to further limitations of this type of method. Therefore, this paper proposes a new feature-correction-based model for SSP recognition in plants, abbreviated as SE-SSP. The model mainly includes the following three advantages: First, the use of transformer encoders can better reveal implicit features. Second, design a feature correction module suitable for sequences, named 2-D SENET, to adaptively adjust the features to obtain a more robust feature representation. Third, stack multiple linear modules to further dig out the deep information on the sample. At the same time, the training based on a contrastive learning strategy can alleviate the problem of sparse samples. We construct experiments on publicly available data sets, and the results verify that our model shows an excellent performance. The proposed model can be used as a convenient and effective SSP prediction tool in the future. Our data and code are publicly available at https://github.com/wrab12/SE-SSP/.

A Review of DNA Data Storage Technologies Based on Biomolecules

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In the information age, data storage technology has become the key to improving computer systems. Since traditional storage technologies cannot meet the demand for massive storage, new DNA storage technology based on biomolecules attracts much attention. DNA storage refers to the technology that uses artificially synthesized deoxynucleotide chains to store and read all information, such as documents, pictures, and audio. First, data are encoded into binary number strings. Then, the four types of base, A(Adenine), T(Thymine), C(Cytosine), and G(Guanine), are used to encode the corresponding binary numbers so that the data can be used to construct the target DNA molecules in the form of deoxynucleotide chains. Subsequently, the corresponding DNA molecules are artificially synthesized, enabling the data to be stored within them. Compared with traditional storage systems, DNA storage has major advantages, such as high storage density, long duration, as well as low hardware cost, high access parallelism, and strong scalability, which satisfies the demands for big data storage. This manuscript first reviews the origin and development of DNA storage technology, then the storage principles, contents, and methods are introduced. Finally, the development of DNA storage technology is analyzed. From the initial research to the cutting edge of this field and beyond, the advantages, disadvantages, and practical applications of DNA storage technology require continuous exploration.

Web and Cloud Computing to Analyze Microarray Data

Microarray technology is a high-throughput technique that can simultaneously measure hundreds of thousands of genes' expression levels. Web and cloud computing tools and databases for storage and analysis of microarray data are necessary for biologists to interpret massive data from experiments. This chapter presents the main databases and web and cloud computing tools for microarray data storage and analysis.

Security challenges and solutions using healthcare cloud computing

Cloud computing is among the most beneficial solutions to digital problems. Security is one of the focal issues in cloud computing technology, and this study aims at investigating security issues of cloud computing and their probable solutions. A systematic review was performed using Scopus, Pubmed, Science Direct, and Web of Science databases. Once the title and abstract were evaluated, the quality of studies was assessed in order to choose the most relevant according to exclusion and inclusion criteria. Then, the full texts of studies selected were read thoroughly to extract the necessary results. According to the review, data security, availability, and integrity, as well as information confidentiality and network security, were the major challenges in cloud security. Further, data encryption, authentication, and classification, besides application programming interfaces (API), were security solutions to cloud infrastructure. Data encryption could be applied to store and retrieve data from the cloud in order to provide secure communication. Besides, several central challenges, which make the cloud security engineering process problematic, have been considered in this study.

Smartphone-Enabled Personalized Diagnostics: Current Status and Future Prospects

Smartphones are becoming increasingly versatile thanks to the wide variety of sensor and actuator systems packed in them. Mobile devices today go well beyond their original purpose as communication devices, and this enables important new applications, ranging from augmented reality to the Internet of Things. Personalized diagnostics is one of the areas where mobile devices can have the greatest impact. Hitherto, the camera and communication abilities of these devices have been barely exploited for point of care (POC) purposes. This short review covers the recent evolution of mobile devices in the area of POC diagnostics and puts forward some ideas that may facilitate the development of more advanced applications and devices in the area of personalized diagnostics. With this purpose, the potential exploitation of wireless power and actuation of sensors and biosensors using near field communication (NFC), the use of the screen as a light source for actuation and spectroscopic analysis, using the haptic module to enhance mass transport in micro volumes, and the use of magnetic sensors are discussed.

The Role of non-coding RNAs in colorectal cancer, with a focus on its autophagy

Colorectal cancer (CRC) is one of malignant afflictions burdening people worldwide, mainly caused by shortages of effective medical intervention and poorly mechanistic understanding of the pathogenesis of CRC. Non-coding RNAs (ncRNAs) are a type of heterogeneous transcripts without the capability of coding protein, but have the potency of regulating protein-coding gene expression. Autophagy is an evolutionarily conserved catabolic process in which cytoplasmic contents are delivered to cellular lysosomes for degradation, resulting in the turnover of cellular components and producing energy for cell functions. A growing body of evidence reveals that ncRNAs, autophagy, and the crosstalks of ncRNAs and autophagy play intricate roles in the initiation, progression, metastasis, recurrence and therapeutic resistance of CRC, which confer ncRNAs and autophagy to serve as clinical biomarkers and therapeutic targets for CRC. In this review, we sought to delineate the complicated roles of ncRNAs, mainly including miRNAs, lncRNAs and circRNAs, in the pathogenesis of CRC, particularly focus on the regulatory role of ncRNAs in CRC-related autophagy, attempting to shed light on the complex pathological mechanisms, involving ncRNAs and autophagy, responsible for CRC tumorigenesis and development, so as to underpin the ncRNAs- and autophagy-based therapeutic strategies for CRC in clinical setting.

Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases

Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) with a closed-loop structure that are mainly produced by variable processing of precursor mRNAs (pre-mRNAs). They are widely present in all eukaryotes and are very stable. Currently, circRNA studies have become a hotspot in RNA research. It has been reported that circRNAs constitute a significant proportion of transcript expression, and some are significantly more abundantly expressed than other transcripts. CircRNAs have regulatory roles in gene expression and critical biological functions in the development of organisms, such as acting as microRNA sponges or as endogenous RNAs and biomarkers. As such, they may have useful functions in the diagnosis and treatment of diseases. CircRNAs have been found to play an important role in the development of several diseases, including atherosclerosis, neurological disorders, diabetes, and cancer. In this paper, we review the status of circRNA research, describe circRNA-related databases and the identification of circRNAs, discuss the role of circRNAs in human diseases such as colon cancer, atherosclerosis, and gastric cancer, and identify remaining research questions related to circRNAs.

HumiR: Web Services, Tools and Databases for Exploring Human microRNA Data

For many research aspects on small non-coding RNAs, especially microRNAs, computational tools and databases are developed. This includes quantification of miRNAs, piRNAs, tRNAs and tRNA fragments, circRNAs and others. Furthermore, the prediction of new miRNAs, isomiRs, arm switch events, target and target pathway prediction and miRNA pathway enrichment are common tasks. Additionally, databases and resources containing expression profiles, e.g., from different tissues, organs or cell types, are generated. This information in turn leads to improved miRNA repositories. While most of the respective tools are implemented in a species-independent manner, we focused on tools for human small non-coding RNAs. This includes four aspects: (1) miRNA analysis tools (2) databases on miRNAs and variations thereof (3) databases on expression profiles (4) miRNA helper tools facilitating frequent tasks such as naming conversion or reporter assay design. Although dependencies between the tools exist and several tools are jointly used in studies, the interoperability is limited. We present HumiR, a joint web presence for our tools. HumiR facilitates an entry in the world of miRNA research, supports the selection of the right tool for a research task and represents the very first step towards a fully integrated knowledge-base for human small non-coding RNA research. We demonstrate the utility of HumiR by performing a very comprehensive analysis of Alzheimer's miRNAs.