Three-tier quick-response code: Applications for encoded text and counterfeit prevention system.

This paper introduces a novel approach for encoding information in PDF documents or similar files. The proposed encoding involves a dual-step method: firstly, the information is encoded in base64, and subsequently, it is uploaded in a user-selected color, while the rest of the colors contain dummy information. Merging of the encoded segments results in a single QR code. The Literature Review subsection investigates the usage of similar methods for information encoding, followed by a comparison of the luminance of the generated QR code with theoretical expectations. Finally, diverse use cases are presented. The proposed methodology is presented:•Compare the results obtained from the theorical approximation with those acquired in the merged QR code.•Use cases: encoding text sample to obtain a counterfeit system.•Results, contributions, and future work.

A serverless computing architecture for Martian aurora detection with the Emirates Mars Mission.

Remote sensing technologies are experiencing a surge in adoption for monitoring Earth's environment, demanding more efficient and scalable methods for image analysis. This paper presents a new approach for the Emirates Mars Mission (Hope probe); A serverless computing architecture designed to analyze images of Martian auroras, a key aspect in understanding the Martian atmosphere. Harnessing the power of OpenCV and machine learning algorithms, our architecture offers image classification, object detection, and segmentation in a swift and cost-effective manner. Leveraging the scalability and elasticity of cloud computing, this innovative system is capable of managing high volumes of image data, adapting to fluctuating workloads. This technology, applied to the study of Martian auroras within the HOPE Mission, not only solves a complex problem but also paves the way for future applications in the broad field of remote sensing.

NGScloud2: optimized bioinformatic analysis using Amazon Web Services.

BACKGROUND: NGScloud was a bioinformatic system developed to perform de novo RNAseq analysis of non-model species by exploiting the cloud computing capabilities of Amazon Web Services. The rapid changes undergone in the way this cloud computing service operates, along with the continuous release of novel bioinformatic applications to analyze next generation sequencing data, have made the software obsolete. NGScloud2 is an enhanced and expanded version of NGScloud that permits the access to ad hoc cloud computing infrastructure, scaled according to the complexity of each experiment. METHODS: NGScloud2 presents major technical improvements, such as the possibility of running spot instances and the most updated AWS instances types, that can lead to significant cost savings. As compared to its initial implementation, this improved version updates and includes common applications for de novo RNAseq analysis, and incorporates tools to operate workflows of bioinformatic analysis of reference-based RNAseq, RADseq and functional annotation. NGScloud2 optimizes the access to Amazon's large computing infrastructures to easily run popular bioinformatic software applications, otherwise inaccessible to non-specialized users lacking suitable hardware infrastructures. RESULTS: The correct performance of the pipelines for de novo RNAseq, reference-based RNAseq, RADseq and functional annotation was tested with real experimental data, providing workflow performance estimates and tips to make optimal use of NGScloud2. Further, we provide a qualitative comparison of NGScloud2 vs. the Galaxy framework. NGScloud2 code, instructions for software installation and use are available at https://github.com/GGFHF/NGScloud2. NGScloud2 includes a companion package, NGShelper that contains Python utilities to post-process the output of the pipelines for downstream analysis at https://github.com/GGFHF/NGShelper.

TOA: A software package for automated functional annotation in non-model plant species.

The increase of sequencing capacity provided by high-throughput platforms has made it possible to routinely obtain large sets of genomic and transcriptomic sequences from model and non-model organisms. Subsequent genomic analysis and gene discovery in next-generation sequencing experiments are, however, bottlenecked by functional annotation. One common way to perform functional annotation of sets of sequences obtained from next-generation sequencing experiments, is by searching for homologous sequences and accessing the related functional information deposited in genomic databases. Functional annotation is especially challenging for non-model organisms, like many plant species. In such cases, existing free and commercial general-purpose applications may not offer complete and accurate results. We present TOA (Taxonomy-oriented annotation), a Python-based user-friendly open source application designed to establish functional annotation pipelines geared towards non-model plant species that can run in Linux/Mac computers, HPCs and cloud servers. TOA performs homology searches against proteins stored in the PLAZA databases, NCBI RefSeq Plant, Nucleotide Database and Non-Redundant Protein Sequence Database, and outputs functional information from several ontology systems: Gene Ontology, InterPro, EC, KEGG, Mapman and MetaCyc. The software performance was validated by comparing the runtimes, total number of annotated sequences and accuracy of the functional information obtained for several plant benchmark data sets with TOA and other functional annotation solutions. TOA outperformed the other software in terms of number of annotated sequences and accuracy of the annotation and constitutes a good alternative to improve functional annotation in plants. TOA is especially recommended for gymnosperms or for low quality sequence data sets of non-model plants.

NGScloud: RNA-seq analysis of non-model species using cloud computing.

Summary: RNA-seq analysis usually requires large computing infrastructures. NGScloud is a bioinformatic system developed to analyze RNA-seq data using the cloud computing services of Amazon that permit the access to ad hoc computing infrastructure scaled according to the complexity of the experiment, so its costs and times can be optimized. The application provides a user-friendly front-end to operate Amazon's hardware resources, and to control a workflow of RNA-seq analysis oriented to non-model species, incorporating the cluster concept, which allows parallel runs of common RNA-seq analysis programs in several virtual machines for faster analysis. Availability and implementation: NGScloud is freely available at https://github.com/GGFHF/NGScloud/. A manual detailing installation and how-to-use instructions is available with the distribution.